A Sentence Level Classification of Telugu News Document using Sentiment Analysis-Reference-Cited by-同舟云学术

A Sentence Level Classification of Telugu News Document using Sentiment Analysis

Published:2023 Issue: Volume:391 Page:01037
ISSN:2267-1242
Container-title:E3S Web of Conferences
language:
Short-container-title:E3S Web Conf.

Author:

Bygani Jalaja kumari,Venkateshwaralu Yella,Ramana K.V.

Abstract

In recent years, sentiment analysis-based categorization in low-resource languages and regional languages has become a hot topic in natural language processing. Researchers are more interested in categorizing sentiment in Indian languages such as Hindi, Telugu, Tamil, Bengali, Malayalam, and others. To the best of our knowledge, no microscopic study on Indian languages has been published to yet due to a lack of annotated data. Using Telugu sentiment analysis, we presented a two-phase classification technique for Telugu news phrases in this work. It first recognizes subjectivity categorization, which categorizes statements as Positive, Negative, or Neutral. Sentiment Classification is the next step, which divides subjective statements into positive and negative categories. We get an accuracy of 81 percent for sentiment analysis categorization using this method.

Publisher

EDP Sciences

Subject

General Medicine

Link

https://www.e3s-conferences.org/10.1051/e3sconf/202339101037/pdf

Reference11 articles.

1. Patra B.G., Das D., Das A., Prasath R. Shared task on sentiment analysis in indian languages (sail) tweets-an overview. In International Conference on Mining Intelligence and Knowledge Exploration 2015 Dec 9 (pp. 650–655). Springer, Cham.

2. Das D., Poria S., Dasari C.M., Bandyopadhyay S. Building resources for multilingual affect analysis-a case study on hindi, bengali and telugu. In Workshop Programme 2012 (p. 54).

3. Kumar S.S., Premjith B., Kumar M.A., Soman K.P. AMRITA_CEN-NLP@ SAIL2015: sentiment analysis in Indian Language using regularized least square approach with randomized feature learning. In International Conference on Mining Intelligence and Knowledge Exploration 2015 Dec 9 (pp. 671–683). Springer, Cham.

4. Prasad S.S., Kumar J., Prabhakar D.K., Pal S. Sentiment classification: an approach for Indian language tweets using decision tree. In International Conference on Mining Intelligence and Knowledge Exploration 2015 Dec 9 (pp. 656–663). Springer, Cham.

5. Sarkar K., Chakraborty S. A sentiment analysis system for Indian language tweets. In International Conference on Mining Intelligence and Knowledge Exploration 2015 Dec 9 (pp. 694–702). Springer, Cham.