Emotion detection from text using data mining and text mining
24 likes16,916 views
The document outlines the development of an emotion detection system named 'Emotelligence', which uses data mining and text mining to identify human emotions from textual content. It focuses on eight basic emotions—joy, trust, fear, surprise, sadness, disgust, anger, and anticipation—and describes a three-step approach for emotion detection: identifying emotions, collecting data, and processing it through classifiers. The tests conducted on a dataset of 1800 training examples and 200 testing examples resulted in an overall accuracy of 71%, with varied accuracy across different emotions.
1 of 20
Downloaded 661 times
Ad
Recommended
Emotion mining in text
Emotion mining in textLovepreet Singh The document discusses emotion mining in text. It defines text mining and emotions and discusses elements of emotions like thoughts, body responses, and behaviors. It explains that emotion mining seeks the emotional state of a writer from text. Major theories of emotion are physiological, neurological, and cognitive. Positive emotions make one feel good while negative emotions stop rational thinking. Techniques for emotion detection discussed are keyword spotting, lexical affinity, learning-based, and hybrid methods. Limitations include ambiguity in keywords, inability to recognize text without keywords, and lack of linguistic information. An example of analyzing social network comments is provided.
detect emotion from text
detect emotion from textSafayet Hossain This presentation discusses designing an English language compiler to detect emotion from text. It begins with an introduction to emotion and common emotion models. It then outlines the objectives and architecture of the emotion detection system. Key aspects covered include language processing techniques like keyword analysis and parsing, semantic analysis, and the word-processing and sentence analysis modules. Challenges in developing such a system are also discussed. Finally, potential future work and references are presented.
Emotion Detection from Text
Emotion Detection from TextIJERD Editor This document discusses emotion detection from text. It presents an emotion detection model that extracts emotion from text at the sentence level without relying on existing affect lexicons. The model detects emotion by searching for direct emotional keywords and emotion-affect words/phrases. Experiments show the method achieves over 77% accuracy in detecting Ekman's six basic emotions from text. The document also reviews related work on emotion detection approaches, including keyword-based, rule-based, and machine learning methods. It discusses challenges like the lack of large annotated training data and limitations of dictionary-based approaches.
Emotion Detection in text
Emotion Detection in text kashif kashif This document discusses text-based emotion recognition. It describes how emotions are expressed through text messages and the challenges in developing algorithms to accurately recognize emotions from text for machines. Various approaches are discussed, including sentiment analysis, using emotion keywords, and machine learning classifiers like KNN and Hidden Markov Models. Challenges around ambiguity, negation, and multiple opinions in sentences are also outlined. The goal is to develop systems that can detect emotions beyond just keywords to gain a deeper understanding of emotional expression and changes over time from text.
Amazon seniment
Amazon senimentSubhadeep Chakraborty This document discusses analyzing customer reviews on Amazon to develop a recommender system. It provides background on Amazon and the importance of customer reviews. It then outlines a methodology to collect review data, analyze sentiment and ratings, apply machine learning techniques like Naive Bayes for classification, and develop a recommender system. The analysis will identify positive and negative sentiments to recommend high-scoring products and the system could potentially be extended to other online marketplaces.
Sentiment Analysis
Sentiment AnalysisAditya Nag This document discusses sentiment analysis. It defines sentiment analysis as analyzing text to determine the writer's feelings and opinions. It notes the rapid growth of subjective text online and how businesses and individuals can benefit from understanding sentiments. It describes common applications like brand analysis and political opinion mining. It also outlines different approaches to sentiment analysis like using semantics, machine learning classifiers, and sentiment lexicons. The document provides an example implementation and discusses advantages like lower costs and more accurate customer feedback.
Sensor Cloud
Sensor CloudDebjyoti Ghosh The document discusses sensor cloud, which integrates wireless sensor networks with cloud computing. It allows for the powerful analysis of sensor data through massive cloud infrastructure. The key benefits of sensor cloud include scalability, increased data storage and processing power, dynamic provisioning of services, and automation. Some challenges are implementation costs and maintaining continuous connectivity between sensors and the cloud. The document outlines the general architecture and components of a sensor cloud system and provides examples of applications in transportation monitoring, military use, weather forecasting, and healthcare.
Absa project
Absa projectIndranil Mukherjee This document discusses aspect-based sentiment analysis (ABSA) which aims to identify aspects of target entities and the sentiment expressed towards each aspect. It describes 4 tasks: 1) aspect term extraction, 2) aspect term polarity detection, 3) aspect term categorization, and 4) aspect category polarity detection. It provides examples and discusses the tools and methods used for each task, including using the Stanford CoreNLP parser and NLTK. Challenges included some aspect terms not being correctly extracted by rules and assigning polarity when multiple aspects are present. The dataset came from restaurant reviews in XML format.
Emotion Speech Recognition - Convolutional Neural Network Capstone Project
Emotion Speech Recognition - Convolutional Neural Network Capstone ProjectDiego Rios The document discusses a capstone project focused on speech emotion recognition using a CNN model to detect emotions from audio files, which can help route calls to humans when users are upset. It describes the data collection process involving 1,170 audio files representing 8 emotions and explains the feature extraction using mel-frequency cepstral coefficients (MFCCs). The model showed improved accuracy of 75% using a 1D CNN over random classification, although issues like data imbalance and misclassification of certain emotions were noted.
Opinion Mining
Opinion MiningAli Habeeb This document provides an overview of opinion mining and sentiment analysis. It defines opinion mining as attempting to automatically determine human opinion from natural language text. It discusses some key applications, such as classifying reviews and understanding public opinion. The document also outlines some challenges, such as understanding context and differing domains. It then describes common models for sentiment analysis, including preparing data, analyzing reviews linguistically, and classifying sentiment using techniques like machine learning classifiers.
Presentation on supervised learning
Presentation on supervised learningTonmoy Bhagawati This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Sentiment Analysis
Sentiment AnalysisDinesh V This document discusses sentiment analysis techniques for understanding customer opinions expressed in text. It describes how sentiment analysis uses natural language processing and machine learning algorithms to classify text sentiments as positive, negative, or neutral. Conducting sentiment analysis can provide businesses with valuable customer insight to improve products, services, and marketing strategies.
NLP.pptx
NLP.pptxRahul Borate The document provides information about natural language processing (NLP) including:
1. NLP stands for natural language processing and involves using machines to understand, analyze, and interpret human language.
2. The history of NLP began in the 1940s and modern NLP consists of applications like speech recognition and machine translation.
3. The two main components of NLP are natural language understanding, which helps machines understand language, and natural language generation, which converts computer data into natural language.
New sentiment analysis of tweets using python by Ravi kumar
New sentiment analysis of tweets using python by Ravi kumarRavi Kumar The document presents a detailed overview of sentiment analysis applied to tweets using Python, explaining its definition, importance, and methods for extraction and classification. It covers the tools used, such as the Twitter API and the VADER lexicon for sentiment scoring, along with challenges and preprocessing techniques. The conclusion highlights the significant potential for future research and improvements in sentiment analysis models.
Natural language processing (nlp)
Natural language processing (nlp)Kuppusamy P This document provides an overview of natural language processing (NLP). It discusses how NLP allows computers to understand human language through techniques like speech recognition, text analysis, and language generation. The document outlines the main components of NLP including natural language understanding and natural language generation. It also describes common NLP tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Finally, the document explains how to build an NLP pipeline by applying these techniques in a sequential manner.
Python report on twitter sentiment analysis
Python report on twitter sentiment analysisAntaraBhattacharya12 This document outlines a project on analyzing sentiment from Twitter data using Python. Chapter 1 introduces the tools and packages used, including Tweepy, tkinter, TextBlob and Matplotlib. Chapter 2 describes collecting tweets using the Twitter API, preprocessing the data through tokenization and removing stop words. Chapter 3 presents the results of the sentiment analysis but does not provide details. Chapter 4 concludes that the project covered basics of Twitter data collection and preprocessing in Python as an introduction to more advanced analysis.
Twitter sentiment analysis
Twitter sentiment analysisSunil Kandari This document discusses sentiment analysis on Twitter data using machine learning classifiers. It describes Twitter sentiment analysis as determining if a tweet is positive, negative, or neutral. Some challenges are that people express opinions complexly using sarcasm, irony, and slang. The document tests different classifiers like Naive Bayes and SVM on Twitter data preprocessed by tokenizing, extracting sentiment features, and part-of-speech tagging. It finds that extracting more features like sentiment and part-of-speech tags along with an SVM classifier achieves the best accuracy of 68% at determining tweet sentiment.
NLP
NLPGirish Khanzode This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
Evolutionary Computing
Evolutionary ComputingMadhawa Gunasekara This document describes how a genetic algorithm can be used to select the best cricket team from a pool of players without human intervention. It discusses representing the team as a chromosome with 11 genes corresponding to each player position. A fitness function calculates the total performance score for each team. Selection is done with tournament selection to choose teams for reproduction. Crossover and mutation operators combine genes to generate new teams, while invalid teams are removed. The process runs until convergence on the highest scoring team.
Opinion Mining or Sentiment Analysis
Opinion Mining or Sentiment AnalysisRachna Raveendran The document discusses opinion mining and sentiment analysis, emphasizing their significance due to the proliferation of online opinions on social platforms. It outlines the components and types of sentiment classification, highlighting methods like support vector machines for text categorization. Additionally, it addresses the complexities and future directions of sentiment analysis, noting its applications in marketing and customer relations.
Spell checker using Natural language processing
Spell checker using Natural language processing Sandeep Wakchaure This document presents an overview of spell checking techniques in natural language processing. It discusses how spell checkers work by scanning text, comparing words to a dictionary, and using language-dependent algorithms. Two categories of spelling errors are described: real-word errors involving correctly spelled words and non-word errors containing no dictionary words. Techniques for error detection include dictionary lookup and n-gram comparisons using the Jaccard coefficient. The Levenshtein distance and Jaccard coefficient algorithms are then explained and shown to provide suggestions by calculating the edit distance between source and target words. The presentation concludes that these algorithms filter dictionary words and provide accurate suggestions to correct spelling mistakes in text.
Natural Language Processing
Natural Language ProcessingVeenaSKumar2 The document provides an overview of Natural Language Processing (NLP), including its definition, importance, basic terminology, and various approaches. It discusses components of NLP such as Natural Language Understanding and Generation, and outlines applications like machine translation and sentiment analysis. Additionally, it covers the Natural Language Toolkit (nltk) for Python setup and usage, enhancing understanding and implementation of NLP tasks.
Natural language processing PPT presentation
Natural language processing PPT presentationSai Mohith The document discusses a technical seminar on Natural Language Processing (NLP) presented by Sai Mohit Talasila, highlighting the interaction between computers and human languages. It covers NLP techniques like stemming, segmentation, word sense disambiguation, parts of speech tagging, contextual analysis, and sentiment analysis, illustrating their importance in processing language data. The conclusion emphasizes the goal of NLP to enhance computer understanding of human language, advancing artificial intelligence capabilities.
Twitter sentiment analysis project report
Twitter sentiment analysis project reportBharat Khanna The document outlines a project focused on sentiment analysis of Mr. Narendra Modi's brand image using Twitter data, utilizing Apache Flume and Hortonworks Hadoop. It describes the process of fetching unstructured data from Twitter, transforming it into a tabular format in Hive, and visualizing sentiment analysis results using Tableau. The project goals include analyzing tweet metrics and sentiments globally while providing graphical tools for ad-hoc analysis.
Sentiment Analysis Using Twitter
Sentiment Analysis Using Twitterpiya chauhan This document analyzes sentiment toward two giant companies using tweets. It extracted 2500 tweets about the companies from Twitter and cleaned the data. Word clouds and histograms were created to visualize word frequencies for the overall data and positive or negative responses. A machine learning algorithm classified the sentiment with 87.4% and 84.5% accuracy. Both companies had overall positive sentiment but Amazon had slightly higher positive ratings, indicating it currently has stronger customer favorability.
Language processors
Language processorsGanesh Wedpathak The document discusses language processors that bridge gaps between software design descriptions and their implementation in computer systems. It outlines the concepts of semantic, specification, and execution gaps, as well as various types of language processors, such as translators, preprocessors, and interpreters. Additionally, it covers fundamental phases of language processing, including analysis and synthesis of source and target programs, alongside the structure and classification of programming language grammars.
Speech emotion recognition
Speech emotion recognitionsaniya shaikh The document discusses speech emotion recognition using machine learning. It aims to build a model to recognize emotion from speech using the librosa and sklearn libraries and the RAVDESS dataset. It extracts MFCC, mel spectrogram, and chroma features from the dataset and uses an MLP classifier to classify emotions into 8 categories with an accuracy of 66.67%. The model works best at identifying calm emotions and gets confused between similar emotions. Future work could explore using larger datasets with CNN, RNN models on different speakers and accents.
Twitter sentiment-analysis Jiit2013-14
Twitter sentiment-analysis Jiit2013-14Rachit Goel Tweezer is a Twitter sentiment analysis tool that classifies tweets as positive, negative, or neutral based on a query term entered by the user. It collects relevant tweets through Twitter's API, pre-processes the tweets by removing emojis, URLs, stop words, usernames and hashtags. It then classifies the sentiment through either binary, 3-tier, or 5-tier classification methods. The tool detects sarcasm using techniques like identifying positive words with negative emojis. Future work includes improving pre-processing, updating the sentiment dictionary, creating a mobile app, and adding context to sentiment analysis.
A framework for emotion mining from text in online social networks(final)
A framework for emotion mining from text in online social networks(final)es712 This document proposes a framework for characterizing emotional interactions in social networks to distinguish friends from acquaintances. It collects posts and comments from social networks, develops lexicons to analyze informal language, generates features to assess text subjectivity, trains a model to classify text subjectivity, and uses this to train an SVM model that predicts relationships with 87% accuracy.
Emotion recognition
Emotion recognitionMadhusudhan G The document proposes developing Android applications to sense emotions using smartphones for better health and human-machine interactions. It discusses detecting emotions through passive sensors like cameras, microphones, and accelerometers that can capture facial expressions, speech, heart rate without interpreting input. Recognition involves extracting meaningful patterns from sensor data using techniques like speech recognition, facial expression detection to produce labels or inference algorithms. Specific techniques are discussed for recognizing emotions from speech, facial expressions based on the Facial Action Coding System, and heart rate variability. The conclusion states that understanding emotions with smartphones can help people succeed and make research easier.
More Related Content
What's hot (20)
Emotion Speech Recognition - Convolutional Neural Network Capstone Project
Emotion Speech Recognition - Convolutional Neural Network Capstone ProjectDiego Rios The document discusses a capstone project focused on speech emotion recognition using a CNN model to detect emotions from audio files, which can help route calls to humans when users are upset. It describes the data collection process involving 1,170 audio files representing 8 emotions and explains the feature extraction using mel-frequency cepstral coefficients (MFCCs). The model showed improved accuracy of 75% using a 1D CNN over random classification, although issues like data imbalance and misclassification of certain emotions were noted.
Opinion Mining
Opinion MiningAli Habeeb This document provides an overview of opinion mining and sentiment analysis. It defines opinion mining as attempting to automatically determine human opinion from natural language text. It discusses some key applications, such as classifying reviews and understanding public opinion. The document also outlines some challenges, such as understanding context and differing domains. It then describes common models for sentiment analysis, including preparing data, analyzing reviews linguistically, and classifying sentiment using techniques like machine learning classifiers.
Presentation on supervised learning
Presentation on supervised learningTonmoy Bhagawati This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Sentiment Analysis
Sentiment AnalysisDinesh V This document discusses sentiment analysis techniques for understanding customer opinions expressed in text. It describes how sentiment analysis uses natural language processing and machine learning algorithms to classify text sentiments as positive, negative, or neutral. Conducting sentiment analysis can provide businesses with valuable customer insight to improve products, services, and marketing strategies.
NLP.pptx
NLP.pptxRahul Borate The document provides information about natural language processing (NLP) including:
1. NLP stands for natural language processing and involves using machines to understand, analyze, and interpret human language.
2. The history of NLP began in the 1940s and modern NLP consists of applications like speech recognition and machine translation.
3. The two main components of NLP are natural language understanding, which helps machines understand language, and natural language generation, which converts computer data into natural language.
New sentiment analysis of tweets using python by Ravi kumar
New sentiment analysis of tweets using python by Ravi kumarRavi Kumar The document presents a detailed overview of sentiment analysis applied to tweets using Python, explaining its definition, importance, and methods for extraction and classification. It covers the tools used, such as the Twitter API and the VADER lexicon for sentiment scoring, along with challenges and preprocessing techniques. The conclusion highlights the significant potential for future research and improvements in sentiment analysis models.
Natural language processing (nlp)
Natural language processing (nlp)Kuppusamy P This document provides an overview of natural language processing (NLP). It discusses how NLP allows computers to understand human language through techniques like speech recognition, text analysis, and language generation. The document outlines the main components of NLP including natural language understanding and natural language generation. It also describes common NLP tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Finally, the document explains how to build an NLP pipeline by applying these techniques in a sequential manner.
Python report on twitter sentiment analysis
Python report on twitter sentiment analysisAntaraBhattacharya12 This document outlines a project on analyzing sentiment from Twitter data using Python. Chapter 1 introduces the tools and packages used, including Tweepy, tkinter, TextBlob and Matplotlib. Chapter 2 describes collecting tweets using the Twitter API, preprocessing the data through tokenization and removing stop words. Chapter 3 presents the results of the sentiment analysis but does not provide details. Chapter 4 concludes that the project covered basics of Twitter data collection and preprocessing in Python as an introduction to more advanced analysis.
Twitter sentiment analysis
Twitter sentiment analysisSunil Kandari This document discusses sentiment analysis on Twitter data using machine learning classifiers. It describes Twitter sentiment analysis as determining if a tweet is positive, negative, or neutral. Some challenges are that people express opinions complexly using sarcasm, irony, and slang. The document tests different classifiers like Naive Bayes and SVM on Twitter data preprocessed by tokenizing, extracting sentiment features, and part-of-speech tagging. It finds that extracting more features like sentiment and part-of-speech tags along with an SVM classifier achieves the best accuracy of 68% at determining tweet sentiment.
NLP
NLPGirish Khanzode This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
Evolutionary Computing
Evolutionary ComputingMadhawa Gunasekara This document describes how a genetic algorithm can be used to select the best cricket team from a pool of players without human intervention. It discusses representing the team as a chromosome with 11 genes corresponding to each player position. A fitness function calculates the total performance score for each team. Selection is done with tournament selection to choose teams for reproduction. Crossover and mutation operators combine genes to generate new teams, while invalid teams are removed. The process runs until convergence on the highest scoring team.
Opinion Mining or Sentiment Analysis
Opinion Mining or Sentiment AnalysisRachna Raveendran The document discusses opinion mining and sentiment analysis, emphasizing their significance due to the proliferation of online opinions on social platforms. It outlines the components and types of sentiment classification, highlighting methods like support vector machines for text categorization. Additionally, it addresses the complexities and future directions of sentiment analysis, noting its applications in marketing and customer relations.
Spell checker using Natural language processing
Spell checker using Natural language processing Sandeep Wakchaure This document presents an overview of spell checking techniques in natural language processing. It discusses how spell checkers work by scanning text, comparing words to a dictionary, and using language-dependent algorithms. Two categories of spelling errors are described: real-word errors involving correctly spelled words and non-word errors containing no dictionary words. Techniques for error detection include dictionary lookup and n-gram comparisons using the Jaccard coefficient. The Levenshtein distance and Jaccard coefficient algorithms are then explained and shown to provide suggestions by calculating the edit distance between source and target words. The presentation concludes that these algorithms filter dictionary words and provide accurate suggestions to correct spelling mistakes in text.
Natural Language Processing
Natural Language ProcessingVeenaSKumar2 The document provides an overview of Natural Language Processing (NLP), including its definition, importance, basic terminology, and various approaches. It discusses components of NLP such as Natural Language Understanding and Generation, and outlines applications like machine translation and sentiment analysis. Additionally, it covers the Natural Language Toolkit (nltk) for Python setup and usage, enhancing understanding and implementation of NLP tasks.
Natural language processing PPT presentation
Natural language processing PPT presentationSai Mohith The document discusses a technical seminar on Natural Language Processing (NLP) presented by Sai Mohit Talasila, highlighting the interaction between computers and human languages. It covers NLP techniques like stemming, segmentation, word sense disambiguation, parts of speech tagging, contextual analysis, and sentiment analysis, illustrating their importance in processing language data. The conclusion emphasizes the goal of NLP to enhance computer understanding of human language, advancing artificial intelligence capabilities.
Twitter sentiment analysis project report
Twitter sentiment analysis project reportBharat Khanna The document outlines a project focused on sentiment analysis of Mr. Narendra Modi's brand image using Twitter data, utilizing Apache Flume and Hortonworks Hadoop. It describes the process of fetching unstructured data from Twitter, transforming it into a tabular format in Hive, and visualizing sentiment analysis results using Tableau. The project goals include analyzing tweet metrics and sentiments globally while providing graphical tools for ad-hoc analysis.
Sentiment Analysis Using Twitter
Sentiment Analysis Using Twitterpiya chauhan This document analyzes sentiment toward two giant companies using tweets. It extracted 2500 tweets about the companies from Twitter and cleaned the data. Word clouds and histograms were created to visualize word frequencies for the overall data and positive or negative responses. A machine learning algorithm classified the sentiment with 87.4% and 84.5% accuracy. Both companies had overall positive sentiment but Amazon had slightly higher positive ratings, indicating it currently has stronger customer favorability.
Language processors
Language processorsGanesh Wedpathak The document discusses language processors that bridge gaps between software design descriptions and their implementation in computer systems. It outlines the concepts of semantic, specification, and execution gaps, as well as various types of language processors, such as translators, preprocessors, and interpreters. Additionally, it covers fundamental phases of language processing, including analysis and synthesis of source and target programs, alongside the structure and classification of programming language grammars.
Speech emotion recognition
Speech emotion recognitionsaniya shaikh The document discusses speech emotion recognition using machine learning. It aims to build a model to recognize emotion from speech using the librosa and sklearn libraries and the RAVDESS dataset. It extracts MFCC, mel spectrogram, and chroma features from the dataset and uses an MLP classifier to classify emotions into 8 categories with an accuracy of 66.67%. The model works best at identifying calm emotions and gets confused between similar emotions. Future work could explore using larger datasets with CNN, RNN models on different speakers and accents.
Twitter sentiment-analysis Jiit2013-14
Twitter sentiment-analysis Jiit2013-14Rachit Goel Tweezer is a Twitter sentiment analysis tool that classifies tweets as positive, negative, or neutral based on a query term entered by the user. It collects relevant tweets through Twitter's API, pre-processes the tweets by removing emojis, URLs, stop words, usernames and hashtags. It then classifies the sentiment through either binary, 3-tier, or 5-tier classification methods. The tool detects sarcasm using techniques like identifying positive words with negative emojis. Future work includes improving pre-processing, updating the sentiment dictionary, creating a mobile app, and adding context to sentiment analysis.
Viewers also liked (20)
A framework for emotion mining from text in online social networks(final)
A framework for emotion mining from text in online social networks(final)es712 This document proposes a framework for characterizing emotional interactions in social networks to distinguish friends from acquaintances. It collects posts and comments from social networks, develops lexicons to analyze informal language, generates features to assess text subjectivity, trains a model to classify text subjectivity, and uses this to train an SVM model that predicts relationships with 87% accuracy.
Emotion recognition
Emotion recognitionMadhusudhan G The document proposes developing Android applications to sense emotions using smartphones for better health and human-machine interactions. It discusses detecting emotions through passive sensors like cameras, microphones, and accelerometers that can capture facial expressions, speech, heart rate without interpreting input. Recognition involves extracting meaningful patterns from sensor data using techniques like speech recognition, facial expression detection to produce labels or inference algorithms. Specific techniques are discussed for recognizing emotions from speech, facial expressions based on the Facial Action Coding System, and heart rate variability. The conclusion states that understanding emotions with smartphones can help people succeed and make research easier.
Applications of Emotions Recognition
Applications of Emotions RecognitionFrancesco Bonadiman This document discusses applications of emotion recognition technology across several domains:
1) Medicine - To help monitor patients and enhance healthcare like rehabilitation, companion robots, and counseling. It could also help with autism therapy and music therapy.
2) E-learning - To adjust an online tutor's presentation based on a learner's emotions, making the tutoring experience more interactive and effective.
3) Monitoring - Such as detecting emotions in car drivers to alert other cars, warning employees before angry emails, and prioritizing angry calls at call centers.
4) Entertainment - Like music players that satisfy users based on recognized moods.
5) Marketing - To improve advertising by understanding emotional impacts and engagement
Facial expression
Facial expression17791 The document summarizes key aspects of facial expressions. It discusses the basic structure of facial expressions involving the eyes and full face. It then examines specific facial expressions for happiness, sadness, anger and fear by describing the characteristics of the mouth and eyes for each. The document also covers facial expressions in animation, individual differences in expressions, and tips for facial expressions when giving presentations, such as smiling with the eyes and using expressions to engage an audience.
5 R Tutorial Data Visualization
5 R Tutorial Data VisualizationSakthi Dasans The document discusses various data visualization techniques in R including bar plots, pie charts, histograms, kernel density plots, line charts, box plots, heat maps, and word clouds. It provides code examples for creating basic and customized visualizations of different types using the R programming language and exporting the visualizations to file formats like PDF, PNG, and JPEG.
Elements of Text Mining Part - I
Elements of Text Mining Part - IJaganadh Gopinadhan This document discusses various text mining and natural language processing techniques in Python, including tokenization, sentence tokenization, word counting, finding word lengths, word proportions, word types and ratios, finding top N words, plotting word frequencies, lexical dispersion plots, tag clouds, word co-occurrence matrices, and stop words filtering. Code examples are provided for implementing each technique in Python.
Emotion recognition using facial expressions and speech
Emotion recognition using facial expressions and speechLakshmi Sarvani Videla This document discusses non-intrusive methods for recognizing a driver's emotions using vision and acoustic sensing in an advanced driver assistance system. It describes how emotions can impact driver attentiveness and safety. Six primary emotions are identified: anger, disgust, fear, happiness, sadness, and surprise. Various techniques are discussed for extracting visual features from face images and acoustic features from speech to classify emotions, along with their advantages and limitations. Prior work on emotion recognition from speech using Hidden Markov Models, spectral features, and other approaches is also summarized.
Emotion based music player
Emotion based music playerNizam Muhammed The document is a seminar report on an EMO player that generates playlists based on a user's facial expressions. It includes sections on introduction, literature review, current music systems and their limitations, the proposed system, Viola-Jones algorithm, experimental results, conclusion, applications, future scope, and references. The proposed system aims to automatically generate playlists based on detected facial expressions in order to reduce the effort required for manual playlist generation. It uses modules for emotion feature extraction from faces, audio feature extraction, emotion-audio integration, and a classifier to map emotions to music selections.
Emotion based music player
Emotion based music playerNizam Muhammed This document describes an emotion-based music player that generates playlists based on a user's detected mood. It uses three main modules: an emotion extraction module that analyzes facial expressions from webcam images to determine mood, an audio feature extraction module that extracts data from songs, and an emotion-audio recognition module that maps the facial and audio features to select songs for the playlist. The system aims to reduce the effort of manually creating playlists by automatically generating ones tailored to the user's current emotional state. It works by classifying facial expressions and songs into categories like happy, sad, and angry to create playlists that match or influence the user's detected mood.
HUMAN EMOTION RECOGNIITION SYSTEM
HUMAN EMOTION RECOGNIITION SYSTEMsoumi sarkar This document presents a human emotion recognition system that uses facial expression analysis to identify emotions. It discusses how emotions are important to human life and interaction. The system first captures images of a human face and preprocesses the images to extract features. It then compares the facial features to examples in a database to recognize the emotion based on distances between features. The system can identify six basic emotions with up to 97% accuracy. Limitations and potential to incorporate fuzzy logic for improved classification are also discussed.
Big Data & Text Mining
Big Data & Text MiningMichel Bruley The document discusses text mining, emphasizing the need for automated tools to analyze large textual datasets due to the vast amount of unstructured data available. It outlines the text mining process, including key tasks such as categorization, extraction, and clustering, as well as the challenges faced in processing unstructured information. Moreover, it highlights Aster Data's capabilities for handling massive volumes of text data and providing analytics solutions through SQL-MapReduce frameworks.
The revolt of 1857
The revolt of 1857Arnav Bansal The document summarizes the major causes and events of the 1857 revolt in India against British rule. It describes how sepoys in Delhi appealed to the Mughal emperor Bahadur Shah to lead the revolt. The revolt was caused by resentment among Indians towards British economic exploitation, annexation of territories, and racial discrimination. Key figures in the revolt included Rani Laxmi Bai, Mangal Pandey, and Bahadur Shah Zafar. The revolt was eventually suppressed by the British with the help of the army, but led to the end of East India Company rule and more autonomy under direct British governance.
Basic Formal Ontology: A Common Standard
Basic Formal Ontology: A Common StandardBarry Smith The document discusses the Basic Formal Ontology (BFO), a top-level ontology intended to support information integration in scientific research. BFO defines three fundamental dichotomies: continuant vs. occurrent, independent vs. dependent, and type vs. instance. It also distinguishes between specifically dependent continuants like qualities and realizable dependent continuants like functions. The goal of BFO is to provide a framework for building consistent lower-level ontologies in a reusable manner.
Towards an Ontology for Describing Emotions
Towards an Ontology for Describing EmotionsRoberto García The document presents a conceptual model for an ontology that describes emotions, emphasizing context-aware and emergent emotions. It outlines various cognitive models of emotions and discusses the integration of multimodal and contextual elements in emotion detection systems. The proposed ontology aims to be flexible and extensible, facilitating emotion-aware applications and enhancing user experience by adapting to individual emotional responses.
The Emotion Ontology
The Emotion OntologyBarry Smith The Emotion Ontology provides definitions and classifications for emotions, feelings, appraisals, physiological responses, and disorders. It defines core components of emotions like canonical emotions, action tendencies, subjective feelings, and behaviors. The ontology also covers types of appraisal processes, feelings, and physiological responses. It defines emotions like fear, sadness, and pain and different types of each emotion. The document outlines how the Emotion Ontology can be used to represent emotional data and classify tasks involved in cognitive neuroscience studies of emotions.
The emotion ontology: enabling interdisciplinary research in the affective sc...
The emotion ontology: enabling interdisciplinary research in the affective sc...Janna Hastings The document summarizes work presented at the 2011 International Conference on Biomedical Ontology (ICBO) on developing an Emotion Ontology. It discusses the need for an ontology to facilitate analysis and aggregation of disparate affective science data across research disciplines. It presents the ontological classification of emotions, occurrents, dispositions, personality traits and their relationships. Components of emotion like appraisal processes, physiological responses and behaviors are also defined.
Your emotions and the impact of your reactions pdf
Your emotions and the impact of your reactions pdfBettina Pickering Bettina Pickering's document discusses the critical role of emotions in coaching, highlighting their impact on clients and the coaching relationship. It emphasizes the need for coaches to be aware of their own emotions and to avoid projecting them onto clients, while also providing strategies for facilitating emotional awareness and shifts in clients. The document also outlines various types and functions of emotions, underscoring their significance in personal and professional development.
Mob ocr
Mob ocrVivek Bharadwaj This document summarizes a seminar presentation on mobile camera based text detection and translation. The presentation covers the history of optical character recognition from its origins in the 1920s to modern implementations using mobile cameras. It then describes the working of the system using steps like image capture, text detection, translation via an API, and displaying results. Requirements, a block diagram, test results and examples are also included. The document concludes by discussing applications and limitations of the technology.
SA2: Text Mining from User Generated Content
SA2: Text Mining from User Generated ContentJohn Breslin The document provides an extensive overview of text mining and information extraction (IE) techniques, differentiating between information retrieval and extraction. It discusses various applications of IE, including event-based summarization and sentiment mining, while outlining challenges, methodologies, and accuracy metrics associated with different types of information. It highlights the role of human involvement in achieving high accuracy in text mining systems.
Emotive Triggers
Emotive TriggersPowerhouse Advertising The document discusses the importance of understanding customer emotions and needs in market segmentation and targeting. It emphasizes the role of emotive triggers in driving customer behavior and suggests methods for identifying these triggers through qualitative analysis and strategic frameworks. The content highlights the distinction between emotion and reason and the need for businesses to connect emotionally with their target market.
Ad
Similar to Emotion detection from text using data mining and text mining (20)
Issues in Sentiment analysis
Issues in Sentiment analysisIOSR Journals This document discusses issues in sentiment analysis and emotion extraction from text. It provides an overview of different techniques used for emotion extraction like text mining, empirical studies, emotion extraction engines, and vector space models. It then analyzes the issues with each technique, such as only identifying the subject but not sentiment, inability to determine intensity, and difficulties with contradictory or symbolic text. The document concludes that combining the study of multiple techniques and parameters could help develop a more accurate system for sentiment analysis that is closer to realistic human emotion extraction from text.
An Analytical Exploration of Decoding Emotions in Text through NLP
An Analytical Exploration of Decoding Emotions in Text through NLPAakashRoy30 The document discusses the analytical exploration of decoding emotions in text through natural language processing (NLP), led by Aakash Roy. It covers topics such as the architecture of emotion detection, preprocessing text for analysis, software requirements, and various emotion detection techniques including rule-based and emotion-specific models. Challenges in emotion detection, future work directions such as ethical AI, and applications in mental health and marketing are also highlighted.
S33100107
S33100107IJERA Editor This paper discusses a computational approach for analyzing and detecting emotions in Arabic text, specifically focusing on children's stories and utilizing a moderately-sized Arabic emotion lexicon. The proposed method achieved 65% accuracy in emotion detection across six basic emotions: joy, fear, sadness, anger, disgust, and surprise. The study aims to enhance human-computer interaction by making interfaces responsive to users' emotional states, despite the challenges of subjectivity in emotion expression.
Beamer_Presentation_Template_for_IISER_TVM.pdf
Beamer_Presentation_Template_for_IISER_TVM.pdfDevenderYadav65 The document discusses the detection of emotions from speech, highlighting its significance in improving human-computer interaction. It reviews traditional and deep learning methods, pointing out the limitations of traditional approaches and the advantages of deep learning with attention mechanisms. The future plan includes implementing emotion detection across various forms of communication while utilizing the Tacotron 2 architecture.
Emotion Classification Using Massive Examples Extracted From The Web
Emotion Classification Using Massive Examples Extracted From The Webceya This document proposes a data-driven method for emotion classification using a large corpus of emotion-provoking events extracted from the web. The method involves first classifying the sentiment polarity of sentences as positive, negative, or neutral, and then further classifying the emotion into categories such as happy, sad, angry. Experiments show the two-step classification approach outperforms a baseline model, with word n-grams and a sentiment dictionary as effective features for the classification tasks.
Do you Mean what you say? Recognizing Emotions.
Do you Mean what you say? Recognizing Emotions.Sunil Kumar Kopparapu This document summarizes an invited talk on recognizing emotions in spontaneous speech. It discusses the challenges of emotion recognition in spontaneous speech compared to acted speech, including the subtlety of emotions, data mismatch between training and testing, difficulty building spontaneous speech corpora, and disagreement among annotators. The talk presents two approaches to emotion recognition from audio and focuses on developing a knowledge-based framework to address spontaneous speech. It highlights ongoing work on classifier error corrections and challenges that remain, such as optimal features, acoustic variability, and incorporating speaker attributes.
Emotion detection from text documents
Emotion detection from text documentsIJDKP The document discusses the emerging field of emotion detection from text, highlighting its importance in human-computer interaction. It presents a knowledge-based survey of existing methods and proposes a new architecture that combines emotion ontology and an emotion detector algorithm to classify text into six emotional categories. The proposed system aims to address the limitations of current methods by using semantic approaches to enhance emotion recognition capabilities.
EMOTION DETECTION FROM TEXT
EMOTION DETECTION FROM TEXTcscpconf The document discusses techniques for detecting emotions from text, including keyword spotting, lexical affinity methods, and learning-based methods. It proposes a new architecture that combines an emotion ontology and emotion detector algorithm. The emotion ontology is developed using an emotion word hierarchy converted into a class/subclass format. The emotion detector calculates scores for emotion words based on parameters like frequency, depth in ontology, and parent-child relationships to determine the overall emotion class of the text. The proposed approach aims to improve on existing methods by leveraging an emotion ontology and a scoring algorithm to detect emotions more accurately.
Data Augmentation for Improving Emotion Recognition in Software Engineering C...
Data Augmentation for Improving Emotion Recognition in Software Engineering C...Preetha Chatterjee This paper explores improving emotion recognition in software engineering communication through data augmentation. It summarizes that developers often express emotions in communications, but current tools perform poorly at emotion recognition due to a lack of large, high-quality datasets. The paper manually annotates 2000 comments with emotions, extends an emotion taxonomy, and analyzes errors in existing tools. It then evaluates three data augmentation strategies and finds that augmenting data while preserving sentiment polarity improves tool performance the most. The paper contributes annotated data and data augmentation source codes to aid future emotion recognition research.
Text to Emotion Extraction Using Supervised Machine Learning Techniques
Text to Emotion Extraction Using Supervised Machine Learning TechniquesTELKOMNIKA JOURNAL The document discusses a machine learning-based approach to extract emotions from text, particularly using social media data like Twitter. The study investigates various classifiers, with the Support Vector Machine (SVM) showing the best performance in accurately categorizing nine basic emotions without the need for a time-consuming emotion intensity lexicon. It highlights the importance of automated tools for emotion mining as vast amounts of emotional text are generated daily through social media.
2016 datascience emotion analysis - english version
2016 datascience emotion analysis - english versionYi-Shin Chen This document introduces Yi-Shin Chen and their work on emotion analysis using big data. It discusses using subconscious crowdsourcing to collect emotion data from social media without direct annotation. Key steps include preprocessing data, constructing graphs to identify emotion patterns, and ranking patterns for each emotion. Case studies analyze emotions in text and identify mental disorders in individuals by comparing patient and control groups. Potential applications include product feedback, election analysis, and more.
F0363942
F0363942iosrjournals This document discusses issues in sentiment analysis and emotion extraction from text. It provides an overview of natural language processing and its applications. The document then discusses the need for sentiment analysis in areas like artificial intelligence. It proceeds to compare different techniques for emotion extraction from text, including text mining, empirical studies, emotion extraction engines, vector space models, and emotion markup languages. For each technique, it outlines the general approach and provides examples or tables to illustrate how emotions can be identified from text. However, it notes that current applications have not achieved 100% accuracy in realistic sentiment analysis.
Emotion detection on social media status in Myanmar language
Emotion detection on social media status in Myanmar language IJECEIAES This paper presents an emotion detection system for social media posts written in the Myanmar language, focusing on user statuses on Facebook. The system preprocesses user posts through segmentation, stemming, part-of-speech tagging, and stop word removal before using a preconstructed word-emotion lexicon and machine learning classifiers (Naïve Bayes and Recurrent Neural Network) to classify emotions. The system achieves an accuracy of 85% with Naïve Bayes and 86% with RNN for emotion detection.
Emotion Detection from Tweets Using Ensemble Models (1).pptx
Emotion Detection from Tweets Using Ensemble Models (1).pptxKARANAMSANTOSH1 The document presents a study on emotion detection from tweets using ensemble models, highlighting methodologies that involve data processing, model training, and feature representation. It details the use of various machine learning techniques, including Random Forest, MLP, and LightGBM, achieving a high accuracy of 98% in classifying tweets into multiple emotional categories. The research suggests future directions for enhancing emotion analysis through categorical models and real-world validation.
Recognizing Emotions in Spontaneous Speech
Recognizing Emotions in Spontaneous SpeechSunil Kumar Kopparapu This document summarizes a keynote address on recognizing emotions in spontaneous speech. It discusses how emotions can be recognized from audio in either a two-step process of speech to text then emotion analysis, or directly from audio in one step. Direct emotion recognition from audio is preferable as it is independent of speech recognition accuracy and can handle mixed languages. However, building corpora of spontaneous speech with emotion labels is challenging due to the difficulties of eliciting natural emotional speech and achieving consistent emotion annotations. The talk explores these issues and the importance of developing methods and resources for emotion recognition from spontaneous speech.
Wenbo Wang dissertation defense, Kno.e.sis, Wright State University
Wenbo Wang dissertation defense, Kno.e.sis, Wright State UniversityArtificial Intelligence Institute at UofSC Wenbo Wang defended his PhD dissertation on automatic emotion identification from text. His dissertation focused on three areas: 1) Emotion classification using machine learning techniques to identify emotions from suicide notes and tweets. 2) Creating large self-labeled emotion datasets by leveraging hashtags on Twitter. 3) Adapting emotion identification models to new domains by selecting informative tweets to add to limited labeled data in target domains like blogs. The goal was to improve identification by utilizing large Twitter datasets while addressing challenges of limited labeled data in other domains.
Automatic Emotion Identification from Text
Automatic Emotion Identification from TextArtificial Intelligence Institute at UofSC The document outlines research on automatic emotion identification from text, focusing on leveraging social media data and machine learning techniques to classify emotions from text sources like suicide notes and Twitter. It discusses challenges in creating labeled training data and proposes methods for domain adaptation to improve emotion identification in various contexts. The findings suggest that large, self-labeled datasets significantly enhance classifier performance in detecting emotions.
NBLex: emotion prediction in Kannada-English code-switchtext using naïve baye...
NBLex: emotion prediction in Kannada-English code-switchtext using naïve baye...IJECEIAES This document summarizes a research paper that proposes a lexicon-based method called NBLex to predict emotions in Kannada-English code-switch text. The paper describes creating a Kannada-English code-switch corpus by collecting comments from YouTube and annotating them. A Naive Bayes classifier is used to build the NBLex lexicon and predict emotions. Evaluation shows the NBLex method achieves 87.9% accuracy for emotion prediction, outperforming Naive Bayes and BiLSTM baselines. The paper concludes that a simple additive lexicon approach can be an alternative to machine learning for emotion prediction in code-switch text.
Emotion Detection
Emotion DetectionMD. ABUL KALAM AZAD This presentation summarizes a thesis proposal on detecting human emotion on social media based on textual data. The proposal will use a classifier model to identify emotions from social media texts. It will cluster text data into 8 emotion classes to train the classifier. The goal is to analyze social media posts to understand public sentiment on issues and help inform decisions. While the approach only uses text data in English, identifying emotion across languages and media poses challenges.
Benchmarking supervived learning models for emotion analysis
Benchmarking supervived learning models for emotion analysisConference Papers The document summarizes a study that benchmarked supervised learning models for emotion analysis on sentence-level text data. It evaluated one deep learning model (BiLSTM) and one machine learning model (FastText) on two datasets with six emotion categories. The results showed that while requiring significantly more training time, the BiLSTM model consistently outperformed the FastText models in terms of precision, recall, and F1-score. Text preprocessing was also found to improve the performance of both models.
Wenbo Wang dissertation defense, Kno.e.sis, Wright State University
Wenbo Wang dissertation defense, Kno.e.sis, Wright State UniversityArtificial Intelligence Institute at UofSC
Ad
Recently uploaded (20)
How payment terms are configured in Odoo 18
How payment terms are configured in Odoo 18Celine George Payment terms in Odoo 18 help define the conditions for when invoices are due. This feature can split payments into multiple parts and automate due dates based on specific rules.
How to use _name_search() method in Odoo 18
How to use _name_search() method in Odoo 18Celine George In Odoo, the _name_search() method is used to implement or extend the logic for searching records by name or other fields. It is typically invoked when a user types into a many-to-one or many-to-many field's search box in the user interface.
Photo chemistry Power Point Presentation
Photo chemistry Power Point Presentationmprpgcwa2024 Photochemistry is the branch of chemistry that deals with the study of chemical reactions and processes initiated by light.
Photochemistry involves the interaction of light with molecules, leading to electronic excitation. Energy from light is transferred to molecules, initiating chemical reactions.
Photochemistry is used in solar cells to convert light into electrical energy.
It is used Light-driven chemical reactions for environmental remediation and synthesis. Photocatalysis helps in pollution abatement and environmental cleanup. Photodynamic therapy offers a targeted approach to treating diseases It is used in Light-activated treatment for cancer and other diseases.
Photochemistry is used to synthesize complex organic molecules.
Photochemistry contributes to the development of sustainable energy solutions.
Pests of Maize: An comprehensive overview.pptx
Pests of Maize: An comprehensive overview.pptxArshad Shaikh Maize is susceptible to various pests that can significantly impact yields. Key pests include the fall armyworm, stem borers, cob earworms, shoot fly. These pests can cause extensive damage, from leaf feeding and stalk tunneling to grain destruction. Effective management strategies, such as integrated pest management (IPM), resistant varieties, biological control, and judicious use of chemicals, are essential to mitigate losses and ensure sustainable maize production.
How to Manage Different Customer Addresses in Odoo 18 Accounting
How to Manage Different Customer Addresses in Odoo 18 AccountingCeline George A business often have customers with multiple locations such as office, warehouse, home addresses and this feature allows us to associate with different addresses with each customer streamlining the process of creating sales order invoices and delivery orders.
SCHIZOPHRENIA OTHER PSYCHOTIC DISORDER LIKE Persistent delusion/Capgras syndr...
SCHIZOPHRENIA OTHER PSYCHOTIC DISORDER LIKE Persistent delusion/Capgras syndr...parmarjuli1412 SCHIZOPHRENIA INCLUDED TOPIC IS INTRODUCTION, DEFINITION OF GENERAL TERM IN PSYCHIATRIC, THEN DIFINITION OF SCHIZOPHRENIA, EPIDERMIOLOGY, ETIOLOGICAL FACTORS, CLINICAL FEATURE(SIGN AND SYMPTOMS OF SCHIZOPHRENIA), CLINICAL TYPES OF SCHIZOPHRENIA, DIAGNOSIS, INVESTIGATION, TREATMENT MODALITIES(PHARMACOLOGICAL MANAGEMENT, PSYCHOTHERAPY, ECT, PSYCHO-SOCIO-REHABILITATION), NURSING MANAGEMENT(ASSESSMENT,DIAGNOSIS,NURSING INTERVENTION,AND EVALUATION), OTHER PSYCHOTIC DISORDER LIKE Persistent delusion/Capgras syndrome(The Delusion of Doubles)/Acute and Transient Psychotic Disorders/Induced Delusional Disorders/Schizoaffective Disorder /CAPGRAS SYNDROME(DELUSION OF DOUBLE), GERIATRIC CONSIDERATION, FOLLOW UP, HOMECARE AND REHABILITATION OF THE PATIENT,
LDMMIA Shop & Student News Summer Solstice 25
LDMMIA Shop & Student News Summer Solstice 25LDM & Mia eStudios 6/18/25
Shop, Upcoming: Final Notes to Review as we Close Level One. Make sure to review the orientation and videos as well. There’s more to come and material to cover in Levels 2-3. The content will be a combination of Reiki and Yoga. Also energy topics of our spiritual collective.
Thanks again all future Practitioner Level Students. Our Levels so far are: Guest, Grad, and Practitioner. We have had over 5k Spring Views.
https://ldm-mia.creator-spring.com
https://ldmchapels.weebly.com/
Public Health For The 21st Century 1st Edition Judy Orme Jane Powell
Public Health For The 21st Century 1st Edition Judy Orme Jane Powelltrjnesjnqg7801 Public Health For The 21st Century 1st Edition Judy Orme Jane Powell
Public Health For The 21st Century 1st Edition Judy Orme Jane Powell
Public Health For The 21st Century 1st Edition Judy Orme Jane Powell
Code Profiling in Odoo 18 - Odoo 18 Slides
Code Profiling in Odoo 18 - Odoo 18 SlidesCeline George Profiling in Odoo identifies slow code and resource-heavy processes, ensuring better system performance. Odoo code profiling detects bottlenecks in custom modules, making it easier to improve speed and scalability.
F-BLOCK ELEMENTS POWER POINT PRESENTATIONS
F-BLOCK ELEMENTS POWER POINT PRESENTATIONSmprpgcwa2024 F-block elements are a group of elements in the periodic table that have partially filled f-orbitals. They are also known as inner transition elements. F-block elements are divided into two series:
1.Lanthanides (La- Lu) These elements are also known as rare earth elements.
2.Actinides (Ac- Lr): These elements are radioactive and have complex electronic configurations.
F-block elements exhibit multiple oxidation states due to the availability of f-orbitals.
2. Many f-block compounds are colored due to f-f transitions.
3. F-block elements often exhibit paramagnetic or ferromagnetic behavior.4. Actinides are radioactive.
F-block elements are used as catalysts in various industrial processes.
Actinides are used in nuclear reactors and nuclear medicine.
F-block elements are used in lasers and phosphors due to their luminescent properties.
F-block elements have unique electronic and magnetic properties.
ECONOMICS, DISASTER MANAGEMENT, ROAD SAFETY - STUDY MATERIAL [10TH]
ECONOMICS, DISASTER MANAGEMENT, ROAD SAFETY - STUDY MATERIAL [10TH]SHERAZ AHMAD LONE This study material for Class 10th covers the core subjects of Economics, Disaster Management, and Road Safety Education, developed strictly in line with the JKBOSE textbook. It presents the content in a simplified, structured, and student-friendly format, ensuring clarity in concepts. The material includes reframed explanations, flowcharts, infographics, and key point summaries to support better understanding and retention. Designed for classroom teaching and exam preparation, it aims to enhance comprehension, critical thinking, and practical awareness among students.
Great Governors' Send-Off Quiz 2025 Prelims IIT KGP
Great Governors' Send-Off Quiz 2025 Prelims IIT KGPIIT Kharagpur Quiz Club Prelims of the Great Governors' Send-Off Quiz 2025 hosted by the outgoing governors.
QMs: Aarushi, Aatir, Aditya, Arnav
M&A5 Q1 1 differentiate evolving early Philippine conventional and contempora...
M&A5 Q1 1 differentiate evolving early Philippine conventional and contempora...ErlizaRosete MAPEH 6 QI WEEK I
OBSESSIVE COMPULSIVE DISORDER.pptx IN 5TH SEMESTER B.SC NURSING, 2ND YEAR GNM...
OBSESSIVE COMPULSIVE DISORDER.pptx IN 5TH SEMESTER B.SC NURSING, 2ND YEAR GNM...parmarjuli1412 OBSESSIVE COMPULSIVE DISORDER INCLUDED TOPICS ARE INTRODUCTION, DEFINITION OF OBSESSION, DEFINITION OF COMPULSION, MEANING OF OBSESSION AND COMPULSION, DEFINITION OF OBSESSIVE COMPULSIVE DISORDER, EPIDERMIOLOGY OF OCD, ETIOLOGICAL FACTORS OF OCD, CLINICAL SIGN AND SYMPTOMS OF OBSESSION AND COMPULSION, MANAGEMENT INCLUDED PHARMACOTHERAPY(ANTIDEPRESSANT DRUG+ANXIOLYTIC DRUGS), PSYCHOTHERAPY, NURSING MANAGEMENT(ASSESSMENT+DIAGNOSIS+NURSING INTERVENTION+EVALUATION))
INDUCTIVE EFFECT slide for first prof pharamacy students
INDUCTIVE EFFECT slide for first prof pharamacy studentsSHABNAM FAIZ The inductive effect is the electron-withdrawing or electron-donating effect transmitted through sigma (σ) bonds in a molecule due to differences in electronegativity between atoms.
---
🔹 Definition:
The inductive effect is the permanent shifting of electrons in a sigma bond caused by the electronegativity difference of atoms, resulting in partial charges within the molecule.
Tanja Vujicic - PISA for Schools contact Info
Tanja Vujicic - PISA for Schools contact InfoEduSkills OECD Tanja Vujicic, Senior Analyst and PISA for School’s Project Manager at the OECD spoke at the OECD webinar 'Turning insights into impact: What do early case studies reveal about the power of PISA for Schools?' on 20 June 2025
PISA for Schools is an OECD assessment that evaluates 15-year-old performance on reading, mathematics, and science. It also gathers insights into students’ learning environment, engagement and well-being, offering schools valuable data that help them benchmark performance internationally and improve education outcomes. A central ambition, and ongoing challenge, has been translating these insights into meaningful actions that drives lasting school improvement.
Emotion detection from text using data mining and text mining
- 1. Emotion Detection From Text using Data Mining and Text mining
- 2. Emotion Detection From Text ● Based on research paper published by Faculty of Engineering, The University of Tokushima at IEEE 2007 we build an intelligent system under the title Emotelligence on Text to recognize human emotion from textual contents. i.e. if you give an input string , our system would possibly able to say the emotion behind that textual content.
- 3. Emotion Detection From Text Approach the problem Step 1 : what are the emotions we are interested in. Step 2 : how to collect corpus or input data set. Step 3 : How to process and find the emotion.
- 4. Emotion Detection From Text ● ● ● Step 1 : what are the emotions we are interested in. On our investigation and research it has been found that, a human can express 16 types of emotion with the help of body gesture and speech. Since we are indented to find emotion from textual content ,we reduce our scope to find 8 basic emotion that are commonly seen in human expressed language.
- 5. Emotion Detection From Text 8 basic emotion : Joy, Trust, Fear, Surprise, Sadness, Disgust, Anger, Anticipate. 8 basic emotion will act as base to find other advance emotions. Eg : Basic Emotion + Basic Emotion = Advance Emotion Joy + Trust = Love
- 6. Emotion Detection From Text ● ● Step 2 : What type of input we are going to give. It is clear that our input are going to be a text but text could be on any language. we decided to go for English language, the only reason is that we have to finish our project in short span of time. Considering other language will consume more time in understanding the language structure and it is difficult to apply NLP techniques to unknown language. (other details are covered in step 3)
- 7. Emotion Detection From Text Step 3 : How do we going to find the emotion ● ● ● The idea is to treat 8 emotions as 8 different class for classifier. Train the classifier with the good training sets and then go for Testing. The result of classier will point to a class which is nothing but a expect emotion.
- 8. Emotion Detection From Text Training phase : Proper data set should be collected, inputs have to be sent to training phase of classifier. Training phase include two modules (I) Keyword extracting (II) Keyword conversion.
- 9. Emotion Detection From Text Keyword extraction : Unlink other native classification problems direct use of data set will not be useful to us. We need to identify the key terms that are useful for classifier from the in-putted data set. And Noun , Verb , Adverb , Adjective are the useful key terms to find emotion from text. In order to find them we applied POS tagger ( Part-of-speech tagging is the process of assigning a part-of-speech like noun, verb, pronoun, preposition, adverb, adjective or other lexical class marker to each word in a sentence.) and extracted words are the key terms that we want.
- 10. Emotion Detection From Text Example: ● ● ● Data Set : My brother was happy after passing the examination. POS Tagging : My/PRP$ brother/NN was/VBD happy/JJ after/IN passing/VBG the/DT examination/NN ./. Keywords extracted : brother was happy passing examination
- 11. Emotion Detection From Text Keyword conversion : We just implemented our own keyword conversion logic that convert the extracted keywords into numeric format that is accepted for our now implement classifier( NB Classifier ). Eg : Keywords extracted : brother was happy passing examination Text data is converted in to numerical data something similar as given below. Keyword Conversion : 3# 2:1 4:1 5:2 7:1 ……..
- 12. Emotion Detection From Text Abstract view towards the problem
- 13. Emotion Detection From Text Data set collection : We really showed our innovations in data set collection also. A good and proper dataset have to be collected . First question came to our mind is how to find dataset that are related to emotion and where to find them. Then we focused on the statement (English sentence that talk about emotion ) , we start our haunting on different blogs sites , we searched for English quotes , short poems etc. Then moved our search to social sites like twitter, face books to hunt for the emotional messages that shared among the friends etc. , we also collected news headlines and SMS as they also bring the emotional feel in ourself when we read them. In short Data set collection was a tough and we enjoy that also.
- 14. Emotion Detection From Text Testing phase : In testing phase also Keywords extraction and keyword conversion occurs then testing set subject to predicting part of the classifier to predict the class. We test few data set to measure the accuracy of the system and below table shows our accuracy results.
- 15. Emotion Detection From Text
- 16. Emotion Detection From Text Data set collection : We really showed our innovations in data set collection also. A good and proper dataset have to be collected . First question came to our mind is how to find dataset that are related to emotion and where to find them. Then we focused on the statement (English sentence that talk about emotion ) , we start our haunting on different blogs sites , we searched for English quotes , short poems etc. Then moved our search to social sites like twitter, face books to hunt for the emotional messages that shared among the friends etc. , we also collected news headlines and SMS as they also bring the emotional feel in ourself when we read them. In short Data set collection was a tough and we enjoy that also.
- 17. Emotion Detection From Text ● Results of our model
- 18. Emotion Detection From Text Accuracy results of our model No of corpus we user for Training : 1800 No of corpus we user for Testing : 200 Over all accuracy of the model : 71 % Highest individual class accuracy : 96 % for joy Lowest individual class accuracy : 2 % for surprise
- 19. Thank you If like the presentation... I would like to know your insert on endorsing me for my skills on my linkedin profile page. I would greatly appreciate If you could endorse me for Data mining, Text mining, Big Data, Machine Learning, Algorithms, and Mongodb. http://www.linkedin.com/profile/view?id=48289105
- 20. Thank you For more details on Emotion Detection http://shakthydoss.com/3-idiots-project/ Sakthi Dasan http://shakthydoss.com Twitter : @shakthydoss