Classification of Reviews on Mobile Phones Using Text Mining Techniques

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    People register their opinion or feedback regarding the products in different forum. This research work is based on the classification of reviews regarding the different mobile phones. Dataset from Amazon pertaining to the opinions for mobile phones is used in this work. Opinion which is expressed as text is classified as positive opinion or a negative opinion using text mining techniques. Opinion mining helps to understand the customers in a better way. This work shows the visual representation of words by using word cloud and to classify the reviews on a two point scale. From the dataset, randomly 197 reviews are taken out of which 148 reviews are classified as positive, 49 reviews are classified as negative.

     


  • Keywords


    Classification, Opinion mining, text mining

  • References


      [1] Mongkol Saensuk, “Feature-Based Opinion Mining on Smart-Phone Reviews” IIAE International Conference on Intelligent Systems and Image Processing 2015

      [2] Nidhi Mishra, “Classification of Opinion Mining Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 56 No.13, October 2012

      [3] Lin Zhanga, “Sentimental analysis on review of mobile users”, International Conference on Mobile Systems and Pervasive Computing”2014

      [4] Richa Sharma, “Opinion mining of movie reviews at document level” International Journal on Information Theory (IJIT), Vol.3, No.3, July 2014

      [5] Phong Minh Vu, “phrase-based extraction of user opinions in Mobile app reviews” ACM, 2016

      [6] Dr. S. Vijayarani, “Preprocessing Techniques for Text Mining-An Overview” International Journal of Computer Science & Communication Networks,Vol 5(1),October 2016

      [7] Eivind Bjørkelund, “A Study of Opinion Mining and Visualization of Hotel Reviews”,ACM December,2012

      [8] Pravesh Kumar Singh, “Methodology study of opinion mining and sentimental analysis techniques” International Journal on Soft Computing (IJSC) Vol. 5, No. 1, February 2014

      [9] Gaurav Dubey, “User Reviews Data Analysis using Opinion Mining on Web”, International Conference on Futuristic trend in Computational Analysis and Knowledge Management, 2015

      [10] K. M. Anil Kumar, “Analysis of Users’ Sentiments from Kannada Web Documents”, International Multi-Conference on Information Processing, 2015

      [11] Minara P Anto, “Product rating using Sentimental analysis”, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) , 2016

      [12] Mohd Ridzwan Yaakub, “Integration of Opinion into Customer Analysis Model”,IEEE, International Conference on e-Business Engineering,2011

      [13] Po-Wei Liang, “Opinion mining on social media data”, IEEE, International Conference on Mobile Data Management,2013

      [14] [14] Hai Son Le, “Aspect Analysis for Opinion Mining of Vietnamese Text”, International Conference on Advanced Computing and Applications, 2015

      [15] Vekata Rajeev P “Recommending products to customers using opinion mining using online products reviews and features. IEEE 2013.

      [16] R. He, J. McAuley Modeling the visual evolution of fashion trends with one-class collaborative filtering. WWW, 2016 J.McAuley, C.Targett, J.Shi, Van den Hengel. Image-based recommendations on styles and substitutes. SIGIR, 2015


 

View

Download

Article ID: 16766
 
DOI: 10.14419/ijet.v7i3.4.16766




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.