This paper is published in Volume-6, Issue-3, 2020
Area
Computer Science and Engineering
Author
Erukonda Harsha Praneetha
Co-authors
Chokkakula Pushpa, Dundi Swathi, Bushi Deepika, Dr. B. Esther Sunanda
Org/Univ
Andhra University College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
Pub. Date
09 May, 2020
Paper ID
V6I3-1181
Publisher
Keywords
Sentimental analysis, Opinion mining, Naive bayes classification, Bayesian interface system.

Citationsacebook

IEEE
Erukonda Harsha Praneetha, Chokkakula Pushpa, Dundi Swathi, Bushi Deepika, Dr. B. Esther Sunanda. Sentiment Analysis on Mobile Reviews using Naive Bayes Classification, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Erukonda Harsha Praneetha, Chokkakula Pushpa, Dundi Swathi, Bushi Deepika, Dr. B. Esther Sunanda (2020). Sentiment Analysis on Mobile Reviews using Naive Bayes Classification. International Journal of Advance Research, Ideas and Innovations in Technology, 6(3) www.IJARIIT.com.

MLA
Erukonda Harsha Praneetha, Chokkakula Pushpa, Dundi Swathi, Bushi Deepika, Dr. B. Esther Sunanda. "Sentiment Analysis on Mobile Reviews using Naive Bayes Classification." International Journal of Advance Research, Ideas and Innovations in Technology 6.3 (2020). www.IJARIIT.com.

Abstract

Now a day, due to increasing technology we can get the opinions of the users all over the world using web mining. People are always curious and show a keen interest in what people perceive various aspects of living and non-living things. In order to understand and analyze the various traits and varying personalities, opinion mining is needed. The main objective of Sentiment Analysis on Mobile Reviews using Naïve Bayes Classification is to develop a system that mines the positive as well as negative reviews of users. For this purpose, we first collect the data from the famous e-commerce websites and extract the reviews posted by users related to different mobile handsets. In this process, we define some common features like Camera, Battery Life, Overall Performance, and Design and then find related reviews as a dataset for the analysis purpose. Negative and Positive words that are commonly used for expressing opinions are also collected. This system provides a sentimental analysis on various smartphone reviews diving them positive or negative or neutral using Naïve Bayes Classification. This is basically being obtained by studying and analyzing the reviews posted by different users. Analysis of different words coupled in a sentence represents various sentiments and experiences of users and impacts the products available in the market. This analysis compiles a structural modeling approach and Bayesian Interface System to identify the polarity of the opinion which subsequently classifies positive and negative opinions.
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