Sentiment Analysis of Movies on Social Media using R Studio
Jaichandran R1, Bagath Basha C2, Shunmuganathan K.L3, Rajaprakash S4, Kanagasuba Raja S5
1Jaichandran R, Department of CSE, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (Deemed to be University), Tamil Nadu, India.
2C. Bagath Basha, Department of Computer Science and Engineering, Vinayaka Mission’s Research Foundation, Salem, Tamil Nadu, India.
3Shunmuganathan K.L, principal, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (Deemed to be University), Tamil Nadu, India.
4Rajaprakash, Department of CSE, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (Deemed to be University), Tamil Nadu, India,
5Kanagasuba Raja S, Department of IT, SRM Eswari Engineering College, Chenaai, Tamil Nadu, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2171-2175 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8586088619/2019©BEIESP | DOI: 10.35940/ijeat.F8586.088619
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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: This paper presents sentiment analysis of twitter data on movies using R-studio. Twitter is one of the largest social media that shares user opinion about a thing or event that happens all around the world. Recently social media analysis gained importance in digital marketing. User tweets about a product or event, person, movie, etc., are analyzed to know market trends and customer feedback. In this paper, first we have performed literature study on various methods used in twitter data analysis. Second, we have discussed about the steps involved in accessing twitter data. Finally, we have performed sentiment analysis on tweeter data for the movies titled kabali, Bharath Ane Nenu Mersal, and Dangal. User data for the movies are classified into positive, neutral and negative based on DBM and SVM. Sentiment scores are used as evaluation metrics. Results shows DBM is effective in classifying sentiments and produced better sentiment scores compared to SVM. Results are helpful in identifying popularity of the movies and audience feedback about the movies.
Keywords: Big Data Analytics, R Studio, Twitter, Sentiment Analysis.