TY - GEN
T1 - Classification of amazon book reviews based on sentiment analysis
AU - Srujan, K. S.
AU - Nikhil, S. S.
AU - Raghav Rao, H.
AU - Karthik, K.
AU - Harish, B. S.
AU - Keerthi Kumar, H. M.
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2018.
PY - 2018
Y1 - 2018
N2 - Since the dawn of internet, e-shopping vendors like Amazon have grown in popularity. Customers express their opinion or sentiment by giving feedbacks in the form of text. Sentiment analysis is the process of determining the opinion or feeling expressed as either positive, negative or neutral. Capturing the exact sentiment of a review is a challenging task. In this paper, the various preprocessing techniques like HTML tags and URLs removal, punctuation, whitespace, special character removal and stemming are used to eliminate noise. The preprocessed data is represented using feature selection techniques like term frequency-inverse document frequency (TF–IDF). The classifiers like K-Nearest Neighbour (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) and Naive Bayes (NB) are used to classify sentiment of Amazon book reviews. Finally, we present a comparison of (i) Accuracy of various classifiers, (ii) Time elapsed by each classifier and (iii) Sentiment score of various books.
AB - Since the dawn of internet, e-shopping vendors like Amazon have grown in popularity. Customers express their opinion or sentiment by giving feedbacks in the form of text. Sentiment analysis is the process of determining the opinion or feeling expressed as either positive, negative or neutral. Capturing the exact sentiment of a review is a challenging task. In this paper, the various preprocessing techniques like HTML tags and URLs removal, punctuation, whitespace, special character removal and stemming are used to eliminate noise. The preprocessed data is represented using feature selection techniques like term frequency-inverse document frequency (TF–IDF). The classifiers like K-Nearest Neighbour (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) and Naive Bayes (NB) are used to classify sentiment of Amazon book reviews. Finally, we present a comparison of (i) Accuracy of various classifiers, (ii) Time elapsed by each classifier and (iii) Sentiment score of various books.
UR - https://www.scopus.com/pages/publications/85043753985
U2 - 10.1007/978-981-10-7512-4_40
DO - 10.1007/978-981-10-7512-4_40
M3 - Conference contribution
AN - SCOPUS:85043753985
SN - 9789811075117
T3 - Advances in Intelligent Systems and Computing
SP - 401
EP - 411
BT - Information Systems Design and Intelligent Applications - Proceedings of 4th International Conference INDIA 2017
A2 - Bhateja, Vikrant
A2 - Nguyen, Bao Le
A2 - Nguyen, Nhu Gia
A2 - Satapathy, Suresh Chandra
A2 - Le, Dac-Nhuong
PB - Springer Verlag
T2 - 4th International Conference on Information Systems Design and Intelligent Applications, INDIA 2017
Y2 - 15 June 2017 through 17 June 2017
ER -