Lexicon Integrated CNN Models with Attention for Sentiment Analysis
Bonggun Shin, Timothy J. Lee, Jinho D. Choi · 2017
With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting.This paper introduces a novel approach to sentiment analysis that integrates lexicon embeddings and an attention mechanism into Convolutional Neural Networks.Our approach performs separate convolutions for word and lexicon embeddings and provides a global view of the document using attention.Our models are experimented on both the SemEval'16 Task 4 dataset and the Stanford Sentiment Treebank and show comparative or better results against the existing state-of-the-art systems.Our analysis shows that lexicon embeddings allow building high-performing models with much smaller word embeddings, and the attention mechanism effectively dims out noisy words for sentiment analysis.