Improving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings
This work improves sentiment analysis for social media applications, but it is incremental as it builds on existing methods by incorporating word-level sentiment information.
The paper tackled the problem of Twitter sentiment classification by addressing the assumption that all words in a tweet share the same sentiment polarity, proposing a method that learns sentiment-specific word embeddings using lexicon resources and distant supervision. The result is that their approach outperforms state-of-the-art methods on standard benchmarks.
Most of existing work learn sentiment-specific word representation for improving Twitter sentiment classification, which encoded both n-gram and distant supervised tweet sentiment information in learning process. They assume all words within a tweet have the same sentiment polarity as the whole tweet, which ignores the word its own sentiment polarity. To address this problem, we propose to learn sentiment-specific word embedding by exploiting both lexicon resource and distant supervised information. We develop a multi-level sentiment-enriched word embedding learning method, which uses parallel asymmetric neural network to model n-gram, word level sentiment and tweet level sentiment in learning process. Experiments on standard benchmarks show our approach outperforms state-of-the-art methods.