Volume 20 No 8 (2022)
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A Recommender System for Indian Credit Cards using Text Analytics
Madhukeshwar R K, Ratnakar Pandey, Shinu Abhi
With the increase in web and social media usage, views and experiences on products or services shared by online users have increased. Social media acts as the main source of data for text analytics on which the users’ sentiments can be performed. Organizations can gain valuable insights into social marketing strategies by finding sentiments and emotions towards their products. Sentiment analysis is a process of computationally finding, classifying, and categorizing opinions expressed on the block of text, to decide whether sentiment towards a particular topic or a product, is positive, negative, or neutral and whether emotions are happy, sad, angry, etc. by combining Machine Learning and Natural Language Processing. In this paper, we perform sentiment analysis through NLP techniques on the reviews and tweets collected from websites and Twitter on prominent Indian credit cards. Predicted sentiment values are used to develop a recommendation model which recommends similar credit cards based on categories.
Text Mining, Sentiment Analysis, Sentiment Classification, Natural Language Processing, Recommendation engine, Credit Cards
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