Name
Abstract | ||
|---|---|---|
Continuous social text streams, such as tweets, provide a timeline of discussions. Topic modeling techniques such as Latent Dirichlet Allocation (LDA) have been used to extract the topics being discussed on social media streams. Recently, Online LDA has been proposed as a fast alternative for topic extraction, based on on-line stochastic optimization, while sentiment analysis is often used to track the polarity of posts. In this paper, we propose an online technique, integrating Online LDA and sentiment analysis to extract more refined polarity-aware topics within an online learning framework from continuous Twitter streams. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2615569.2615666 | WebSci |
Keywords | Field | DocType |
stream data,social media,online lda,data mining,sentiment analysis,clustering | Online learning,Data mining,Latent Dirichlet allocation,Stochastic optimization,Data stream mining,Social media,Information retrieval,Computer science,Sentiment analysis,Timeline,Topic model | Conference |
Citations | PageRank | References |
1 | 0.37 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gopi Chand Nutakki | 1 | 4 | 2.55 |
| Olfa Nasraoui | 2 | 1515 | 164.53 |