A novel approach for sentiment analysis of Punjabi text using SVM

المؤلفون المشاركون

Kaur, Amandeep
Gupta, Vishal

المصدر

The International Arab Journal of Information Technology

العدد

المجلد 14، العدد 5 (30 سبتمبر/أيلول 2017)6ص.

الناشر

جامعة الزرقاء

تاريخ النشر

2017-09-30

دولة النشر

الأردن

عدد الصفحات

6

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الملخص 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.

نمط استشهاد جمعية علماء النفس الأمريكية (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

نمط استشهاد الجمعية الأمريكية للغات الحديثة (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

نمط استشهاد الجمعية الطبية الأمريكية (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

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-852255