![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
Sentimental Analysis for Social Media–A Review
In recent times, Social media has emerged as a personal communication media, as well as, a media to convey reviews about items and benefits or even political and general occasions among its clients. By using web and the web 2.0 the information in Twitter, Facebook and Instagram are easily retrieved. Because of its use across the board and prevalence, there is a monstrous measure of client surveys or feelings delivered and shared day by day. Mining client sentiments from Social Media is definitely not a straight forward assignment; it can be proficient in various ways. Gathering client sentiments can be costly and tedious assignment utilizing traditional strategies. This paper examines the challenges in doing sentimental analysis for Social Media.
Keywords
Data Mining, Internet, Social Media, Sentimental Analysis, Opinion Mining.
User
Font Size
Information
- Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis”. Foundations and Trends® in Information Retrieval, 2(1–2), 2008, 1-135.
- Das, Sanjiv, and Mike Chen. "Yahoo! for Amazon: Extracting market sentiment from stock message boards." Proc. of the Asia Pacific Finance Association Annual Conf. (APFA). 35, 2001,43.
- Tong, Richard M. "An operational system for detecting and tracking opinions in on-line discussion." In Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification, 1(6), 2001.
- Kaplan, A.M. and Haenlein, M., “Users of the world, unite! The challenges and opportunities of Social Media”. Business Horizons, 53(1), 2010, 59-68.
- Kaschesky, M., Sobkowicz, P. and Bouchard, G.,. “Opinion mining in social media: modeling, simulating, and visualizing political opinion formation in the web”, Proc. 12st Annual Int. Digital Government Research Conf.: Digital Government Innovation in Challenging Times, 2011, 317-326.
- Chaovalit, P. and Zhou, L. “Movie review mining: A comparison between supervised and unsupervised classification approaches”, Proc. 38st Annual Hawaii International Conference on System Sciences (HICSS'05), 2005, 112c-112c.
- Kearns, Michael, Siddharth Suri, and Nick Montfort. "An experimental study of the coloring problem on human subject networks." Science, 313(5788), 2006, 824-827.
- Kleinberg, Jon. "Complex networks and decentralized search algorithms." Proc. Int. Congress of Mathematicians (ICM), 3, 2006, 1019-1044.
- Liben‐Nowell, David, and Jon Kleinberg. "The link‐prediction problem for social networks." Journal of the American society for information science and technology, 58(7),2007, 1019-1031.
- Koppel, M., and Schler, J. “The importance of neutral examples for learning sentiment”. Computational Intelligence, 22(2), 2006,100-109.
- Qu, Yan. "Exploring attitude and affect in text: Theories and applications." AAAI Spring Symposium, 2004.
- Pang, Bo, and Lillian Lee. "Using very simple statistics for review search: An exploration." Coling 2008: Companion volume: Posters ,2008, 75-78.
- Pang, B. and Lee, L. “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales”. Proc. 43rd Annual meeting on association for computational linguistics, 2005,115-124.
- Godbole, Namrata, Manja Srinivasaiah, and Steven Skiena. "Large-Scale Sentiment Analysis for News and Blogs.", Icwsm, 7(21), 2007, 219-222.
- Cortizo J, Carrero F, Gomez J, Monsalve B, Puertas E. “Introduction to Mining SM”. Proc. 1st Int. Workshop on Mining SM, 2009, 1-3.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 209
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 0