Sentiment Analysis in Social Media

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Aishwarya Nagaraj Naik; Dr. B Meenakshi Sundaram

Sentiment Analysis in Social Media

Authors: Aishwarya Nagaraj Naik, Dr. B Meenakshi Sundaram

Unique Paper ID: 166720
Volume: 11
Issue: 2
PageNo: 1882-1892

Keywords: Sentiment analysis; recurrent neural network; deep neural network; convolutional neural network; recursive neural network

Abstract:
The World Wide Web generates vast amounts of data reflecting users' views, emotions, and opinions on various topics, significantly influencing readers, vendors, and politicians. Platforms like Facebook, WhatsApp, and Twitter are inundated with such data, which can be transformed into valuable information through sentiment analysis. This method classifies sentiments as negative, positive, favorable, or unfavorable, but it faces challenges due to a lack of labeled data. To address this, sentiment analysis and machine learning techniques are combined. In a project, a sentiment analysis system for Twitter data was developed using a Random Forest classifier and a Flask web application. The system preprocesses tweets by removing URLs, HTML tags, and special characters, and converts text data into numerical features using TF-IDF vectorization. The classifier was trained and evaluated, demonstrating effective performance in classifying sentiments. The system, deployed as a web application, allows users to input tweets and receive real-time sentiment predictions. This project showcases the practical implementation of sentiment analysis, detailing data preprocessing, feature extraction, model building, and deployment, and highlights the potential of machine learning models in analyzing social media data.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 2
PageNo: 1882-1892

Sentiment Analysis in Social Media

Available:https://ijirt.org/article?manuscript=166720

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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