Year: 2025
Type: article
Abstract: This research utilizes logistic regression as well as LSTM, SVM, and Random Forest to derive useful information from social media information found on websites such as Instagram, Facebook, and Twitter. Utilizing machine learning algorithms, we explore sentiment analysis, trend detection, user profiling, and content classification. Logistic regression is used for analyzing sentiment, an important element in grasping public opinion and brand perception. By applying natural language processing (NLP) methods, we classify social media posts based on whether they convey positive, negative, or neutral feelings. This enables businesses to track shifts in customer happiness and opinions as time progresses, supporting smart decision-making and proactive interaction with users. By adding logistic regression to our approach, we improve the depth of our investigation, giving companies more resources to uncover valuable information from social media data. This comprehensive strategy allows businesses to adjust to customer preferences, strategize effectively, and stay competitive in the digital realm of Facebook, Instagram, and Twitter.
Author: Sachi Bagade
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414934441
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