Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/32399
Title: Sentiment Analysis of Turkish Twitter Data
Authors: Shehu, Harisu Abdullahi
Tokat, Sezai
Sharif, MH
Uyaver, S
Keywords: Artificial Intelligence; Classifier; Machine Learning; Sentiment
Analysis; Turkish; Twitter
Publisher: AMER INST PHYSICS
Abstract: In this paper, we present a mechanism to predict the sentiment on Turkish tweets by adopting two methods based on polarity lexicon (PL) and artificial intelligence (AI). The method of PL introduces a dictionary of words and matches the words to those in the harvested tweets. The tweets are then classified to be either positive, negative, or neutral based on the result found after matching them to the words in the dictionary. The method of AI uses support vector machine (SVM) and random forest (RF) classifiers to classify the tweets as either positive, negative or neutral. Experimental results show that SVM performs better on stemmed data by achieving an accuracy of 76%, whereas RF performs better on raw data with an accuracy of 88%. The performance of PL method increases continuously from 45% to 57% as data are being modified from a raw data to a stemmed data.
URI: https://hdl.handle.net/11499/32399
https://doi.org/10.1063/1.5136197
ISSN: 0094-243X
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record



CORE Recommender

SCOPUSTM   
Citations

10
checked on Oct 13, 2024

WEB OF SCIENCETM
Citations

2
checked on Oct 20, 2024

Page view(s)

64
checked on Aug 24, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.