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Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings.
- Source :
-
Neurocomputing . Jan2018, Vol. 275, p2459-2466. 8p. - Publication Year :
- 2018
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SENTIMENT analysis
*VOCABULARY
*EMBEDDINGS (Mathematics)
*SUPERVISED learning
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 275
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 126959303
- Full Text :
- https://doi.org/10.1016/j.neucom.2017.11.023