A NLP framework based on meaningful latent-topic detection and sentiment analysis via fuzzy lattice reasoning on youtube comments

• Published in: Multimedia tools and applications Vol. 80; no. 3; pp. 4155 - 4181
• Main Authors: Jelodar, Hamed, Wang, Yongli, Rabbani, Mahdi, Ahmadi, Sajjad Bagheri Baba, Boukela, Lynda, Zhao, Ruxin, Larik, Raja Sohail Ahmed
• Format: Journal Article
• Language: English
• Published: New York Springer US 2021; Springer Nature B.V
• Subjects: Algorithms; Computer Communication Networks; Computer Science; Content analysis; Data mining; Data Structures and Information Theory; Digital media; Feature extraction; Fuzzy logic; Multimedia Information Systems; Norms; Reasoning; Sentiment analysis; Social networks; Special Purpose and Application-Based Systems; LDA; Natural language processing; Social media; Topic model; YouTube
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09755-z

Social media platforms such as Twitter, Facebook, and YouTube have unique architecture, norms, and culture. These platforms are valuable sources of people’s opinions which should be examined for knowledge discovery and user behavior analysis. This paper proposed a novel content analysis to examine user reviews or movie comments on YouTube. In fact, the proposed hybrid framework is based on semantic and sentiment aspects using fuzzy lattice reasoning to meaningful latent-topic detection and utilizing sentiment analysis of user comments of the Oscar-nominated movie trailers on YouTube. Based on the word vector feature, classification algorithms are employed to detect the comments’ sentiment level. The results of this study suggest that the hybrid framework could be effective to extract features associated and latent topics with sentiment valence on user comments. In addition, NLP methods can have an impressive role for exploring the relationship between user opinion and Oscar movies comments on YouTube.