Improving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings

TL;DR

This paper introduces a multi-level sentiment-enriched word embedding approach for Twitter sentiment classification, leveraging lexicon and supervised data to improve word representations and classification accuracy.

Contribution

It proposes a novel multi-level embedding learning method using parallel asymmetric neural networks to model n-gram, word, and tweet sentiment levels.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively captures word-level sentiment polarity

Enhances Twitter sentiment classification accuracy

Abstract

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.