Recursive Autoencoder with HowNet Lexicon for Sentence-Level Sentiment Analysis
Xianghua Fu, Yingying Xu · 2015
Semantic word representations have been very useful but usually ignore the syntactic relationship. In the task of sentiment analysis, compositional vector representations require more structure information from natural language text and richer supervised training for more accuracy predictions. However, labeled data are generally expensive to acquire in reality. To remedy this, we propose a new method that train our model based on fully labeled parse tree using supervised learning without manual annotation. Our method not only significantly reduces the burden of manual labeling, but also allows the compositionality to capture syntactic and semantic information jointly. We show the effectiveness of this model on the task of sentence-level sentiment classification and conduct preliminary experiments to investigate its performance. Lastly, it can accurately predict the sentiment distribution and outperforms other approaches.