Sentiment Analysis and Summarization of Social Media Content Using Topic Modeling
Prerna Mishra1, Ranjana Rajnish2, Pankaj Kumar3

1Prerna Mishra*, Amity Institute of Information Technology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow (India), India.
2Ranjana Rajnish, Amity Institute of Information Technology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow (India), India.
3Pankaj Kumar, Department of Computer Science, Sri Ramswaroop College of Engineering & Technology, Lucknow, India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 209-2012 | Volume-9 Issue-3, January 2020. | Retrieval Number: B8102129219/2020©BEIESP | DOI: 10.35940/ijitee.B8102.019320
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Abstract: Explosion of Web 2.0 had made different social media platforms like Facebook, Twitter, Blogs, etc a data hub for the task of Data Mining. Sentiment Analysis or Opinion mining is an automated process of understanding an opinion expressed by customers. By using Data mining techniques, sentiment analysis helps in determining the polarity (Positive, Negative & Neutral) of views expressed by the end user. Nowadays there are terabytes of data available related to any topic then it can be advertising, politics and Survey Companies, etc. CSAT (Customer Satisfaction) is the key factor for this survey companies. In this paper, we used topic modeling by incorporating a LDA algorithm for finding the topics related to social media. We have used datasets of 900 records for analysis. By analysis, we found three important topics from Survey/Response dataset, which are Customers, Agents & Product/Services. Results depict the CSAT score according to Positive, Negative and Neutral response. We used topic modeling which is a statistical modeling technique. Topic modeling is a technique for categorization of text documents into different topics. This approach helps in better summarization of data according to the topic identification and depiction of polarity classification of sentiments expressed.
Keywords: Opinion Mining, Sentiment Analysis, Topic Modeling, LDA.
Scope of the Article:  IoT Applied for Digital Contents