TY - GEN
T1 - Empirical evaluation of various ML algorithms for classification of online restaurant reviews
AU - Priya Kamath, B.
AU - Geetha, M.
AU - Dinesh Acharya, U.
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - 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 ecommerce 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.
AB - 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 ecommerce 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.
UR - http://www.scopus.com/inward/record.url?scp=85109216414&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85109216414&partnerID=8YFLogxK
U2 - 10.1109/ICAECT49130.2021.9392457
DO - 10.1109/ICAECT49130.2021.9392457
M3 - Conference contribution
AN - SCOPUS:85109216414
T3 - Proceedings of the 2021 1st International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021
BT - Proceedings of the 2021 1st International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021
Y2 - 19 February 2021 through 20 February 2021
ER -
