An Integrated Word Embedding-Based Dual-Task Learning Method for Sentiment Analysis

Sentiment analysis aimed to automate the task of discriminating the sentiment tendency of a textual review, which expresses a simple sentiment as positive, negative, or neutral. In general, the basic sentiment analysis solution used for feature extraction is the word embedding technique, which only focuses on the contextual or global semantic information and ignores the sentiment polarity of text. Thus, the word embedding technique leads to biased analysis results, especially for some words that have the same semantic context but an opposite sentiment. In this paper, we propose an integrated sentiment embedding method to combine context and sentiment information using a dual-task learning algorithm to perform sentiment analysis. First, we propose three sentiment language models by encoding the sentiment information of texts into word embedding based on three existing semantic models, namely, continuous bag-of-words, prediction, and log-bilinear. Next, based on semantic language models and the proposed sentiment language models, we propose a dual-task learning algorithm to generate hybrid word embedding named integrated sentiment embedding, in which the joint learning method and parallel learning method are applied to jointly process tasks. Experiments on sentence-level and document-level sentiment classification tasks demonstrate that the proposed integrated sentiment embedding has better classification performances compared with basic word embedding methods.