Knowledge-Based Sentiment Analysis and Visualization on Social Networks
- PDF / 3,558,206 Bytes
- 31 Pages / 439.37 x 666.142 pts Page_size
- 13 Downloads / 208 Views
Knowledge‑Based Sentiment Analysis and Visualization on Social Networks Julio Vizcarra1 · Kouji Kozaki1,2 · Miguel Torres Ruiz3 · Rolando Quintero3 Received: 8 February 2019 / Accepted: 18 August 2020 © Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2020
Abstract A knowledge-based methodology is proposed for sentiment analysis on social networks. The work was focused on semantic processing taking into account the content handling the public user’s opinions as excerpts of knowledge. Our approach implements knowledge graphs, similarity measures, graph theory algorithms, and a disambiguation process. The results obtained were compared with data retrieved from Twitter and users’ reviews in Amazon. We measured the efficiency of our contribution with precision, recall, and the F-measure, comparing it with the traditional method of looking up concepts in dictionaries which usually assign averages. Moreover, an analysis was carried out to find the best performance for the classification by using polarity, sentiment, and a polarity–sentiment hybrid. A study is presented for arguing the advantage of using a disambiguation process in knowledge processing. A visualization system presents the social graphs to display the sentiment information of each comment as well as the social structure and communications in the network. Keywords Sentiment analysis · Knowledge engineering · Conceptual similarity · Knowledge graph · Disambiguation
* Julio Vizcarra [email protected] 1
The Institute of Scientific and Industrial Research (ISIR) Osaka University, Mihogaoka 8‑1, Ibaraki, Osaka 567‑0047, Japan
2
Department of Engineering Informatics, Faculty of Information and Communication Engineering, Osaka Electro-Communication University, 12‑16 Hayakocho, Neyagawa‑shi, Osaka 572‑0837, Japan
3
Centro de Investigación en Computación CIC, Instituto Politécnico Nacional, UPALM-Zacatenco, CIC. Building, 07738 Mexico City, Mexico
123
Vol.:(0123456789)
New Generation Computing
Introduction Nowadays, the huge content transmitted on social networks has become a rich source of information for human understanding as well as a way of expression where the users share their sentiment status and personal opinions through comments. The sentiment identification can classify comments as positive or negative (polarity) and reveal implicit emotions such as anger, trust, sadness, etc., on certain topics or users. In addition, the sentiments presented in the opinions can be relevant in the design of custom services and social plans for public health, marketing, e-commerce, etc. Moreover, sentiment analysis has become one of the fastest growing research areas in computer science due to the explosion in computer-based sentiment studies with the availability of subjective texts on the Web [21]. Furthermore, sentiment analysis has gained attention over the years among the general public as it is currently shown in Google trends [12], hence the importance of developing a methodology for sentiment analysis on social media considering sh
Data Loading...
