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dc.contributor.author | Waheed, Sadia Reg # 31243 | |
dc.contributor.author | Yaseen, Saira Reg # 31245 | |
dc.contributor.author | Abid, Sumayya Reg # 31253 | |
dc.date.accessioned | 2017-06-19T08:42:27Z | |
dc.date.available | 2017-06-19T08:42:27Z | |
dc.date.issued | 2016-05 | |
dc.identifier.uri | http://hdl.handle.net/123456789/1826 | |
dc.description | Supervised by Engr. Bushra Fazal | en_US |
dc.description.abstract | The world of technology is developing rapidly and people are adopting to social media fast. To this day twitter is one of the most popular social network. Personal opinions regarding “everything” is shared on the social media. This data is very useful for gathering public opinion. Opinionist provides a web based platform for user to fetch tweets on a desired topic and displays the result in the form of graphical notations. This makes it easier for the user to determine whether the public opinion is positive or negative about the particular topic. The Opinionist uses three sentiment analysis techniques namely Naive Bayes Algorithm, Lexicon Bayes approach and Support Vector Machine Algorithm. Using the result of the three algorithms and produces an average and displays it along with the results produced by the three algorithm. Use of averaging reduces extreme values giving a more general and realistic value. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Bahria University Karachi Campus | en_US |
dc.subject | Stop-words, Opinion Mining, Sentiment analysis, Lexicon, Naive Bayes, Support Vector Machine. | en_US |
dc.title | Opinionist | en_US |
dc.type | Thesis | en_US |