Abstract—This paper focuses on mining the value of customers emotional behaviors using Twitter data. Using Apache Flume to collect tweets data from Twitter. 192,390 tweets were collected. Then the natural language processing (NLP) technology has been used to divide and filter tweets for customers’ emotional behaviors analysis. We picked five main hot topics among these tweets. Choose one of the hot topics HUAWEI honors 9 for sentiment analysis (SA). Compared with Native Bayes, Maximum Entropy Classifier. Decision Tree Classifier is the most effective classification method for our data sets. According to our experiment, the result shows that 45% of customers are satisfied with HUAWEI honors 9, but there is still having 36% of customers unsatisfied with it. Specifically, in the field of battery, game and stand-by power consumption, it needs a great of an improvement.
Index Terms—Customers’ emotional behavior, twitter, big data, sentiment analysis.
Xiaorui Shao is with Information collaboration Engineering department, Pukyong national university, Korea(e-mail: sxrpknu@qq.com). Chang-Soo Kim is with the IT convergence and application engineering Department of Pukyong national university, Korea (e-mail:cskim@pknu.ac.kr). Kwak Dong Ryul is with PKNU Department of IT Convergence and Application Engineering (e-mail:destroy247@naver.com).
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Cite:Xiaorui Shao, Chang-So Kim, and Kwak Dong Ryul, "A Study on Customers’ Sentiment Analysis Based on Big Data Using Twitter Data," International Journal of Computer Theory and Engineering vol. 11, no. 1, pp. 11-14, 2019.
