Aspect-Based Sentiment Analysis Using BERT

摘要

Sentiment analysis has become very popular in both research and business due to the vast amount of opinionated text currently produced by Internet users. Standard sentiment analysis deals with classifying the overall sentiment of a text, but this doesn't include other important information such as towards which entity, topic or aspect within the text the sentiment is directed. Aspect-based sentiment analysis (ABSA) is a more complex task that consists in identifying both sentiments and aspects. This paper shows the potential of using the contextual word representations from the pre-trained language model BERT, together with a fine-tuning method with additional generated text, in order to solve out-of-domain ABSA and outperform previous state-of-the-art results on SemEval- 2015 (task 12, subtask 2) and SemEval- 2016 (task 5). To the best of our knowledge, no other existing work has been done on out-of-domain ABSA for aspect classification.