Twitter sentiment analysis

Social media have received more attention nowadays. Public and private opinion about a wide variety of subjects are expressed and spread continually via numerous social media. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers' perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers' perceptions. This paper reports on the design of a sentiment analysis, extracting a vast amount of tweets. Prototyping is used in this development. Results classify customers' perspective via tweets into positive and negative, which is represented in a pie chart and html page. However, the program has planned to develop on a web application system, but due to limitation of Django which can be worked on a Linux server or LAMP, for further this approach need to be done.

[1]  Gitanjali Kalia,et al.  A Research Paper on Social media: An Innovative Educational Tool , 2013 .

[2]  Bin Gu,et al.  Content Contribution in Social Media: The Case of YouTube , 2012, 2012 45th Hawaii International Conference on System Sciences.

[3]  Li Liu,et al.  Kernel-Based Method for Automated Walking Patterns Recognition Using Kinematics Data , 2006, ICNC.

[4]  Shubhamoy Dey,et al.  Performance Investigation of Feature Selection Methods and Sentiment Lexicons for Sentiment Analysis , 2012 .

[5]  Al Sweigart,et al.  Invent Your Own Computer Games With Python , 2010 .

[6]  Jun Zhang,et al.  A case study of micro-blogging in the enterprise: use, value, and related issues , 2010, CHI.

[7]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[8]  David C. Yen,et al.  Exploring the potential effects of emoticons , 2008, Inf. Manag..

[9]  Rudy Prabowo,et al.  Sentiment analysis: A combined approach , 2009, J. Informetrics.

[10]  Johanna D. Moore,et al.  Twitter Sentiment Analysis: The Good the Bad and the OMG! , 2011, ICWSM.

[11]  Fabrício Benevenuto,et al.  Comparing and combining sentiment analysis methods , 2013, COSN '13.

[12]  Grzegorz Kondrak,et al.  A Comparison of Sentiment Analysis Techniques: Polarizing Movie Blogs , 2008, Canadian Conference on AI.

[13]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .

[14]  Yanchun Zhou,et al.  A Sociolinguistic Study of American Slang , 2013 .

[15]  Patrick Paroubek,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.

[16]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[17]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

[18]  Danah Boyd,et al.  Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[19]  Gulden Uchyigit,et al.  Sentimentor: Sentiment Analysis of Twitter Data , 2012, SDAD@ECML/PKDD.

[20]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[21]  Siddharth Sharma,et al.  Application of Support Vector Machines for Damage Detection in Structures , 2009 .

[22]  Manuel Perea,et al.  ERP correlates of masked affective priming with emoticons 1-s2.0-S0747563212003007-fx1 , 2013, Comput. Hum. Behav..

[23]  Hansjörg Schmauder,et al.  Visual analysis of microblog content using time-varying co-occurrence highlighting in tag clouds , 2012, AVI.

[24]  Walter Daelemans,et al.  Pattern for Python , 2012, J. Mach. Learn. Res..

[25]  Thomas Way,et al.  Tracking Sentiment Analysis through Twitter , 2012 .