ECNU: Expression- And Message-level Sentiment Orientation Classification in Twitter Using Multiple Effective Features

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Abstract

Microblogging websites (such as Twitter, Facebook) are rich sources of data for opinion mining and sentiment analysis. In this paper, we describe our approaches used for sentiment analysis in twitter (task 9) organized in SemEval 2014. This task tries to determine whether the sentiment orientations conveyed by the whole tweets or pieces of tweets are positive, negative or neutral. To solve this problem, we extracted several simple and basic features considering the following aspects: surface text, syntax, sentiment score and twitter characteristic. Then we exploited these features to build a classifier using SVM algorithm. Despite the simplicity of features, our systems rank above the average.

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APA

Zhao, J., Lan, M., & Zhu, T. T. (2014). ECNU: Expression- And Message-level Sentiment Orientation Classification in Twitter Using Multiple Effective Features. In 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings (pp. 259–264). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/s14-2042

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