Projects per year
Abstract
While it is widely recognized that streams of social media messages contain valuable information, such as important trends in the users’ interest in consumer products and markets, uncovering such trends is problematic, due to the extreme volumes of messages in such media. In the case Twitter messages, following the interest in relation to all known products all the time is technically infeasible. IE narrows topics to search. In this paper, we present
experiments on using deeper NLP-based processing of product-related events mentioned in news streams to restrict the volume of tweets that need to be considered, to make the problem more tractable. Our goal is to analyze whether such a combined approach can help reveal correlations and how they may be captured.
experiments on using deeper NLP-based processing of product-related events mentioned in news streams to restrict the volume of tweets that need to be considered, to make the problem more tractable. Our goal is to analyze whether such a combined approach can help reveal correlations and how they may be captured.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Joint Workshop on NLP&LOD and SWAIE : SemanticWeb, Linked Open Data and Information Extraction |
| Number of pages | 8 |
| Place of Publication | Hissar |
| Publication date | 2013 |
| Pages | 41-48 |
| ISBN (Print) | 978-954-452-025-0 |
| Publication status | Published - 2013 |
| MoE publication type | A4 Article in conference proceedings |
| Event | RANLP 2013 Workshop on Semantic Web and Information Extraction - Hissar, Bulgaria Duration: 13 Sep 2013 → 13 Sep 2013 |
Fields of Science
- 113 Computer and information sciences
- social media
- Information Extraction
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PULS
Yangarber, R., Du, M., Pivovarova, L., Pierce, M., von Etter, P. & Huttunen, S.
01/12/2007 → …
Project: Research project
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LLL: Language Learning Lab
Yangarber, R., Katinskaia, A., Hou, J., Furlan, G. & Kylliäinen, I. P.
Project: Research project