Momina Shaheen; Shahid M. Awan; Nisar Hussain; Zaheer A. Gondal

Sentiment Analysis on Mobile Phone Reviews Using Supervised Learning Techniques

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Author(s)

Momina Shaheen ^1,* Shahid M. Awan ² Nisar Hussain ² Zaheer A. Gondal ²

1. Department of Computer Science Comsats University Islamabad, Lahore Campus, Lahore, Pakistan

2. School of Systems and Technology University of Management and Technology Lahore, Pakistan

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2019.07.04

Received: 18 Feb. 2019 / Revised: 1 Mar. 2019 / Accepted: 21 Mar. 2019 / Published: 8 Jul. 2019

Index Terms

Sentiment Classification, NLP, Opinion Mining, NB-SVM, Random Forest, LSTM, CNN, Phone Reviews

Abstract

Opinion Mining or Sentiment Analysis is the process of mining emotions, attitudes, and opinions automatically from speech, text, and database sources through Natural Language Processing (NLP). Opinions can be given on anything. It may be a product, feature of a product or any sentiment view on a product. In this research, Mobile phone products reviews, fetched from Amazon.com, are mined to predict customer rating of the product based on its user reviews. This is performed by the sentiment classification of unlocked mobile reviews for the sake of opinion mining. Different opinion mining algorithms are used to identify the sentiments hidden in the reviews and comments for a specific unlocked mobile. Moreover, a performance analysis of Sentiment Classification algorithms is performed on the data set of mobile phone reviews. Results yields from this research provide the comparative analysis of eight different classifiers on the evaluation parameters of accuracy, recall, precision and F-measure. The Random Forest Classifiers offers more accurate predictions than others but LSTM and CNN also give better accuracy.

Cite This Paper

Momina Shaheen, Shahid M. Awan, Nisar Hussain, Zaheer A. Gondal, "Sentiment Analysis on Mobile Phone Reviews Using Supervised Learning Techniques", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.7, pp. 32-43, 2019.DOI: 10.5815/ijmecs.2019.07.04

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