Abstract
This paper describes our automatic sentiment analysis system - CIS-positive - for SemEval 2015 Task 10 “Sentiment Analysis in Twitter”, subtask B “Message Polarity Classification”. In this system, we propose to normalize the Twitter data in a way that maximizes the coverage of sentiment lexicons and minimizes distracting elements. Furthermore, we integrate the output of Convolutional Neural Networks into Support Vector Machines for the polarity classification. Our system achieves a macro F1 score of the positive and negative class of 59.57 on the SemEval 2015 test data.
Cite
CITATION STYLE
Ebert, S., Vu, N. T., & Schütze, H. (2015). CIS-positive: Combining Convolutional Neural Networks and SVMs for Sentiment Analysis in Twitter. In SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings (pp. 527–532). Association for Computational Linguistics (ACL).
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