Sentiment analysis of Turkish tweets by data mining methods

Abstract

Twitter, has fast emerged as one of the most powerful social media sites which can sway opinions. Sentiment or opinion analysis has of late emerged one of the most researched and talked about subject in Natural Language Processing (NLP), thanks mainly to sites like Twitter. In the past, sentiment analysis models using Twitter data have been built to predict sales performance, rank products and merchants, public opinion polls, predict election results, political standpoints, predict box-office revenues for movies and even predict the stock market. This study proposes a general frame in R programming language to act as a gateway for the analysis of the tweets that portray emotions in a short and concentrated format. The target tweets include brief emotion descriptions and words that are not used with a proper format or grammatical structure. Majority of the work constituted in Turkish includes the data scope and the aim of preparing a data-set. There is no concrete and usable work done on Turkish Tweet sentiment analysis as a software client/web application. This study is a starting point on building up the next steps. The aim is to compare five different common machine learning methods (support vector machines, random forests, boosting, maximum entropy, and artificial neural networks) to classify Twitters sentiments. © 2019 Elsevier B.V., All rights reserved.