Sentiment Analysis of Tweets using Rapid Miner Tool
Sai Durga Pravallika Devi Setty1, Yarramneni Mownika Sai2, Akash Vijay Yadav3, Pellakuri Vidyullatha4
1Sai Durga Pravallika Devi Setty, Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur (Andhra Pradesh), India.
2Mownika Sai Yarramneni, Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur (Andhra Pradesh), India.
3Akash Vijay Yadav, Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur (Andhra Pradesh), India.
4Pellakuri Vidyullatha, Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur (Andhra Pradesh), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1410-1414 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3426048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Social media is one of the popular source for information retrieval and also it is being used for sharing day-to-day events of our lives and the incidents which are happening around the world. In present days, social networking websites like Twitter, Facebook, etc. are used extensively for communication. So they become an important source for understanding the emotions and opinions of most of the people. In this paper, data mining techniques are used to perform sentiment analysis on the tweets that are shared by the people on Twitter. In order to achieve this, tweets are collected from Twitter, text mining techniques are applied, and then they are used for building sentiment classifier. Rapid Miner software is used for this purpose. Here, three different classifiers, namely, K-Nearest Neighbor,Naive Bayes, SVM, are applied on data and then the results obtained are compared. The SVM algorithm proved to be more accurate compared to the other two algorithms used.
Keyword: Classification, RapidMiner, Sentiment Analysis, Text Mining, Twitter.
Scope of the Article: Pattern Recognition and Analysis