NDLI: Make It Possible: Multilingual Sentiment Analysis without Much Prior Knowledge

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Make It Possible: Multilingual Sentiment Analysis without Much Prior Knowledge

Content Provider	ACM Digital Library
Author	Xu, Xueke Tan, Songbo Wang, Yuanzhuo Lin, Zheng Jin, Xiaolong Cheng, Xueqi
Abstract	Sentiment analysis is a hard problem, while multilingual sentiment analysis is even harder due to the different expression styles in different languages. Although many methods for multilingual sentiment analysis have been developed in the open literature, most of them suffer from two major problems. The first is their excessive dependence on external tools or resources (e.g., Machine translation systems or bilingual dictionaries), which may not be readily obtained, especially for minority languages, The second is conflictive sentiments, i.e., The sentiment polarity of some parts of a text is inconsistent with its overall sentiment polarity. It is observed that in a product or service review there usually exist a few sentences which play a more important role in determining its sentiment polarity, as compared to others. Therefore, differentiating key sentences from trivial ones may be helpful to improve sentiment analysis. Inspired by this observation in this paper we propose a novel framework to estimate the sentiment polarity of reviews by virtue of opinion lexica and key sentences automatically extracted from unlabelled data. This framework cannot only overcome the problem of excessive dependence on external resources, but also is able to capture the overall sentiment polarity of reviews. Experimental results on realistic review datasets demonstrate that the proposed framework is effective and competitive with the representative baselines.
Starting Page	79
Ending Page	86
Page Count	8
File Format	PDF
ISBN	9781479941438
DOI	10.1109/WI-IAT.2014.83
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-11
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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