Name
Abstract | ||
|---|---|---|
Topic modeling becomes a popular research area which shows us new way to search, browse and summarize large amount of texts. Methods of topic modeling try to uncover the hidden thematic structure in document collections. Topic modeling in connection with social networks, which are one of the strongest communication tool and produces large amount of opinions and attitudes on world events, can be useful for analysis in case of crisis situations, elections, launching a new product on the market etc. For that reason we pro-pose a tool for topic modeling over text streams from social networks in this paper. Description of proposed tool is extended with practical experiments. Realized experiments shown promising results when using our tool on real data in comparison to state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-45510-5_19 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Topic modeling,Social media,Natural language processing,Modularity clustering | Social network,Social media,Thematic structure,Computer science,Artificial intelligence,Natural language processing,Topic model,New product development | Conference |
Volume | ISSN | Citations |
9924 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miroslav Smatana | 1 | 0 | 1.69 |
| Jan Paralic | 2 | 56 | 13.96 |
| Peter Butka | 3 | 41 | 8.44 |