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http://dx.doi.org/10.13088/jiis.2016.22.3.091

A Method of Analyzing Sentiment Polarity of Multilingual Social Media: A Case of Korean-Chinese Languages  

Cui, Meina (School of Management, Kyung Hee University)
Jin, Yoonsun (School of Management, Kyung Hee University)
Kwon, Ohbyung (School of Management, Kyung Hee University)
Publication Information
Journal of Intelligence and Information Systems / v.22, no.3, 2016 , pp. 91-111 More about this Journal
Abstract
It is crucial for the social media based marketing practices to perform sentiment analyze the unstructured data written by the potential consumers of their products and services. In particular, when it comes to the companies which are interested in global business, the companies must collect and analyze the data from the social media of multinational settings (e.g. Youtube, Instagram, etc.). In this case, since the texts are multilingual, they usually translate the sentences into a certain target language before conducting sentiment analysis. However, due to the lack of cultural differences and highly qualified data dictionary, translated sentences suffer from misunderstanding the true meaning. These result in decreasing the quality of sentiment analysis. Hence, this study aims to propose a method to perform a multilingual sentiment analysis, focusing on Korean-Chinese cases, while avoiding language translations. To show the feasibility of the idea proposed in this paper, we compare the performance of the proposed method with those of the legacy methods which adopt language translators. The results suggest that our method outperforms in terms of RMSE, and can be applied by the global business institutions.
Keywords
Sentiment Analysis; Multilingual Data Analysis; Social Media Marketing; Text Mining; SentiWordNet;
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Times Cited By KSCI : 1  (Citation Analysis)
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