Three Convolutional Neural Network-based models for learning Sentiment Word Vectors towards sentiment analysis

Abstract

With the development of deep learning, word vectors (i.e., word embeddings) have been extensively explored and applied to many Natural Language Processing tasks (e.g., parsing, Named Entity Recognition, etc). However, the semantic word vectors learned from context have insufficient sentiment information for performing sentiment analysis at different text levels. In this work, we present three Convolutional Neural Network (CNN)-based models to learn sentiment word vectors (SWV), which integrate sentiment information with semantic and syntactic information into word representations in three different strategies. Experimental results on benchmark datasets showed that sentiment word vectors are able to capture both sentiment and semantic information and outperform semantic word vectors for word-level and sentence-level sentiment analysis. Moreover, in combination with traditional NLP features, the sentiment word vectors achieve the best performance so far.