Name
Abstract | ||
|---|---|---|
Social media services have become popular. Especially, Twitter is accumulating and distributing vast amounts of information for its numerous users. One feature of Twitter is that a user follows other users, who can obtain information that is tweeted from followees. However, it is difficult for a user to find promising followees because there are so many Twitter users. Therefore, numerous studies have tackled the recommendation of followees for Twitter users. Many methods recommend followees based on topics extracted from their tweets, but many people tweet on the same topic, but with very different emotions about the topic. These people are not beneficial as candidates for new followees. The system should recommend new followees who tweet the same topic while expressing similar emotions about the topics in which the user is interested. Actually, it is easy for users to find which people tweet the same or similar topics, but it is difficult to find people who have similar emotions about the same topic. Therefore, users cannot follow people who have similar emotions about the same topic. As described in this paper, we call a person who tweet same topic and similar emotions about the topic a "latent followee". We propose the new followee recommendation system that recommends latent followees based on similar topics and similar emotions. Our proposed system first extracts same topics using clustering. Next the system extracts emotion of the same topic tweets using SVM. Then the system presents to the user people who tweet the same topic and similar emotions for the topic as latent followees. We also conducted an experiment and confirmed the availability of our proposed system. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3151759.3151817 | 19TH INTERNATIONAL CONFERENCE ON INFORMATION INTEGRATION AND WEB-BASED APPLICATIONS & SERVICES (IIWAS2017) |
Keywords | Field | DocType |
Recommendation, Twitter, Latent followee, Emotion, Clustering, SVM | Recommender system,Data mining,World Wide Web,Social media,Computer science,Cluster analysis | Conference |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuhiro Akiyama | 1 | 0 | 1.01 |
| Tadahiko Kumamoto | 2 | 92 | 15.22 |
| Akiyo Nadamoto | 3 | 189 | 34.24 |