A Survey on Sentiment Analysis of Threads Application: A Comprehensive Study

Sentiment analysis, also considered as opinion mining, is a crucial element in NLP (Natural Learning Processing), which distinguishes the polarity of any text, or word, or phrase. The project aims to examine and enhance sentiment analysis abilities using machine learning models: Support vector machine (svm), Naīve Bayes, Linear regression. The raw datasets are collected and preprocessed from diverse domains to ensure a comprehensive study of this paper. Text preprocessing includes the steps: manual labelling, normalization, tokenization, stop words removal, preparing the texts for sentiment analysis. The project focuses on comparison of models to identify the suitable approach out of all. This project offers insights to different techniques, its application, limitations, strengths contributing to NLP methodologies for elevation of sentimental analysis.

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Publication Details

Journal
International Journal of Recent Development in Engineering and Technology
Published
2025-04-09
DOI
https://doi.org/10.54380/ijrdet0425_01
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
0.00
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article

A Survey on Sentiment Analysis of Threads Application: A Comprehensive Study

Afsheen Pothiyawala
International Journal of Recent Development in Engineering and Technology
Sentiment Analysis and Opinion Mining
article

A Survey on Sentiment Analysis of Threads Application: A Comprehensive Study

Afsheen Pothiyawala
article en

Abstract

Sentiment analysis, also considered as opinion mining, is a crucial element in NLP (Natural Learning Processing), which distinguishes the polarity of any text, or word, or phrase. The project aims to examine and enhance sentiment analysis abilities using machine learning models: Support vector machine (svm), Naīve Bayes, Linear regression. The raw datasets are collected and preprocessed from diverse domains to ensure a comprehensive study of this paper. Text preprocessing includes the steps: manual labelling, normalization, tokenization, stop words removal, preparing the texts for sentiment analysis. The project focuses on comparison of models to identify the suitable approach out of all. This project offers insights to different techniques, its application, limitations, strengths contributing to NLP methodologies for elevation of sentimental analysis.

International Journal of Recent Development in Engineering and TechnologyVol. 14(1)
Openalex Percentile: Top 97%
Sentiment Analysis and Opinion Mining
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