Name
Abstract | ||
|---|---|---|
As Twitter gains popularity, millions of messages are generated every day via this platform allowing people to communicate with each other through daily chatter and public conversations. This social content is usually considered more subjective than professional articles involving a lot of opinions and thoughts. This has stimulated many companies to use tweets to keep track and have general overview on their customer opinions about the brand. The conversational element of Twitter is of particular interest to the marketing community. However, most studies on summarizing customer opinions through Twitter, have so far employed single tweets rather than considering the whole conversations. As tweets are limited to 140 characters and usually written in an informal way, it is frequently hard to detect the exact meaning of the tweet when taken separately. In this paper, we propose a new approach for customer opinion summarization based on Twitter conversations named COSTwiC. We employ a new conversation-based method that employs conversation interactions to effectively extract the different product features as well as the polarity of the conversation messages. Experimentations show promising results. In particular, we have proved that incorporating conversation structure in the opinion summarization task contributes to improving system performance. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2912845.2912862 | WIMS |
Field | DocType | Citations |
Data mining,Automatic summarization,World Wide Web,Conversation,Computer science,Popularity | Conference | 0 |
PageRank | References | Authors |
0.34 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rania Othman | 1 | 0 | 1.01 |
| Rami Belkaroui | 2 | 16 | 4.56 |
| Faiz, R. | 3 | 18 | 2.72 |