TurkiS: A Turkish Sentiment Analyzer Using Domain-specific Automatic Labelled Dataset
AbstractA preliminary task of sentiment analysis aims to detect polarities of a text either positive or negative. These texts vary from movie reviews to customer comments on electronic devices. The polarity detector trained in one domain may not achieve remarkable results in another domain. In this study, we provide a training and test dataset generator for domain-specific sentiment analysis in which machine learning methods can be trained without any human labor. To do it, we extract comments and polarity scores from a popular e-commerce website for electronic devices in Turkey. Also, we translate a well-known sentiment lexicon into Turkish and use this lexicon in a lexicon-based polarity detection. We compare well-known machine learning methods trained by automatically labeled dataset and lexicon-based method for Turkish texts in the electronics domain. The experimental setup is conducted by the generated evaluation dataset for the e-commerce domain. The test set contains 30% randomly selected from the generated dataset. Our lexicon-based method has achieved 0.62 F1 score and other three supervised learning methods achieved the best results. Further, logistic regression gets the highest score when it uses count vectorizer as a feature extraction mechanism.
Copyright (c) 2019 International Journal of Intelligent Systems and Applications in Engineering
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.