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Paper Title : Exploration and Assessment of Multiple Crowd Sourced Feedbacks

ISSN : 2395-1303
Year of Publication : 2021
10.29126/23951303/IJET-V7I5P13

Authors: Gaddam Akhil Reddy, Kothapally Nithesh Reddy,Dr. Vijayalakshmi Kakulapati

         


Abstract
The majority of moviegoers use websites like IMDb, Amazon, and Yelp to research and select their next viewing or purchase. Currently, moviegoers make their choices based on IMDb or Amazon ratings and reviews of films they've seen. As outlined in this article, a better technique is proposed: the subjective content of movie critics' movie scores and critiques may be evaluated and projected onto a picture, resulting in an emotional map. The second step is to seek films with emotion maps that have certain emotion map patterns that appeal to you. Concerning decision-making processes, sentiment analysis has long been a point of concern for marketing, business, and management departments. When we conduct sentiment analysis, we're looking for emotions and points of view in a piece of writing. It identifies and validates a person's feelings about a certain piece of material for the reader. Product reviews, blogs, status updates, and tweets, among other things, are all examples of sentiment data seen on social media sites. This massively generated data may be used for sentiment analysis to express the opinions of the general public on products. This article presents a highly accurate sentiment analysis algorithm for Amazon, IMDB, and Yelp product, movie, and restaurant reviews. These reviews are classified into two categories: positive and negative, and we employ a variety of classifiers to determine which category is more favorable. The best classifier is chosen based on how accurate it is.

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Keywords
-Opinion Mining, SVM, Logistic Regression, Lexicon, Pipeline, TF-IDF Vectorizer, Ensemble Learning