Sentiment analysis of customer product reviews using machine learning

Today, digital reviews play a pivotal role in enhancing global communications among consumers and influencing consumer buying patterns. E-commerce giants like Amazon, Flipkart, etc. provide a platform to consumers to share their experience and provide real insights about the performance of the produ...

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Bibliographic Details
Published in2017 International Conference on Intelligent Computing and Control (I2C2) pp. 1 - 5
Main Authors Singla, Zeenia, Randhawa, Sukhchandan, Jain, Sushma
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2017
Subjects
Big Data
Decision trees
Feature extraction
machine learning
Mobile handsets
Predictive models
Sentiment analysis
Support vector machines
text classification
text mining
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Summary:Today, digital reviews play a pivotal role in enhancing global communications among consumers and influencing consumer buying patterns. E-commerce giants like Amazon, Flipkart, etc. provide a platform to consumers to share their experience and provide real insights about the performance of the product to future buyers. In order to extract valuable insights from a large set of reviews, classification of reviews into positive and negative sentiment is required. Sentiment Analysis is a computational study to extract subjective information from the text. In the proposed work, over 4,000,00 reviews have been classified into positive and negative sentiments using Sentiment Analysis. Out of the various classification models, Naïve Bayes, Support Vector Machine (SVM) and Decision Tree have been employed for classification of reviews. The evaluation of models is done using 10 Fold Cross Validation.
DOI:10.1109/I2C2.2017.8321910