SemEval-2017 Task 4: Sentiment Analysis in Twitter

Rosenthal, Sara; Farra, Noura; Nakov, Preslav

doi:10.18653/v1/s17-2088

Cited by 730 publications

(550 citation statements)

References 68 publications

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“…Tables 2-4 summarize the results. For subtasks A & B, recall and F 1 scores are assessed as averaged scores according to the task organizers (see Rosenthal et al 2017 for detailed discussion on the evaluation metrics). The test data provided by SemEval-2017 Task 4 is so far one of the largest annotated sentiment analysis test datasets.…”

Section: Resultsmentioning

confidence: 99%

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CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification

Gupta

Yang

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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This paper describes a system developed for a shared sentiment analysis task and its subtasks organized by SemEval-2017. A key feature of our system is the embedded ability to detect sarcasm in order to enhance the performance of sentiment classification. We first constructed an affect-cognition-sociolinguistics sarcasm features model and trained a SVM-based classifier for detecting sarcastic expressions from general tweets. For sentiment prediction, we developed CrystalNest-a two-level cascade classification system using features combining sarcasm score derived from our sarcasm classifier, sentiment scores from Alchemy, NRC lexicon, n-grams, word embedding vectors, and part-of-speech features. We found that the sarcasm detection derived features consistently benefited key sentiment analysis evaluation metrics, in different degrees, across four subtasks A-D.

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Section: Resultsmentioning

confidence: 99%

“…In the following, we describe our approach and present supportive findings evaluated on a large set of test data provided by SemEval-2017 Task 4 (Rosenthal et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification

Gupta

Yang

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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“…To run our experiments, we used datasets provided by the task organizers (Rosenthal et al, 2017) as follows. During evaluation, we trained our models on the TRAIN set, and evaluated our different systems on the DEV set.…”

Section: Datasets and Preprocessingmentioning

confidence: 99%

“…In this paper, we present the different systems we developed as part of our participation in SemEval-2017 Task 4 on Sentiment Analysis in Twitter (Rosenthal et al, 2017). This task covers both English and Arabic languages.…”

Section: Introductionmentioning

confidence: 99%

OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model

Baly

Badaro

Hamdi

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to Twitter data that comes with additional sources of noise including dialects, misspellings, grammatical mistakes, code switching and the use of non-textual objects to express sentiments. This paper describes the "OMAM" systems that we developed as part of SemEval-2017 task 4. We evaluate English state-of-the-art methods on Arabic tweets for subtask A. As for the remaining subtasks, we introduce a topicbased approach that accounts for topic specificities by predicting topics or domains of upcoming tweets, and then using this information to predict their sentiment. Results indicate that applying the English state-of-the-art method to Arabic has achieved solid results without significant enhancements. Furthermore, the topic-based method ranked 1 st in subtasks C and E, and 2 nd in subtask D.

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“…Task 4 of SemEval 2017, Sentiment Analysis in Twitter (Rosenthal et al, 2017), has included some new subtasks this year. One of these subtasks considers user information to be also integrated in proposed systems.…”

Section: Introductionmentioning

confidence: 99%

SINAI at SemEval-2017 Task 4: User based classification

Jiménez-Zafra

Montejo-Ráez

Martin³

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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This document describes our participation in SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have only reported results for subtask B -English, determining the polarity towards a topic on a two point scale (positive or negative sentiment). Our main contribution is the integration of user information in the classification process. A SVM model is trained with Word2Vec vectors from user's tweets extracted from his timeline. The obtained results show that user-specific classifiers trained on tweets from user timeline can introduce noise as they are error prone because they are classified by an imperfect system. This encourages us to explore further integration of user information for author-based Sentiment Analysis.

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SemEval-2017 Task 4: Sentiment Analysis in Twitter

Cited by 730 publications

References 68 publications

CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification

CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification

OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model

SINAI at SemEval-2017 Task 4: User based classification

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