Summary of the paper
| Title | Twitter as a Corpus for Sentiment Analysis and Opinion Mining |
|---|---|
| Authors | Alexander Pak and Patrick Paroubek |
| Abstract | Microblogging today has become a very popular communication tool among Internetusers. Millions of users share opinions on different aspects of life everyday.Therefore microblogging web-sites are rich sources of data for opinion miningand sentiment analysis. Because microblogging has appeared relatively recently,there are a few research works that were devoted to this topic. In our paper,we focus on using Twitter, the most popular microblogging platform, for thetask of sentiment analysis. We show how to automatically collect a corpus forsentiment analysis and opinion mining purposes. We perform linguistic analysisof the collected corpus and explain discovered phenomena. Using the corpus, webuild a sentiment classifier, that is able to determine positive, negative andneutral sentiments for a document. Experimental evaluations show that ourproposed techniques are efficient and performs better than previously proposedmethods. In our research, we worked with English, however, the proposedtechnique can be used with any other language. |
| Language | Document Classification, Text categorisation |
| Topics | Corpus (creation, annotation, etc.), Emotion Recognition/Generation, Document Classification, Text categorisation |
Full paper  |
Twitter as a Corpus for Sentiment Analysis and Opinion Mining |
| Bibtex | @InProceedings{PAK10.385, author = {Alexander Pak and Patrick Paroubek}, title = {Twitter as a Corpus for Sentiment Analysis and Opinion Mining}, booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |