Open Access
Issue |
ITM Web Conf.
Volume 13, 2017
2nd International Conference on Computational Mathematics and Engineering Sciences (CMES2017)
|
|
---|---|---|
Article Number | 01030 | |
Number of page(s) | 6 | |
DOI | https://doi.org/10.1051/itmconf/20171301030 | |
Published online | 02 October 2017 |
- B. Pang, and L. Lee, Opinion mining and sentiment analysis, Foundations and Trends® in Information Retrieval, 1–135 (2008) [Google Scholar]
- W. Tom, Hadoop: The definitive guide, O’Reilly Media, Inc. (2012) [Google Scholar]
- https://manifoldcf. apache.org/ (2015) [Google Scholar]
- http://www.statista. com/statistics/272014/global-social-networks-ranked-by-number-of-users/, (2015) [Google Scholar]
- G. Aydin, and İ. R. Hallac, Distributed NLP, ISITES2015 (2015) [Google Scholar]
- A. Pak, and P. Paroubek, Twitter as a Corpus Sentiment Analysis and Opinion Mining, In Proceedings of the Seventh Conference on International Language Resources and Evaluation, 1320–1326 (2010) [Google Scholar]
- A. Bifet, and E. Frank, Sentiment Knowledge Discovery in Twitter Streaming Data, 13th International Conference, DS 2010, Canberra, Australia, pp 1–15 (2010). [Google Scholar]
- S. R. Yerva, Z. Mikĺos, and K. Aberer, It was easy, when apples and blackberries were only fruits, Working Notes for CLEF 2010 Conference, 1176 (2010) [Google Scholar]
- R. González-Ibáñez, S. Muresan, and N. Wacholder, Identifying Sarcasm in Twitter: A Closer Look, The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 581–586 (2011) [Google Scholar]
- A. Agarwal, B. Xie, I. Vovsha, O. Rambow, R. Passonneau, Sentiment Analysis of Twitter Data, Proceedings of the Workshop on Language in Social Media (LSM 2011), 30–38 (2011) [Google Scholar]
- H. Saif, H. Yulan, and H. Alani, Semantic sentiment analysis of twitter, The 11th International Semantic Web Conference (ISWC 2012), Boston, MA, USA (2012) [Google Scholar]
- J. Spencer, and G. Uchyigit, Sentimentor: Sentiment Analysis of Twitter Data, Proceedings of the 1st International Workshop on Sentiment Discovery from Affective Data (SDAD 2012), 56–66 (2012) [Google Scholar]
- E. Kouloumpis, T. Wilson, and J. Moore, Twitter Sentiment Analysis: The Good the Bad and the OMG!, 5. International AAAI Conference on Weblogs and Social Media, 538–541 (2011) [Google Scholar]
- H. Saif, H. Yulan, and H. Alani, Alleviating data sparsity for twitter sentiment analysis, CEUR Workshop Proceedings (2012) [Google Scholar]
- A. Agarwal, and J. S. Sabharwal, End-to-End Sentiment Analysis of Twitter Data, 24th International Conference on Computational Linguistics (2012) [Google Scholar]
- E. M. Cody, A. J. Reaga, L. Mitchell, P. S. Dodds, and C. M. Danforth, Climate change sentiment on Twitter: An unsolicited public opinion poll, 2015. [Google Scholar]
- G. Paltoglou, Sentiment-based event detection in Twitter, Journal of the Association for Information Science and Technology (2015) [Google Scholar]
- K. Singhal, B. Agrawal, and N. Mittal, Modeling Indian General Elections: Sentiment Analysis of Political Twitter Data, In Information Systems Design and Intelligent Applications, 469–477 (2015) [Google Scholar]
- https://github. com/ahmetaa/zemberek-nlp (2015) [Google Scholar]
- https://github. com/maidis/mythes-tr/tree/master/veriler (2015) [Google Scholar]
- J. Lin, and A. Kolcz, Large-scale machine learning at twitter, Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, 793–804 (2012) [Google Scholar]
- A. Pak, and P. Paroubek, Twitter based system: Using Twitter for disambiguating sentiment ambiguous adjectives, Proceedings of the 5th International Workshop on Semantic Evaluation. Association for Computational Linguistics (2010) [Google Scholar]
- S. Mukherjee, A. Malu, B. AR, and F P. Bhattacharyya, TwiSent: a multistage system for analyzing sentiment in twitter, Proceedings of the 21st ACM international conference on Information and knowledge management (2012) [Google Scholar]
- J. P. Carvalho, and L. Coheur, Introducing UWS-A fuzzy based word similarity function with good discrimination capability: Preliminary results, Fuzzy Systems (FUZZ), 2013 IEEE International Conference on. IEEE (2013) [Google Scholar]
- A. Kumar, and T. M. Sebastian, Sentiment analysis on twitter, IJCSI International Journal of Computer Science, P 372-L 378 (2012) [Google Scholar]
- B. O’Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith, From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series, ICWSM 11., 122-129 (2010) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.