A transformation method for aspect–based sentiment analysis

Abstract

Along with the explosion of user reviews on the internet, sentiment analysis has become one of the trending research topics in the field of natural language processing. In the last five years, many shared tasks were organized to keep track of the progress of sentiment analysis for various languages. In the Fifth International Workshop on Vietnamese Language and Speech Processing (VLSP 2018), the Sentiment Analysis shared task was the first evaluation campaign for the Vietnamese language. In this paper, we describe our system for this shared task. We employ a supervised learning method based on the Support Vector Machine classifiers combined with a variety of features. We obtained the F1-score of 61% for both domains, which was ranked highest in the shared task. For the aspect detection subtask, our method achieved 77% and 69% in F1-score for the restaurant domain and the hotel domain respectively.