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Web Sentiment Analysis for Scoring Positive or Negative Words using Tweeter Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 96 - Number 6 |
| Year of Publication: 2014 |
| Authors: A. Pappu Rajan, S. P. Victor |
10.5120/16801-6518
|
A. Pappu Rajan, S. P. Victor . Web Sentiment Analysis for Scoring Positive or Negative Words using Tweeter Data. International Journal of Computer Applications. 96, 6 ( June 2014), 33-37. DOI=10.5120/16801-6518
Abstract
As people are free to say their opinions on anything using various social networking sites like Twitter, Facebook, Discussion forums, and blogs. Particularly Microblogging and text messaging have emerged and become dominated tool over the web. Twitter messages (tweets) is often used to share opinions and sentiments about the surrounding world. The availability of social content generated on sites such as Twitter creates new opportunities to study public opinion about the entity. This analysis we took twitter data for sentiment classification. The Sentiment analysis is done on a per-Tweet basis. The words in each Tweet are compared with those in other Tweets that have been previously labeled as "positive", or "negative". After looking at these words, the algorithm then judges whether the text in the Tweet is positive or negative based on the likelihood for each possibility. The overall objective of this paper is to determine the sentiment of the text, whether it is positive or negative, which is extended to strength of polarity also this approach is used to obtain the significant features and to analyzing the overall sentiment for each object by computing the weighted average for all the sentiments in the textual data.
References
- Curator. S (2013, September 23). Social media: a rich source of customer sentiment for you to mine. Whatech Channel.
- Koweika A. ,Gupta A. ,Sondhi K. (2013). Sentiment analysis for social media. International Journal of Advanced Research in Computer Science and Software Engineering
- Bissattini C. , Christodoulou K. (2013). Web sentiment analysis for revealing public opinions, trends and making good financial decisions. Journal of Advanced Research in Computer Science and Software Engineering
- Tulankar S. ,Athale R. ,Bhujbal S (2013). Sentiment analysis of equities using data mining techniques and visualizing the trends. International Journal of Computer Science Issues. .
- Qiu M. ,Yang L. , Jiang J. (2013). Mining user relations from online discussions using sentiment analysis and probabilistic matrix factorization. Proceedings of NAACL-HLT, Atlanta, Georgia.
- Buche A. , Chandak M. B. , Zadgaonkar A. (2013). Opinion mining and analysis: a survey. International Journal on Natural Language Computing(IJNLC). 39-48.
- Cataldi M. , Ballatore A. , Ilaria T. (2013. Good location, terrible food: Detecting feature sentiment in user-generated reviews. International Journal of Social Network Analysis and Mining (SNAM).
- Manjaly J. S. (2013). Twitter based sentiment analysis for subject identification. International Journal of Advanced Research in Computer and Communication Engineering
- Zuell B. , Preradovic N. M. , (2013). Methods and usage of sentiment analysis in the context of the TV industry. International Journal of Recent Advances in Information Science. .
- Jagtap V. S. , Pawar K. (2013). Analysis of different approaches to sentence-level sentiment classification. International Journal of Scientific Engineering and Technology. 164-170. Mullen T. and Malouf R. ,Taking sides: User classification for informal online political discourse. Internet Research, 2008.
- Nakov P. ,Kozareva Z. , Ritter A. (2013),"Sentiment analysis in Twitter. Second Joint Conference on Lexical and Computational Semantics". Atlanta, Georgia.
- Younggue B. ,Hongchul L. (2012),"Sentiment analysis of Twitter audience: Measuring the positive or negative influence", Journal of the American Society for Information Science and Technology.
- Savage N. (2011, March)," Twitter as medium and message", Communications of the ACM Society, Issue: 3, Vol. 54. pp: 18-20.
- ZHU Jian, XU Chen, and WANG Han-shi, 2010. Sentiment classification using the theory of ANNs, The Journal of China Universities of Posts and Telecommunications, 17(Suppl. ): pp. 58–62.
- X. Ding, B. Liu, and P. S. Yu, 2008. A holistic lexicon-based approach to opinion mining, Proceedings of the Conference on Web Search and Web Data Mining (WSDM).
- A. Pappu Rajan ,(2013)," A Study on Security Threat Awareness among Students Using Social Networking Sites, by Applying Data Mining Techniques", International Journal Of Research In Commerce, IT & Management, Vol. No. 3 , Issue No. 09 : ISSN 2231-5756
- Kwak, H. , Lee, C. , Park, H. &Moon, S. What is Twitter, a social network or a news media? Proceedings of the 19th international conference on World Wide Web, 591–600 (2010).
- Changhua Yang, Kevin Hsin-Yih Lin, and Hsin-Hsi Chen. 2007. Emotion classification using web blog corpora. In WI '07: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pages 275–278,Washington, DC, USA. IEEE Computer Society.
- B. Liu. Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, 2007
- Dongcheol,K. and Soon-?Ho,K. (2012)Investor Sentiment from Internet Message Postings and Predictability of Stock Returns. Working paper, Korea University Business School, Seoul 136-?701, pp. 2-?53.
- Kumar,A. and Teeja, M. S. (2012) Sentiment Analysis on Twitter. IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 3, pp. 372-?373.
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