Name
Abstract | ||
|---|---|---|
Due to its ever increasing popularity, Twitter has become a pervasive information outlet. In this paper, we present a passive sensing framework for identifying trends via Twitter. In our framework, we use a multi-dimensional corpus for fine-granularity sensing of trends, and employ both vector-space and set-space methods for achieving accuracy. We present two applications of our framework. The first one is sensing trends in public opinion by using an emotion-category corpus. The second application is sensing trends in location-types in a city by using a location-category corpus. Our experiments show that the proposed methods are able to determine changes in trends effectively in both application scenarios. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1504/IJAHUC.2013.056271 | IJAHUC |
Keywords | Field | DocType |
set-space method,location-category corpus,emotion-category corpus,multi-dimensional corpus,pervasive information outlet,public opinion,application scenario,opinion mining,location | Data science,Computer science,Sentiment analysis,Popularity,Passive sensing,Public opinion | Journal |
Volume | Issue | ISSN |
14 | 1 | 1743-8225 |
Citations | PageRank | References |
6 | 0.53 | 25 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yavuz Selim Yilmaz | 1 | 89 | 5.47 |
| Muhammed Fatih Bulut | 2 | 60 | 6.81 |
| Cuneyt Gurcan Akcora | 3 | 158 | 12.48 |
| Murat Ali Bayir | 4 | 184 | 13.46 |
| Murat Demirbas | 5 | 1670 | 102.80 |