Joint Embedding of Words and Labels for Sentiment Classification
Yingwei Sheng, Takashi Inui · 2020
Due to the fast growth of social networks, sentiment analysis on the web has been a popular research topic. Recently, word embedding-based sentiment analysis methods have reached outstanding performance compared to traditional methods. However, word embeddings always ignore information from dataset's labels. Inspired by LEAM model proposed by Wang [1], we propose a method that jointly learns information of words and sentiment labels, which can improve the performance of the label embedding model. We defined a set of sentiment lexicons and used it to represent sentiment labels in the proposed method. We finally conducted experiments on Yelp dataset, which reached 64.99% accuracy.