Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/3389
Title: Sentiment Analysis about Turkish TV Series with Web Scraping
Authors: Ergül Aydın, Zeliha
Keywords: classification
sentiment analysis
text mining
Turkish
tv series
web scraping
Learning algorithms
Nearest neighbor search
Sentiment analysis
Support vector machines
Automatic extraction
Broadcast TV
Machine learning algorithms
Media platforms
Sentiment analysis
Text-mining
Turkishs
Tv series
Vector-modeling
Web scrapings
Digital storage
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: A person's preferences for a product or service are influenced by other people's opinions about that product or service. One of the sectors where this situation is most common is digital media platforms that broadcast TV series. Therefore, the automatic extraction of other people's feelings and opinions from comments about TV series on digital media platforms by sentiment analysis allows users to have information and judgment about series easily. This study aims to conduct sentiment analysis on the Turkish Tv Series with machine learning algorithms. First, we collect comments about digital media platform TV series from a user-generated website, namely Ekşi Sözlük, with web scraping techniques and apply preprocessing to this data. Then, we conduct sentiment analysis with Support Vector Machine (SVM), Logistic Regression (LR), K-nearest Neighbors machine learning techniques with Bag of Words (BoW), Term Frequency Inverse Document Frequency (TF-IDF), Word2vec vector models. Our results show that the SVM classifier trained on the TF -IDF vector model gives the highest prediction macro averaged F -score with 0.631. © 2022 IEEE.
Description: 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022 -- 9 June 2022 through 11 June 2022 -- -- 180434
URI: https://doi.org/10.1109/HORA55278.2022.9800089
https://hdl.handle.net/20.500.13087/3389
ISBN: 9781665468350
Appears in Collections:Endüstri Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu

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