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Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings - 2017

Towards Twitter Sentiment Classification By Multi-Level Sentiment-Enriched Word Embeddings

Research Area:  Machine Learning


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.


Author(s) Name:  Shufeng Xiong, Hailian Lv, Weiting Zhao, Donghong Jib

Journal name:  Neurocomputing

Conferrence name:  

Publisher name:  ELSEVIER


Volume Information:  Volume 275, 31 January 2018, Pages 2459-2466