Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text

Munezero, Myriam; Montero, Calkin Suero; Sutinen, Erkki; Pajunen, John

doi:10.1109/taffc.2014.2317187

Cited by 274 publications

(139 citation statements)

References 43 publications

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“…For a review of the fundaments of emotion and sentiment and the differences between these concepts, we refer the reader to Munezero et al (2014). For sentiment analysis, a large number of annotated data sets exists.…”

Section: Background and Related Workmentioning

confidence: 99%

Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus

Schuff¹,

Barnes²,

Mohme³

et al. 2017

Proceedings of the 8th Workshop on Computational Approaches To Subjectivity, Sentiment and Social Media Analysis

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There is a rich variety of data sets for sentiment analysis (viz., polarity and subjectivity classification). For the more challenging task of detecting discrete emotions following the definitions of Ekman and Plutchik, however, there are much fewer data sets, and notably no resources for the social media domain. This paper contributes to closing this gap by extending the SemEval 2016 stance and sentiment dataset with emotion annotation. We (a) analyse annotation reliability and annotation merging; (b) investigate the relation between emotion annotation and the other annotation layers (stance, sentiment); (c) report modelling results as a baseline for future work.

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Section: Background and Related Workmentioning

confidence: 99%

Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus

Schuff¹,

Barnes²,

Mohme³

et al. 2017

Proceedings of the 8th Workshop on Computational Approaches To Subjectivity, Sentiment and Social Media Analysis

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show abstract

“…• Sentiment features: Though sentiment and emotion are different by definition [42], prior research in emotion classification [32] explored the role of sentiment knolwedge offered by lexicons. Similary we define two integer valued features, to capture for the number of positive words and negative words observed in a document.…”

Section: Baseline Emotion Featuresmentioning

confidence: 99%

Lexicon based feature extraction for emotion text classification

Bandhakavi

Wiratunga

Deepak

et al. 2017

Pattern Recognition Letters

121

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“…In NLP research, affects, feelings, emotions and sentiments are often considered similarly and used interchangeably [25]. In general, moods differ from emotions, feelings, or sentiments in that they are less specific, less intense, and less likely to be triggered by a particular stimulus or event [24,26].…”

Section: Sentiment Analysis and Emotion Recognition In Social Networkmentioning

confidence: 99%

A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics

et al. 2017

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Analyzing people's opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for identifying the psychological characteristics of social network users. However, most studies focus on identifying the sentiments carried in the micro-blogging text and there is no ideal calculation method for users' real emotional states. In this study, the Profile of Mood State (POMS) is used to characterize users' real mood states and a regression model is built based on cyber psychometrics and a multitask method. Features of users' online behavior are selected through structured statistics and unstructured text. Results of the correlation analysis of different features demonstrate that users' real mood states are not only characterized by the messages expressed through texts, but also correlate with statistical features of online behavior. The sentiment-related features in different timespans indicate different correlations with the real mood state. The comparison among various regression algorithms suggests that the multitask learning method outperforms other algorithms in root-mean-square error and error ratio. Therefore, this cyber psychometrics method based on multitask learning that integrates structural features and temporal emotional information could effectively obtain users' real mood states and could be applied in further psychological measurements and predictions.

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Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text

Cited by 274 publications

References 43 publications

Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus

Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus

Lexicon based feature extraction for emotion text classification

A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics

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