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

ACL ID S14-2042
Title ECNU: Expression- and Message-level Sentiment Orientation Classification in Twitter Using Multiple Effective Features
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

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 ex- tracted 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 fea- tures, our systems rank above the average.