Empirical Evaluation of various ML algorithms for classification of online Restaurant reviews

The gaining popularity and availability of ecommerce websites led many users to express their views and opinions on various products or services that are available online. Due to the extensive presence of data which is increasing everyday over the internet, it shows that the reviews or opinions prov...

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Bibliographic Details
Published in2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) pp. 1 - 4
Main Authors Priya, Kamath B., Geetha, M., Dinesh, Acharya U.
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.02.2021
Subjects
Business
Classification
Classification algorithms
Computational modeling
Electronic commerce
Machine Learning
Opinion Mining
Review Mining
Sentiment analysis
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Summary:The gaining popularity and availability of ecommerce websites led many users to express their views and opinions on various products or services that are available online. Due to the extensive presence of data which is increasing everyday over the internet, it shows that the reviews or opinions provided by the users on the e- commerce sites are very large and in unstructured form. Therefore, it is highly challenging for the online users, customers and manufacturers to take appropriate decision about the opinions on these reviews. Hence there is a need to analyze the opinions present in the reviews to know if the review stated is positive or negative. Opinion Mining aims at analyzing the user opinions in the text. This work mainly aims at binary classification of reviews using different ML techniques thereby identifying the best model suitable for classifying the online restaurant reviews.
DOI:10.1109/ICAECT49130.2021.9392457