Proceedings of the 8th Workshop on Computational Approaches To Subjectivity, Sentiment and Social Media Analysis 2017
DOI: 10.18653/v1/w17-5203
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Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus

Abstract: 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) investi… Show more

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Cited by 71 publications
(70 citation statements)
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References 38 publications
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“…We compare the performance of our proposed system with the state-of-the-art systems of SemEval 2016 Task 6 and the systems of [16]. Experimental results show that the proposed system improves the existing state-of-the-art systems for sentiment and emotion analysis.…”
Section: Results and Analysismentioning
confidence: 97%
See 1 more Smart Citation
“…We compare the performance of our proposed system with the state-of-the-art systems of SemEval 2016 Task 6 and the systems of [16]. Experimental results show that the proposed system improves the existing state-of-the-art systems for sentiment and emotion analysis.…”
Section: Results and Analysismentioning
confidence: 97%
“…Sigmoid cross-entropy was used as the loss function. F1-score was reported for the sentiment analysis [8] and precision, recall and F1-score were used as the evaluation metric for emotion analysis [16]. Therefore, we report the F1-score for sentiment and precision, recall and F1-score for emotion analysis.…”
Section: Implementation Detailsmentioning
confidence: 99%
“…The statistics of annotated tweets are presented in Table II. [13]. We have prepared two datasets of annotated tweets considering majority votes and all votes.…”
Section: Dataset Collection and Annotationmentioning
confidence: 99%
“…The first manually-annotated corpus of tweets for emotion analysis made publicly available was provided by [19], followed by a larger set with a focus on emotion intensity prediction [20]. The corpus by [21] provides multiple annotations of each instance and analyzes interactions between classes. It is a re-annotation of a SemEval corpus for stance detection [22].…”
Section: Background and Related Work A Emotion Analysismentioning
confidence: 99%