An Empirical Study on Sentiment Analysis for Vietnamese Comparative Sentences

  • Ngô Xuân Bách
Keywords: Sentiment Analysis, Opinion Mining, Comparative Sentences, Support Vector Machines, Conditional Random Fields

Abstract

This paper presents an empirical study on sentiment analysis for Vietnamese language focusing on comparative sentences, which have different structures compared with narrative or question sentences. Given a set of evaluative Vietnamese documents, the goal of the
task consists of (1) identifying comparative sentences in the documents; (2) recognition of relations in the identified sentences; and (3) identifying the preferred entity in the comparative sentences if any. A relation describes a comparison of two entities or two sets of entities on 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 to solve
the task in which we formulate the first and the third subtasks, i.e. identifying comparative sentences and identifying the preferred entity, 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.

Published
2020-03-14

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