Mining Vietnamese Comparative Sentences for Sentiment Analysis

This paper presents an empirical study on mining comparative sentences for Vietnamese language. Given a set of evaluative Vietnamese documents, the goal of comparative sentence mining consists of (1) identifying comparative sentences in the documents, and (2) recognition of relations in identified comparative sentences. A relation describes a comparison of two entities or two sets of entities in some features or aspects in the sentence. Such information is needed for sentiment analysis in comparative sentences, which is very useful not only for customers in choosing products but also for manufacturers in producing and marketing. We present a general framework for mining Vietnamese comparative sentences in which we formulate the first subtask, i.e. Identifying comparative sentences, as a classification problem, and the second subtask, i.e. Recognition of relations, as a sequence learning problem. We introduce a new corpus for the task in Vietnamese and conduct a series of experiments on that corpus to investigate the task in both linguistic and modeling aspects. Our work provides promising results for further research on this interesting task.