Lexicon Construction for Sentiment Analysis in Media Quality Rating
Article Sidebar
Main Article Content
Abstract
The objective of this study, " Lexicon Construction for Sentiment Analysis in Media Quality Rating," was to develop a lexicon for sentiment analysis system to measure the quality of media programs focusing on television news programs and child, youth, and family programs. The research process consists of 3 steps: 1) generating keywords, 2) analyzing and improving keywords, and 3) applying keywords to test the quality of media programs. A total of 25 media programs were examined to measure the quality, divided into 15 programs for television news and 10 programs for child, youth, and family.
During the analysis and improvement of keywords, issues related to word segmentation, such as incorrect keyword matching and incomplete keyword matching, were identified. These were resolved by adjusting the keywords to be more relevant to various aspects of media quality, and filtering out keywords that were not relevant to media quality rating. For issues related to topic classification, such as identifying positive keywords in negative sentences or sentences with meaning not related to the considered quality aspect, "human" was used to verify the text, particularly for expressions of sarcasm and comments on news content without expressing opinions on the quality of the news.
The results presented a lexicon for sentiment analysis system which contained 15,383 keywords dividing into positive and negative sentiment. The keywords were categorized into four dimensions: 1) content, 2) moderator, 3) presentation technique, and 4) other dimensions. Based on the results of testing the quality of media presented to society through two types of programs, news and programs for children, youth, and families, it was found that both types of programs had the most comments categorized under the content dimension. When considering positive and negative aspects, news programs had 25.86% negative comments, while programs for children, youth, and families had only 0.37% negative comments. The constructed lexicon's keywords were applied to test the quality of media programs, and the results were evaluated with an F-score of 87%. The findings of this study can be utilized in designing and developing tools to measure the quality of media across different dimensions leading to improved media content for society and fostering innovation and knowledge in the field of media.
Article Details
References
มนัสวินี จันทะเลิศ. (2548). เกณฑ์ในการประเมินคุณภาพรายการเพื่อเด็กทางโทรทัศน์ (วิทยานิพนธ์ปริญญามหาบัณฑิต). กรุงเทพฯ: คณะนิเทศศาสตร์ จุฬาลงกรณ์มหาวิทยาลัย
Bowornlertsutee, P., & Paireekreng, W. (2022). The Model of Sentiment Analysis for Classifying the Online Shopping Reviews. Journal of Engineering and Digital Technology (JEDT), 10(1), 71-79. (in Thai)
Ding, Y., Korolov, R., Wallace, W., & Wang, X. (2021). How are sentiments on autonomous vehicles influenced? An analysis using Twitter feeds. Transportation Research Part C: Emerging Technologies, 131.
Drus, Z., & Khalid, H. (2019). Sentiment Analysis in Social Media and Its Application: Systematic Literature Review. Procedia Computer Science, 161, 707-714.
Kiawkaew, T.-a. (2018). Polite Response to Customer Feedback Suggestion for Thai Texts (Master of Science in Information Technology). Prince of Songkla University. (in Thai)
Kumar, A., Beri, T., & Sobti, T. (2021). A Survey of Sentiment Analysis and Opinion Mining. Paper presented at the Emerging Technologies in Data Mining and Information Security, Singapore.
McQuail, D. (1997). Audience Analysis.
Melton, C. A., Olusanya, O. A., Ammar, N., & Shaban-Nejad, A. (2021). Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence. Journal of Infection and Public Health, 14(10), 1505-1512.
Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking: " O'Reilly Media, Inc.".
Rosengren, K. E., Carlsson, M., & Ta ̈gerud, Y. (1996). Quality in programming: Views from the north. In S. Ishikawa (Ed.), Quality assessment of television (pp. 3-48). Luton, UK: John Libbey Media.
Shamir, Jacob. (2007). Quality Assessment of Television Programs in Israel: Can Viewers Recognize Production Value?, Journal of Applied Communication Research, 35(3), 320-341.
Wang, G., Chen, G., & Chu, Y. (2018). A new random subspace method incorporating sentiment and textual information for financial distress prediction. Electronic Commerce Research and Applications, 29, 30-49.
Xu, Q. A., Chang, V., & Jayne, C. (2022). A systematic review of social media-based sentiment analysis: Emerging trends and challenges. Decision Analytics Journal, 3, 100073.
Yang, H.-L., & Chao, A. F. Y. (2018). Sentiment annotations for reviews: an information quality perspective. Online Information Review, 42(5), 579-594.
Žitnik, S., Blagus, N., & Bajec, M. (2022). Target-level sentiment analysis for news articles. Knowledge-Based Systems, 249.
