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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
SVM Optimization for Sentiment Analysis
Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.090455
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 4, 2018.
Abstract: Exponential growth in mobile technology and mini computing devices has led to a massive increment in social media users, who are continuously posting their views and comments about certain products and services, which are in their use. These views and comments can be extremely beneficial for the companies which are interested to know about the public opinion regarding their offered products or services. This type of public opinion otherwise can be obtained via questionnaires and surveys, which is no doubt a difficult and complex task. So, the valuable information in the form of comments and posts from micro-blogging sites can be used by the companies to eliminate the flaws and to improve the products or services according to customer needs. However, extracting a general opinion out of a staggering number of users’ comments manually cannot be feasible. A solution to this is to use an automatic method for sentiment mining. Support Vector Machine (SVM) is one of the widely used classification techniques for polarity detection from textual data. This study proposes a technique to tune the SVM performance by using grid search method for sentiment analysis. In this paper, three datasets are used for the experiment and performance of proposed technique is evaluated using three information retrieval metrics: precision, recall and f-measure.
Keywords: Sentiment analysis; polarity detection; machine learning technique; support vector machine (SVM); optimized SVM; grid search technique
Munir Ahmad, Shabib Aftab, Muhammad Salman Bashir, Noureen Hameed, Iftikhar Ali and Zahid Nawaz, “SVM Optimization for Sentiment Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 9(4), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090455
@article{Ahmad2018,
title = {SVM Optimization for Sentiment Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090455},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090455},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {4},
author = {Munir Ahmad and Shabib Aftab and Muhammad Salman Bashir and Noureen Hameed and Iftikhar Ali and Zahid Nawaz}
}
