Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings

Existing studies learn sentiment-specific word representations to boost the performance of Twitter sentiment classification, via encoding both n-gram and distant supervised tweet sentiment information in learning process. Pioneer efforts explicitly or implicitly assume that all words within a tweet...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2018-01, Vol.275, p.2459-2466
Main Authors: Xiong, Shufeng, Lv, Hailian, Zhao, Weiting, Ji, Donghong
Format: Article
Language:English
Subjects:
Multi-level learning
Sentiment analysis
Twitter sentiment classification
Word embedding
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Summary:Existing studies learn sentiment-specific word representations to boost the performance of Twitter sentiment classification, via encoding both n-gram and distant supervised tweet sentiment information in learning process. Pioneer efforts explicitly or implicitly assume that all words within a tweet have the same sentiment polarity as that of the whole tweet, which basically ignores the word its own sentiment polarity. To alleviate this problem, we propose to learn sentiment-specific word embedding by exploiting both the lexicon resource and distant supervised information. In particular, we develop a multi-level sentiment-enriched word embedding learning method, which employs a parallel asymmetric neural network to model n-gram, word-level sentiment, and tweet-level sentiment in the learning process. Extensive experiments on standard benchmarks demonstrate our approach outperforms state-of-the-art methods.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2017.11.023