Cover page and Table of Contents: PDF (size: 1227KB)
Full Text (PDF, 1227KB), PP.19-29
Views: 0 Downloads: 0
Sentiment Analysis, RSS feeds, Sports News, Polarity, Social Media
With the advent of online social media, such as articles, websites, blogs, messages, posts, news channels, and by and large web content has drastically changed the way individuals take a glimpse at different things around them. Today, it's an everyday practice for some individuals to read the news on the web. Sentiment analysis (also called opinion mining) alludes to the utilization of natural language processing, content investigation, and computational linguistics to distinguish and separate subjective data in source materials. Sentiment analysis is broadly applied to online reviews, news feeds and social networking for a wide variety of applications, ranging from marketing to client services. Sentiment analysis emphasizes on the classification of textual data into positive, negative and neutral categories. This research is an endeavor to the case study that calculates news polarity or emotions on different sports feeds which may influence changes in sports news development patterns. The interest of this approach is to generate various text analytics that computes feelings from all pertinent ongoing sports news accessible out in the public domain. The significance and application value of sentiment analysis of RSS feeds in this study is to distinguish between positive feeds and negative feeds on sports that could affect readers or users minds in order to improve RSS feeds messaging broadcast among folks. The methodology utilizes the sentiment analysis techniques using two different online open-source sentiment analysis tools in Rich Site Summary (RSS) news feeds that have an influence on sports-related broadcast esteems.
Khalid Mahboob, Fayyaz Ali, Hafsa Nizami, "Sentiment Analysis of RSS Feeds on Sports News – A Case Study", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.12, pp.19-29, 2019. DOI:10.5815/ijitcs.2019.12.02
J.A. Morente-Molinera, et al, “Analysing Discussions in Social Networks Using Group Decision Making Methods and Sentiment Analysis.” Information Sciences, vol. 447, 2018, pp. 157–168, doi:10.1016/j.ins.2018.03.020.
https://en.wikipedia.org/wiki/Sentiment_analysis [Aug. 29, 2018].
https://en.wikipedia.org/wiki/RSS [Oct. 3, 2018].
Y. Haribhakta, K.S. Doddi, “Categorization of News Articles using Sentiment Analysis.” International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 2017 Sept&Oct; 2(5): 52–60.
M. O’Shea, et al, “Mining and Visualising Information from RSS Feeds: a Case Study.” International Journal of Web Information Systems, vol. 7, no. 2, 2011, pp. 105–129, doi:10.1108/17440081111141763.
K.D. Gaikwad, et al, “Opinion Mining and Sentiment Analysis Techniques: A Recent Survey.” International Journal of Engineering Sciences & Research Technology 2016 Dec; 5(12): 1003–6.
http://www.rss-specifications.com/rss-faqs.htm [Oct. 4, 2018].
S.V.S. Bharathi, A. Geetha, “Sentiment Analysis for Online Stock Market News using RSS Feeds.” International Journal of Current Engineering and Scientific Research. 2017; 4(4): 58–63.
A. D’Andrea, F. Ferri, P. Grifoni, T. Guzzo, 9. “Approaches, Tools and Applications for Sentiment Analysis Implementation.” International Journal of Computer Applications. 2015 Sep; 125(3): 26–33.
R. Kent Wills, “Efficient Sentiment Analysis of Feeds for Rapid User Information Gain.” : 1–4.
A.S. Dulange, R.B. Kulkarni, S.S. Ambarkar, “Opinion Mining From Blogosphere for Analysis of Social Networking.” International Journal of Soft Computing and Engineering. 2013 Sep; 3(4): 141–146.
J.M. Ruiz-Martíne, R. Valencia-García, F. García-Sánchez, “Semantic-Based Sentiment analysis in financial news.”:38–51.
S. Bharathi, A. Geetha, “Sentiment Analysis for Effective Stock Market Prediction.” International Journal of Intelligent Engineering and Systems. 2017; 10(3): 146–54., doi: 10.22266/ijies2017.0630.16.
E. Haddi, X. Liu, Y. Shi, “The Role of Text Pre-processing in Sentiment Analysis.” Procedia Computer Science. 2013; 26–32., doi:10.1016/j.procs.2013.05.005.
https://www.w3schools.com/xml/xml_rss.asp [Oct. 5, 2018].
https://en.wikipedia.org/wiki/News_aggregator [Oct. 5, 2018].
https://voyant-tools.org/ [Oct. 7, 2018].
https://tribune.com.pk/story/ [Sep. 30, 2018].
https://www.samaa.tv/sports/2018 [Sep. 30, 2018].
https://www.geo.tv/latest/ [cited 2018 Sep 30].
https://www.dawn.com/news [cited 2018 Sep 30].
https://www.thenews.com.pk/print/ [cited 2018 Sep 30].
http://liwc.wpengine.com/ [cited 2018 Oct 10].
https://www.newscientist.com/ [cited 2018 Oct 16].
https://www.ukessays.com/essays/internet/ [cited 2018 Oct 16].