Sentiment Analysis Intro
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This document summarizes two recent papers on sentiment analysis and emotion detection using emojis: 1. The first paper presented a method using millions of emoji occurrences on Twitter to learn representations that can detect sentiment, emotion, and sarcasm without requiring labeled training data. It achieved state-of-the-art results on several benchmarks. 2. The second paper introduced an interpretable emoji prediction model using label-wise attention LSTMs. This approach is less biased towards frequent emojis and provides visualizations of the attention weights to analyze model predictions. It was shown to better predict infrequent emojis compared to standard attention models.
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communications as well as in social media. In past few years, more than ten billion emojis were used on Twitter.
Emojis which are known as the Unicode graphic symbols, which are basically used as shorthand to express the
concepts and ideas of the people. For smaller number of well-known emoticons, their meanings or sentiments
are well known but there are thousands of emojis so extracting their sentiments is difficult. The Emoji Sentiment
Ranking method which is used to evaluate a sentiment mapping of emojis by using sentiment polarity such as
negative, neutral, or positive. The sentimental classification of tweets with and without emoticons are very much
different.Finally, the method also gives representation of sentiments and a better visualization in the form of a
sentimental Bar.
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This document discusses using natural language processing and machine learning techniques for sentiment analysis. It describes how sentiment analysis can be used to analyze sentiment in texts like reviews, comments, and surveys. It then outlines some challenges with sentiment analysis, such as sarcasm and complex opinions. The document proposes a clustering method to group similar short texts together to reduce sparsity. It describes training a naive Bayes classifier on the clustered texts and evaluating the method on a Twitter dataset, finding an average 1.7% improvement in precision and accuracy over baselines.
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2. The document describes a solution for sarcasm detection that uses features extracted from pretrained convolutional neural networks for sentiment analysis and emotion detection, combined with features from a baseline model.
3. Evaluation on a test set showed improved performance over the baseline models, with future work including collecting more data and exploring attention mechanisms and recurrent neural networks. Addressing sarcasm detection was presented as an important problem at the intersection of natural language processing and domain knowledge.
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Video: https://www.youtube.com/watch?v=ZCToaDgxnAs
Abstract:
People's emotions can be gleaned from their text using machine learning techniques to build models that exploit large self-labeled emotion data from social media. Further, the self-labeled emotion data can be effectively adapted to train emotion classifiers in different target domains where training data are sparse.
Emotions are both prevalent in and essential to most aspects of our lives. They influence our decision-making, affect our social relationships and shape our daily behavior. With the rapid growth of emotion-rich textual content, such as microblog posts, blog posts, and forum discussions, there is a growing need to develop algorithms and techniques for identifying people's emotions expressed in text. It has valuable implications for the studies of suicide prevention, employee productivity, well-being of people, customer relationship management, etc. However, emotion identification is quite challenging partly due to the following reasons: i) It is a multi-class classification problem that usually involves at least six basic emotions. Text describing an event or situation that causes the emotion can be devoid of explicit emotion-bearing words, thus the distinction between different emotions can be very subtle, which makes it difficult to glean emotions purely by keywords. ii) Manual annotation of emotion data by human experts is very labor-intensive and error-prone. iii) Existing labeled emotion datasets are relatively small, which fails to provide a comprehensive coverage of emotion-triggering events and situations.
Wenbo Wang dissertation defense, Kno.e.sis, Wright State University
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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.Methodology
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Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare and software development. Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations. We investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low-cost EEG, EMG, and GSR sensors. We report the results of an empirical study involving 19 subjects in a laboratory setting for emotion elicitation. We achieve state-of-the-art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models. Furthermore, we will discuss our ongoing work on the recognition of affective and cognitive states of software engineer during their daily programming tasks.
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The document discusses research into the relationship between personality traits and communication styles on WeChat. It presents literature reviewing links between traits like extraversion and communication behaviors. The study used surveys measuring Big Five personality traits and WeChat usage. Results found extraversion positively correlated with emoticon, voice chat, and status updates. Younger females used those features more frequently. The research aimed to understand how personality relates to mobile communication styles.
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Dissertation Committee: Dr. Amit Sheth, Advisor, Dr. T. K. Prasad, Dr. Keke Chen, Dr. Ingmar Weber, and Dr. Justin Martineau,
Pictures: https://www.facebook.com/Kno.e.sis/photos/?tab=album&album_id=1225911137443732
Video: https://youtu.be/tzLEUB-hggQ
Lu's Home page: http://knoesis.wright.edu/researchers/luchen/
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We first explore the task of extracting sentiment expressions from social media posts. We propose an optimization-based approach that extracts a diverse set of sentiment-bearing expressions, including formal and slang words/phrases, for a given target from an unlabeled corpus. Instead of associating the overall sentiment with a given text, this method assesses the more fine-grained target-dependent polarity of each sentiment expression. Unlike pattern-based approaches which often fail to capture the diversity of sentiment expressions due to the informal nature of language usage and writing style in social media posts, the proposed approach is capable of identifying sentiment phraseTransaction Analysis.
Transactional analysis is a theory that analyzes human behavior and communication. It was developed by Eric Berne and uses a model of ego states to explain how and why people think, act, and interact. The three ego states are the parent, adult, and child. The parent ego state is programmed by childhood experiences and role models. The adult ego state processes information objectively. The child ego state involves emotions learned from childhood. Effective communication involves complementary exchanges between people's adult ego states, while crossed or ulterior transactions are less direct.
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Iulia Pasov is a senior Data Scientist working for Sixt SE, as well as a PhD student in Artificial Intelligence and Psychology and a WiDS Ambassador. As a Data Scientist, Iulia focuses on building AI-based services meant to optimize car rental processes, as well as pipelines for automatic training and deploying of machine learning models. For her studies, she searches ways to improve learning in online knowledge building communities with the use of artificial intelligence.
Speech Overview:
Sentiment analysis is one of the most known sub-domains of Natural Language Processing (NLP), especially used in the classification of feedback messages. This talk will condense over 15 years of research on different approaches in sentiment analysis, as they evolved during time. The audience will be guided through the advantages and disadvantages of each method, in order to understand how to approach the topic given their needs.
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People’s write-ups, such as online reviews and personal micro-blogs, often reflect their emotions, ranging from just-in-the-moment sentiment to long-lasting mood. In this talk, I will first give an overview on modeling human emotions encapsulated in people’s write-ups. I will then sample two visual analytic systems that use very different methods to automatically extract and visualize human emotions for two very different purposes. The first is an interactive visual analytic system that automatically summarizes human sentiment captured in online reviews and leverages the power of a crowd to rectify the imperfections in machine sentiment analysis. The second is a timeline-based visual analytic tool that extracts and visualizes a person’s moods over time based on the person’s tweets. Finally, I will discuss the challenges of inferring human emotional DNA in general and potential research directions.
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The latest generation of emoticons which are called as emojis that is largely being used in mobile
communications as well as in social media. In past few years, more than ten billion emojis were used on Twitter.
Emojis which are known as the Unicode graphic symbols, which are basically used as shorthand to express the
concepts and ideas of the people. For smaller number of well-known emoticons, their meanings or sentiments
are well known but there are thousands of emojis so extracting their sentiments is difficult. The Emoji Sentiment
Ranking method which is used to evaluate a sentiment mapping of emojis by using sentiment polarity such as
negative, neutral, or positive. The sentimental classification of tweets with and without emoticons are very much
different.Finally, the method also gives representation of sentiments and a better visualization in the form of a
sentimental Bar.
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This document discusses using natural language processing and machine learning techniques for sentiment analysis. It describes how sentiment analysis can be used to analyze sentiment in texts like reviews, comments, and surveys. It then outlines some challenges with sentiment analysis, such as sarcasm and complex opinions. The document proposes a clustering method to group similar short texts together to reduce sparsity. It describes training a naive Bayes classifier on the clustered texts and evaluating the method on a Twitter dataset, finding an average 1.7% improvement in precision and accuracy over baselines.
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Sentiment Analysis
This document provides an overview of sentiment analysis and discusses why it is an important area of research in language technology. Sentiment analysis involves detecting positive or negative opinions in text about products, politicians, or other topics. It has many applications, such as determining how consumers feel about a new product or predicting election outcomes based on public sentiment. The document also discusses challenges in modeling affective meaning in language at the lexical level in order to perform tasks like sentiment analysis.
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Sentiment Analysis (GDSCTU).pdf
Sentiment analysis aims to identify the orientation of opinions in text and can be used to understand sentiment at various levels of granularity, from documents to specific aspects or features. It uses natural language processing and machine learning techniques to classify opinions expressed in text as positive, negative or neutral. Supervised machine learning is a common approach where a classifier is trained on manually annotated examples and used to predict the sentiment of new examples. However, sentiment analysis can also make use of lexicon-based methods and hybrid approaches.
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1. Sarcasm detection poses a challenge for sentiment analysis systems as sarcasm involves stating the opposite sentiment from what is meant. This "Achilles heel" is important to address from both business and research perspectives.
2. The document describes a solution for sarcasm detection that uses features extracted from pretrained convolutional neural networks for sentiment analysis and emotion detection, combined with features from a baseline model.
3. Evaluation on a test set showed improved performance over the baseline models, with future work including collecting more data and exploring attention mechanisms and recurrent neural networks. Addressing sarcasm detection was presented as an important problem at the intersection of natural language processing and domain knowledge.
RULE-BASED SENTIMENT ANALYSIS OF UKRAINIAN REVIEWS
This document summarizes a research paper on rule-based sentiment analysis of Ukrainian reviews. It presents the general architecture of a sentiment analysis system implemented for Ukrainian reviews using a rule-based approach. Key aspects include using a sentiment dictionary generated from an annotated Ukrainian review corpus, identifying sentiments and emotions of individual words, and defining rules to compute sentiments at the clause level based on word order and syntactic structure. The goal is to analyze sentiment at the clause level for a more nuanced understanding of opinions expressed in reviews.
PhD_Oral_Defense_Kun.ppt
Kun Zhou presents research on emotion modelling for speech generation. The document outlines three topics: (1) Seq2Seq emotion modelling to improve generalizability with limited training data, (2) Modelling emotion intensity and developing methods for its control, and (3) Mixed emotion modelling and synthesis to generate speech with combined emotions. The overall goal is to develop more expressive and controllable speech synthesis by teaching machines to better imitate human emotional speech.
Automatic Emotion Identification from Text
Video: https://www.youtube.com/watch?v=ZCToaDgxnAs
Abstract:
People's emotions can be gleaned from their text using machine learning techniques to build models that exploit large self-labeled emotion data from social media. Further, the self-labeled emotion data can be effectively adapted to train emotion classifiers in different target domains where training data are sparse.
Emotions are both prevalent in and essential to most aspects of our lives. They influence our decision-making, affect our social relationships and shape our daily behavior. With the rapid growth of emotion-rich textual content, such as microblog posts, blog posts, and forum discussions, there is a growing need to develop algorithms and techniques for identifying people's emotions expressed in text. It has valuable implications for the studies of suicide prevention, employee productivity, well-being of people, customer relationship management, etc. However, emotion identification is quite challenging partly due to the following reasons: i) It is a multi-class classification problem that usually involves at least six basic emotions. Text describing an event or situation that causes the emotion can be devoid of explicit emotion-bearing words, thus the distinction between different emotions can be very subtle, which makes it difficult to glean emotions purely by keywords. ii) Manual annotation of emotion data by human experts is very labor-intensive and error-prone. iii) Existing labeled emotion datasets are relatively small, which fails to provide a comprehensive coverage of emotion-triggering events and situations.
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.Methodology
The methodology involves a comparative analysis of linguistic differences between two soap operas from different nationalities, British and Australian. Data will be collected by transcribing randomly selected scenes from storylines of interest. Framework analysis will be used to analyze the transcripts for lexical, semantic, discourse and pragmatic differences that may be influenced by character or dialect. Relevant language and gender theories, such as those from Tannen, will also inform the analysis by providing perspectives on how gender may influence language use within the soap operas.
Emotion Detection Using Noninvasive Low-cost Sensors
Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare and software development. Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations. We investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low-cost EEG, EMG, and GSR sensors. We report the results of an empirical study involving 19 subjects in a laboratory setting for emotion elicitation. We achieve state-of-the-art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models. Furthermore, we will discuss our ongoing work on the recognition of affective and cognitive states of software engineer during their daily programming tasks.
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The document discusses research into the relationship between personality traits and communication styles on WeChat. It presents literature reviewing links between traits like extraversion and communication behaviors. The study used surveys measuring Big Five personality traits and WeChat usage. Results found extraversion positively correlated with emoticon, voice chat, and status updates. Younger females used those features more frequently. The research aimed to understand how personality relates to mobile communication styles.
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Mining and Analyzing Subjective Experiences in User-generated ContentArtificial Intelligence Institute at UofSC
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" Mining and Analyzing Subjective Experiences in User Generated Content "
By Lu Chen
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Dissertation Committee: Dr. Amit Sheth, Advisor, Dr. T. K. Prasad, Dr. Keke Chen, Dr. Ingmar Weber, and Dr. Justin Martineau,
Pictures: https://www.facebook.com/Kno.e.sis/photos/?tab=album&album_id=1225911137443732
Video: https://youtu.be/tzLEUB-hggQ
Lu's Home page: http://knoesis.wright.edu/researchers/luchen/
ABSTRACT
Web 2.0 and social media enable people to create, share and discover information instantly anywhere, anytime. A great amount of this information is subjective information -- the information about people's subjective experiences, ranging from feelings of what is happening in our daily lives to opinions on a wide variety of topics. Subjective information is useful to individuals, businesses, and government agencies to support decision making in areas such as product purchase, marketing strategy, and policy making. However, much useful subjective information is buried in ever-growing user generated data on social media platforms, it is still difficult to extract high quality subjective information and make full use of it with current technologies.
Current subjectivity and sentiment analysis research has largely focused on classifying the text polarity -- whether the expressed opinion regarding a specific topic in a given text is positive, negative, or neutral. This narrow definition does not take into account the other types of subjective information such as emotion, intent, and preference, which may prevent their exploitation from reaching its full potential. This dissertation extends the definition and introduces a unified framework for mining and analyzing diverse types of subjective information. We have identified four components of a subjective experience: an individual who holds it, a target that elicits it (e.g., a movie, or an event), a set of expressions that describe it (e.g., "excellent", "exciting"), and a classification or assessment that characterize it (e.g., positive vs. negative). Accordingly, this dissertation makes contributions in developing novel and general techniques for the tasks of identifying and extracting these components.
We first explore the task of extracting sentiment expressions from social media posts. We propose an optimization-based approach that extracts a diverse set of sentiment-bearing expressions, including formal and slang words/phrases, for a given target from an unlabeled corpus. Instead of associating the overall sentiment with a given text, this method assesses the more fine-grained target-dependent polarity of each sentiment expression. Unlike pattern-based approaches which often fail to capture the diversity of sentiment expressions due to the informal nature of language usage and writing style in social media posts, the proposed approach is capable of identifying sentiment phraseTransaction Analysis.
Transactional analysis is a theory that analyzes human behavior and communication. It was developed by Eric Berne and uses a model of ego states to explain how and why people think, act, and interact. The three ego states are the parent, adult, and child. The parent ego state is programmed by childhood experiences and role models. The adult ego state processes information objectively. The child ego state involves emotions learned from childhood. Effective communication involves complementary exchanges between people's adult ego states, while crossed or ulterior transactions are less direct.
Meetup presentation amsterdam
Melvyn Peignon is an Education Engineer at Elastic who focuses on sentiment analysis and brand monitoring. He introduces himself and his background in machine learning and data science. He then provides an overview of the Elastic Stack and its components like Elasticsearch, Kibana, Logstash, and Beats. Peignon defines sentiment analysis as being able to systematically identify sentiment in data and explains how it can be used for brand monitoring to understand customer reactions and identify influencers. He outlines the process of collecting Twitter data, analyzing sentiment with Logstash and Elasticsearch in Kibana, and how supervised and unsupervised machine learning can be applied. Peignon concludes by sharing a demo GitHub project for analyzing Twitter sentiment with the ELK stack.
Iulia Pasov, Sixt. Trends in sentiment analysis. The entire history from rule...
Iulia Pasov is a senior Data Scientist working for Sixt SE, as well as a PhD student in Artificial Intelligence and Psychology and a WiDS Ambassador. As a Data Scientist, Iulia focuses on building AI-based services meant to optimize car rental processes, as well as pipelines for automatic training and deploying of machine learning models. For her studies, she searches ways to improve learning in online knowledge building communities with the use of artificial intelligence.
Speech Overview:
Sentiment analysis is one of the most known sub-domains of Natural Language Processing (NLP), especially used in the classification of feedback messages. This talk will condense over 15 years of research on different approaches in sentiment analysis, as they evolved during time. The audience will be guided through the advantages and disadvantages of each method, in order to understand how to approach the topic given their needs.
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Sentiment Analysis Intro
- 1. SENTIMENT Presented by Hyunwoo Kim SNUAI 2018.10.02 A N A LY S I S
- 2. CONTENTS 1. Psychological Backgrounds 2. Intro to Sentiment Analysis 3. Recent paper works: • Using millions of emoji occurrences – • Interpretable emoji prediction –
- 4. Paul Ekman’s Theory of Basic Emotions
- 7. Theory of Basic Emotions 1971, New Guinea
- 8. Robert Plutchik’s Wheel of Emotions
- 9. Emotions have their own functions
- 11. Granularity of Sentiment Analysis • Document-level [coarse-grained] • overall positive / negative • Sentence-level • Subjectivity classification first! • Then sentiment classification • Or just 3-class classification: positive, negative, neutral • Aspect-level
- 12. “The voice quality of iphone is great, but its battery sucks!” Granularity of Sentiment Analysis • Aspect-level • Aspect extraction • Aspect sentiment classification Entity Aspect or Target
- 14. Sarcasm Detection “I love long walks, especially when they are taken by people who annoy me.”
- 15. Sarcasm Detection “A woman needs a man like a fish needs bicycle.”
- 16. Here are somethings you can start with!
- 17. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm 2017 EMNLP Oral Bjarke Felbo
- 18. Lack of annotated Sentiment data Co-occurring emotional expressions have been used for distant supervision ex: positive/negative emojis, hashtags #happytweet #fml #yuck #wtf #ugh Use of Twitter!
- 19. “You were always on my side when I was in trouble.” X Y
- 20. Previous works Manually specified which emotional category each emoji belongs to
- 21. Direct emoji prediction with 64 types
- 22. What’s the big deal with emoji classification? Emoji prediction is not the final goal. We’re going to use it for Transfer Learning!
- 23. 1,200,000,000 tweets plentiful to learn richer representations
- 24. 56billion raw tweets to 1.6 billion filtered tweets • English tweets • No URL’s • Tokenized on word-by-word basis • Words with 2 or more repeated characters are shortened to the same token ex: loool, looooool lol • User mentions and numbers are replaced with special tokens ex: @acl2018, @emnlp2018 is treated the same
- 25. 56billion raw tweets to 1.6 billion filtered tweets • Many tweets contain • multiple repetitions of the same emoji • multiple different emojis “I got a date tonight with her!” “I got a date tonight with her!” “I got a date tonight with her!”
- 26. Balancing the dataset • Validation &Test set’s emojis are randomly sampled equally • The remaining data is upsampled for a balancedTraining set
- 27. Model Access to earlier features helps transfer learning tanh
- 28. Transfer Learning Sentiment analysis Emotion analysis Sarcasm detection Emoji prediction 64 Encodes data into 2304dimensional vector that can be easily used for new tasks
- 29. Chain-thaw method for transfer learning “Last” “Full”
- 31. Benchmark Datasets Sentiment analysis Emotion analysis Sarcasm detection
- 33. Importance of emoji diversity Reduce to 8types of emojis
- 34. Emoji Clustering
- 37. Interpretable Emoji Prediction via Label-Wise Attention LSTMs 2018 EMNLP short Francesco Barbieri
- 38. Model Label-wise attention, more sensitive to infrequent emojis
- 39. Evaluation the average number of labels that need to be in the predictions for all true labels to be predicted Lab: #Labels A@1:Top1 A@5:Top5 CE: Coverage Error Deepmoji
- 42. Analysis Less heavily biased towards the most frequent emojis Most frequent Least frequent Rank Diff = Rank in Single Attention – Rank in Label-wise Attention
- 43. Analysis Less heavily biased towards the most frequent emojis Single Attention Model: !"❤$% Label-wise Attention Model: in 4-th rank (which is in the 10% most infrequent emojis)
- 44. Analysis a thought-provoking finding !The highest attention weights associated with emoji 1. Game 2. Boy 3. Football
- 46. Thank you !