Name
Abstract | ||
|---|---|---|
In last years, the spread of social Web has promoted a strong interest in analyzing how information related to a given topic diffuses. Nevertheless, this is still quite an unexplored field in the literature. In this paper we propose a general approach that makes use of a set of Natural Language Processing (NLP) techniques to analyse some of the most important features of information related to a topic. The domain of this study is Twitter, since here topics are easily identified by means of hashtags. In particular, our aim is to analyse the possible change over time of the content sub-topicality and sentiment in the tracked tweets, and bring out their relationships with the users' demographic features. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-41754-7_34 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Corpus analysis,Natural language processing techniques,Twitter,Information demographic,User generated content analysis | World Wide Web,Social web,Computer science,Thread (computing),Corpus analysis,Artificial intelligence,Natural language processing | Conference |
Volume | ISSN | Citations |
9612 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ekaterina Shabunina | 1 | 3 | 1.07 |
| Stefania Marrara | 2 | 171 | 21.05 |
| Gabriella Pasi | 3 | 1673 | 169.31 |