This paper is published in Volume-7, Issue-3, 2021
Area
Information Technology and Software Development.
Author
Rutik Pravin Ambre, Abhishek Sand
Org/Univ
Thakur College of Engineering and Technology, Mumbai, Maharashtra, India
Pub. Date
25 May, 2021
Paper ID
V7I3-1464
Publisher
Keywords
Sentiment Analysis, Prediction, Data Extraction, Data Pre-Processing, Opinion Mining, Parts of Speech

Citationsacebook

IEEE
Rutik Pravin Ambre, Abhishek Sand. Opinion Mining for restaurant reviews using Naive Bayes Algorithm, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Rutik Pravin Ambre, Abhishek Sand (2021). Opinion Mining for restaurant reviews using Naive Bayes Algorithm. International Journal of Advance Research, Ideas and Innovations in Technology, 7(3) www.IJARIIT.com.

MLA
Rutik Pravin Ambre, Abhishek Sand. "Opinion Mining for restaurant reviews using Naive Bayes Algorithm." International Journal of Advance Research, Ideas and Innovations in Technology 7.3 (2021). www.IJARIIT.com.

Abstract

A wealth of unstructured opinion data exists online. Every day, millions of consumers add to this data when they share their opinion on a range of things, including feedback about their experiences with products and services. This feedback is volunteered, it contains the raw, unsolicited views and opinions about a brand, individual or event. Opinion Mining finds out the drivers behind the sentiment. By understanding what is driving the sentiment and how one is performing based on Net Sentiment, opinion data can be used to expose critical areas of strength and weakness. This data allows decision-makers in business, from customer experience and marketing to risk and compliance teams, to make the targeted, strategic overhauls needed to reinvigorate profitability or reclaim slipping market share. It is practically impossible to analyze all this reviews manually, so and automated aspect-based opinion mining approach is used. This paper focuses on aspect level, shows a comparative study amongst existing algorithm and proposes a new syntactic based approach which uses dictionary, aggregate score for opinion words. The dataset used was for restaurant reviews. The proposed method achieved a total accuracy of 87.06%.
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