Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/37547
Title: Sentiment analysis of turkish twitter data using polarity lexicon and artificial intelligence
Authors: Shehu, H.A.
Haidar Sharif, M.
Uyaver, S.
Tokat, Sezai
Ramadan, R.A.
Keywords: Artificial intelligence
Entropy
Sentiment
SVM
Turkish
Twitter
Application programming interfaces (API)
Decision trees
Sentiment analysis
Social networking (online)
Support vector machines
Combined classifiers
Internet based
Turkishs
Maximum entropy methods
Publisher: Springer Science and Business Media Deutschland GmbH
Abstract: Sentiment analysis is a process of computationally detecting and classifying opinions written in a piece of writer’s text. It determines the writer’s impression as achromatic or negative or positive. Sentiment analysis became unsophisticated due to the invention of Internet-based societal media. At present, usually people express their opinions by dint of Twitter. Henceforth, Twitter is a fascinating medium for researchers to perform data analysis. In this paper, we address a handful of methods to prognosticate the sentiment on Turkish tweets by taking up polarity lexicon as well as artificial intelligence. The polarity lexicon method uses a dictionary of words and accords with the words among the harvested tweets. The tweets are then grouped into either positive tweets or negative tweets or neutral tweets. The methods of artificial intelligence use either individually or combined classifiers e.g., support vector machine (SVM), random forest (RF), maximum entropy (ME), and decision tree (DT) for categorizing positive tweets, negative tweets, and neutral tweets. To analyze sentiment, a total of 13000 Turkish tweets are collected from Twitter with the help of Twitter’s application programming interface (API). Experimental results show that the mean performance of our proposed methods is greater than 72%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.
URI: https://hdl.handle.net/11499/37547
https://doi.org/10.1007/978-3-030-60036-5_8
ISBN: 18678211 (ISSN)
9783030600358
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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