A novel approach for sentiment analysis of Punjabi text using SVM

Joint Authors

Kaur, Amandeep
Gupta, Vishal

Source

The International Arab Journal of Information Technology

Issue

Vol. 14, Issue 5 (30 Sep. 2017)6 p.

Publisher

Zarqa University

Publication Date

2017-09-30

Country of Publication

Jordan

No. of Pages

6

Main Subjects

Information Technology and Computer Science

Abstract EN

Opinion mining or sentiment analysis is to identify and classify the sentiments/opinion/emotions from text.

Over the last decade, in addition to english language, many indian languages include interest of research in this field.

For this paper, we compared many approaches developed till now and also reviewed previous researches done in case of indian languages like telugu, Hindi and Bengali.

We developed a hybrid system for Sentiment analysis of Punjabi text by integrating subjective lexicon, N-gram modelling and support vector machine.

Our research includes generation of corpus data, algorithm for Stemming, generation of punjabi subjective lexicon, developing Feature set, Training and testing support vector machine.

Our technique proves good in terms of accuracy on the testing data.

We also reviewed the results provided by previous approaches to validate the accuracy of our system.

American Psychological Association (APA)

Kaur, Amandeep& Gupta, Vishal. 2017. A novel approach for sentiment analysis of Punjabi text using SVM. The International Arab Journal of Information Technology،Vol. 14, no. 5.
https://search.emarefa.net/detail/BIM-852255

Modern Language Association (MLA)

Kaur, Amandeep& Gupta, Vishal. A novel approach for sentiment analysis of Punjabi text using SVM. The International Arab Journal of Information Technology Vol. 14, no. 5 (Sep. 2017).
https://search.emarefa.net/detail/BIM-852255

American Medical Association (AMA)

Kaur, Amandeep& Gupta, Vishal. A novel approach for sentiment analysis of Punjabi text using SVM. The International Arab Journal of Information Technology. 2017. Vol. 14, no. 5.
https://search.emarefa.net/detail/BIM-852255

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-852255