Method for AI-Assisted Sentiment Analysis in Social Media

Abstract

The present invention provides a method and system for AI-assisted sentiment analysis of social media content, utilizing advanced natural language processing (NLP) and machine learning (ML) techniques to automatically classify the sentiment of user-generated content across multiple platforms in real-time. By analyzing posts, comments, hashtags, and user interactions, the system categorizes sentiment into predefined categories such as positive, negative, neutral, or specific emotional states like joy, anger, or sadness. The invention allows businesses, brands, and organizations to gain actionable insights into public perception, monitor trends, and respond proactively to shifts in sentiment, offering a scalable, adaptive, and efficient solution for social media sentiment tracking.

Specification

Description:The present invention relates to the field of natural language processing (NLP), artificial intelligence (AI), and sentiment analysis, particularly to a method for AI-assisted sentiment analysis in social media content. More specifically, it provides a system and method for automatically analyzing user-generated content from social media platforms using AI-driven sentiment classification to determine the emotional tone of posts, comments, and interactions, facilitating real-time insights for businesses and organizations to monitor and understand public sentiment.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prio , Claims:1. A method for AI-assisted sentiment analysis in social media, comprising:
• Collecting user-generated content from one or more social media platforms;
• Preprocessing the collected content to remove irrelevant information and normalize the data;
• Extracting features from the content using natural language processing techniques;
• Classifying the sentiment of the content into one or more predefined categories using an AI model;
• Generating real-time feedback or reports based on the classified sentiment.

2. The method of claim 1, wherein the sentiment classification model uses machine learning algorithms selected from the group consisting of Support Vector Machines, Random Forests, deep learning models, and recurrent neural networks.
3. The method of claim 1, wherein the sentiment categories include positive, negative, neutral, and mixed emotions.
4. The method of claim 1, further comprising the step of adapting the AI model by incorporating user feedback and additional labeled data to improve the sentimen

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