Year 2016,
Volume: 22 Issue: 2, 106 - 110, 01.05.2016
Abstract
Sentimental Twitter software is parsing, analyzing and reporting Twitter data, giving service to individuals and corporate users via its user friendly graphical user interface. Each tweet is classified as positive, negative or neutral in Sentimental Twitter. In this study, both lexicon and n-gram method has been used to perform and implement two different methods. As a result the lexicon method has been measured more performance than the n-gram method.
Keywords
References
- Szomszor MN, Kostkova P, de Quincey, E. “#Swineflu: Twitter predicts swine flu outbreak in 2009”. 3rd International ICST Conference on Electronic Healthcare for the 21st Century (eHEALTH2010), Casablanca, Morocco, 13-15 December 2010.
- Bian J, Topaloglu U, Yu F. “Towards large-scale Twitter mining for drug-related adverse events”. International Workshop on Smart Health and Wellbeing (SHB’12), Maui, Hawaii, USA, 29 October-2 November 2012
- Nguyen LE, Wu P, Chan W, Peng W, Zhang Y. “Predicting collective sentiment dynamics from time-series social media”. Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM ’12), Beijing, China, 12 August 2012.
- Claster WB, Dinh H, Cooper M. “Naive bayes and unsupervised artificial neural nets for Cancun tourismsocial media data analysis”. 2nd World Congress on Nature and Biologically Inspired Computing (NaBIC). Kitakyushu, Fukuoka, Japan, 15-17 December 2010.
- Liu Y, Huang X, An A, Yu X. “ARSA: A sentiment awaremodel for predicting sales performance using blogs”. 30th ACM SIGIR International Conference on Research and Development in Information Retrieval, Amsterdam, the Netherlands, 23-27 July 2007.
- Asur S, Huberman BA. “Predicting the Future with Social Media”. IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT), Toronto, ON, Canada, 31 August-3 September 2010.
- Joshi M, Das D, Gimpel K, Smith NA. “Movie reviews and revenues: an experiment in text regression”. Human Language Technologies: The 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Los Angeles, CA, USA, 1-6 June 2010.
- Bollen J, Mao H, Zeng X. “Twitter mood predicts the stock market”. Journal of Computational Science, 2(1), 1-8, 2011.
- Pang B, Lee L, Vaithyanathan S. “Thumbs up? Sentiment classification using machine learning techniques”. Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, PA, USA, 6-7 July 2002.
- Turney PD. “Thumbs up or thumbs down? Semantic orientation Applied to unsupervised classification of reviews”. 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, PA, USA, 712 July 2002.
- Eroğul U. Sentiment Analysis in Turkish. MSc Thesis, Middle East Technical University, Ankara, Turkey, 2009.
- Vural AG, Cambazoğlu BB, Şenkul P, Tokgöz ZO. “A frame work for sentiment analysis in Turkish: Application to polarity detection of movie reviews in Turkish”. 27th International Symposium on Computer and Information Sciences, Paris, France, 3-4 October 2012.
- Meral M, Diri B. “Twitter üzerinde duygu analizi”. IEEE 22. Sinyal İşleme ve İletişim Uygulamaları Kurultayı, Trabzon, Türkiye, 23-25 Nisan 2014.
- Şimşek M, Özdemir S. “Analysis of the relation between Turkish twitter messages and stock market index”. 6th International Conference on Application of Information and Communication Technologies (AICT), Tbilisi, Georgia, 1719 October 2012.
- Türkmenoğlu C, Tantuğ AC. “Sentiment analysis in Turkish media”. Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM ’14), Beijing, China, 21-26 June 2014.
Year 2016,
Volume: 22 Issue: 2, 106 - 110, 01.05.2016
Abstract
Duygusal Twitter, kullanıcıya kullanım kolaylığı sağlayan ve görsel kullanıcı ara yüzü ile hem bireysel, hem de kurumsal kullanıcılar için Twitter verisini ayrıştıran, analiz eden ve raporlayan bir programdır. Duygusal Twitter’da her tweet için olumlu, olumsuz ve nötr olmak üzere 3 farklı sonuç döndürülmektedir. Çalışmada hem sözlük hem de n-gram modeli kullanılarak iki yöntem geliştirilmiştir. Sözlük yöntemi, n-gram yöntemine göre daha başarılı sonuçlar vermiştir.
Keywords
References
- Szomszor MN, Kostkova P, de Quincey, E. “#Swineflu: Twitter predicts swine flu outbreak in 2009”. 3rd International ICST Conference on Electronic Healthcare for the 21st Century (eHEALTH2010), Casablanca, Morocco, 13-15 December 2010.
- Bian J, Topaloglu U, Yu F. “Towards large-scale Twitter mining for drug-related adverse events”. International Workshop on Smart Health and Wellbeing (SHB’12), Maui, Hawaii, USA, 29 October-2 November 2012
- Nguyen LE, Wu P, Chan W, Peng W, Zhang Y. “Predicting collective sentiment dynamics from time-series social media”. Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM ’12), Beijing, China, 12 August 2012.
- Claster WB, Dinh H, Cooper M. “Naive bayes and unsupervised artificial neural nets for Cancun tourismsocial media data analysis”. 2nd World Congress on Nature and Biologically Inspired Computing (NaBIC). Kitakyushu, Fukuoka, Japan, 15-17 December 2010.
- Liu Y, Huang X, An A, Yu X. “ARSA: A sentiment awaremodel for predicting sales performance using blogs”. 30th ACM SIGIR International Conference on Research and Development in Information Retrieval, Amsterdam, the Netherlands, 23-27 July 2007.
- Asur S, Huberman BA. “Predicting the Future with Social Media”. IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT), Toronto, ON, Canada, 31 August-3 September 2010.
- Joshi M, Das D, Gimpel K, Smith NA. “Movie reviews and revenues: an experiment in text regression”. Human Language Technologies: The 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Los Angeles, CA, USA, 1-6 June 2010.
- Bollen J, Mao H, Zeng X. “Twitter mood predicts the stock market”. Journal of Computational Science, 2(1), 1-8, 2011.
- Pang B, Lee L, Vaithyanathan S. “Thumbs up? Sentiment classification using machine learning techniques”. Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, PA, USA, 6-7 July 2002.
- Turney PD. “Thumbs up or thumbs down? Semantic orientation Applied to unsupervised classification of reviews”. 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, PA, USA, 712 July 2002.
- Eroğul U. Sentiment Analysis in Turkish. MSc Thesis, Middle East Technical University, Ankara, Turkey, 2009.
- Vural AG, Cambazoğlu BB, Şenkul P, Tokgöz ZO. “A frame work for sentiment analysis in Turkish: Application to polarity detection of movie reviews in Turkish”. 27th International Symposium on Computer and Information Sciences, Paris, France, 3-4 October 2012.
- Meral M, Diri B. “Twitter üzerinde duygu analizi”. IEEE 22. Sinyal İşleme ve İletişim Uygulamaları Kurultayı, Trabzon, Türkiye, 23-25 Nisan 2014.
- Şimşek M, Özdemir S. “Analysis of the relation between Turkish twitter messages and stock market index”. 6th International Conference on Application of Information and Communication Technologies (AICT), Tbilisi, Georgia, 1719 October 2012.
- Türkmenoğlu C, Tantuğ AC. “Sentiment analysis in Turkish media”. Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM ’14), Beijing, China, 21-26 June 2014.
There are 15 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Research Article |
| Authors | |
| Publication Date | May 1, 2016 |
| Published in Issue | Year 2016 Volume: 22 Issue: 2 |
