Improving Twitter Sentiment Classification Using Topic-Enriched Multi-Prototype Word Embeddings

It has been shown that learning distributed word representations is highly useful for Twitter sentiment classification. Most existing models rely on a single distributed representation for each word. This is problematic for sentiment classi- fication because words are often polysemous and each word can contain different sentiment polarities under different topics. We address this issue by learning topic-enriched multiprototype word embeddings (TMWE). In particular, we develop two neural networks which 1) learn word embeddings that better capture tweet context by incorporating topic information, and 2) learn topic-enriched multiple prototype embeddings for each word. Experiments on Twitter sentiment benchmark datasets in SemEval 2013 show that TMWE outperforms the top system with hand-crafted features, and the current best neural network model.

Authors

• Yafeng Ren
• Yue Zhang
• Meishan Zhang Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China
• Donghong Ji Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China

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