Sentiment classification of social media data for telecommunication companies in Turkey

Ismail Iseri, Ömer Faruk Atasoy, Harun Alçiçek
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引用次数: 3

Abstract

In recent years, the huge amount of data that has emerged in the world as a result of a very rapid increase in digital data has brought about the storage, processing and analysis of data into business intelligence solutions. One of the biggest sources of this large-scale data that has emerged and continues to grow is the data produced from social media tools. The average daily amount generated by Twitter social media is around 7 terabytes and this value increases day by day. Twitter is a social media tool that users express their feelings and thoughts about commercial companies, about social events, or sharing in any subject. In this study, a sentiment classification study was carried out on the tweets that were taken in the two selected date ranges of two major telecommunication companies serving in Turkey. The feature vectors obtained by two different feature extraction methods from the tweets where the users shared are classified as “positive / negative” by using KNN classifier. In this way, Twitter users' thoughts and satisfaction about three telecommunication companies in Turkey were determined in two selected dates.
土耳其电信公司社交媒体数据的情感分类
近年来,由于数字数据的快速增长,世界上出现了大量的数据,这使得数据的存储、处理和分析成为商业智能解决方案。这种已经出现并持续增长的大规模数据的最大来源之一是社交媒体工具产生的数据。Twitter社交媒体每天产生的平均量约为7tb,并且这个值每天都在增加。Twitter是一种社交媒体工具,用户可以在这里表达他们对商业公司、社会事件或任何主题的分享的感受和想法。在本研究中,对在土耳其两家主要电信公司的两个选定日期范围内拍摄的推文进行了情绪分类研究。通过两种不同的特征提取方法对用户所分享的推文进行特征向量的提取,利用KNN分类器将其分类为“正/负”。通过这种方式,Twitter用户对土耳其三家电信公司的想法和满意度在两个选定的日期被确定。
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