Mining User Opinion Influences on Twitter Social Network: Find that Friend who Leads your Opinion Using Bayesian Method and a New Emotional PageRank Algorithm
| Autor: | Walid El Ayeb, Zied Choukair, Armielle Noulapeu Ngaffo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Social network
Computer science business.industry Bayesian probability Sentiment analysis Service discovery 02 engineering and technology 01 natural sciences law.invention World Wide Web 010104 statistics & probability PageRank law 020204 information systems 0202 electrical engineering electronic engineering information engineering 0101 mathematics business Pagerank algorithm |
| Zdroj: | IWCMC |
| DOI: | 10.1109/iwcmc.2019.8766571 |
| Popis: | With about 326 million[1] monthly active users and many millions of tweets sent per day, Twitter is undoubtedly one of the social networks most requested[2] by users sharing opinions, and feelings about trends, events… As a result, how users influence their opinions mutually constitute a hot issue for researchers. Indeed, the study and the estimation of the opinion influence observed between Twitter users constitutes a rich opportunity for the adjustment of services/products offered involved in the service discovery process. In this paper we propose an approach to determine the target user's Twitter friends from whom the target user opinion is influenced by. Our model is based on opinion mining of retweets and target user's Favorites markings from which we estimate the opinion influence using the Bayesian method combined with our EPR (Emotional PageRank) algorithm. The results obtained highlight our contribution compared to the standard PR (PageRank) algorithm. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|