STM Journals Peer-reviewed research from Consortium e-Learning Network

OmniScience: A Multi-disciplinary Journal Original Research IMSEMTI 2024

Punjabi Text Sentiment Analysis Using Indic NLP

  1. Surbhi Sekhri Department of Computer Science and Engineering, Desh Bhagat University, Mandi Gobindgarh
  2. Nancy Shrivastav Department of Computer Science, St Soldier Institute of Engineering & Technology, Jalandhar
  3. Preet Kaur Department of Computer Science, CT Institute of Engineering, Management & Technology, Jalandhar
  4. Khushboo Bansal Department of Computer Science and Engineering, Desh Bhagat University, Mandi Gobindgarh
  5. Shilpi Wadhwa Department of Computer Science, CT Institute of Engineering, Management & Technology, Jalandhar
  6. Poonam Bhalla Department of Computer Science, CT Institute of Engineering, Management & Technology, Jalandhar

Abstract

Sentiment Analysis has become one of the challenging domains with the gaining popularity of Internet and social media platforms. Sentiment or opinion mining means to know people’s opinion, attitude, views and emotions towards anything. Sentiment analysis in English language has already been analyzing but there is a need of analyzing sentiments in other regional language in which Punjabi language is one of them. Users from the Punjab state of India have been creating content in the Punjabi language. There is a need to process such content by performing sentiment analysis to obtain valuable insights and directions from it. This will help researchers, organizations, and governments to analyze the user-generated content and mining useful information from it. The research uses n-gram approach which detects a sequence of n-words (n = 1, 2, 3…). The Indic NLP library of Python is used to perform the sentiment analysis of the public domain Punjabi language ext. datasets. There is a requirement to examine the content written in the Punjabi language to gain a more comprehensive grasp of Punjabi text. The focus of the paper is to recognize positive or negative sentiment from Punjabi content. The outcomes of the suggested approach demonstrate exceptional precision. The algorithm achieved an accuracy of 89% in sentiment analysis of Punjabi news articles.

Keywords

References (11)

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