A Multi-Feature AI Framework for Sentiment Analysis and Business Intelligence

Abstract

With the exponential growth of user-generated content across digital platforms, analysing customer sentiments has become a critical tool for businesses, policymakers, and researchers. This paper presents Sentiment.AI, a multi-feature AI-powered sentiment analysis system that integrates sentiment classification, emotion detection, topic modelling, word cloud generation, feature-based sentiment analysis, aspect-based sentiment analysis, named entity recognition (NER), customer segmentation, and fake review detection. Unlike traditional sentiment analysis models, Sentiment.AI leverages hybrid NLP techniques, combining VADER, DistilBERT, and TextBlob for sentiment detection, while employing Latent Dirichlet Allocation (LDA) for topic modelling and K-Means clustering for customer segmentation.

The system is designed to handle large-scale textual data with an average processing speed of 50,000 words per second, making it highly efficient for real-world applications such as business intelligence, brand reputation management, market research, and fraud detection. Our evaluation on datasets such as IMDB, Yelp, and Twitter Sentiment140 demonstrates an accuracy improvement of 8-12% over traditional sentiment classifiers, particularly in aspect-based and emotion classification tasks.

Furthermore, Sentiment.AI integrates large language models (LLMs) to generate AI-powered insights, allowing even non-data analysts to extract meaningful trends and patterns from various sentiment features. This LLM-powered content enhances data-driven decision-making by summarizing key findings in plain language. Additionally, users can download these AI-generated insights as a structured PDF report, making sentiment intelligence accessible and actionable across different business and research domains.

This paper outlines the architecture, implementation, and evaluation of Sentiment.AI, demonstrating its ability to deliver granular sentiment analysis with high precision. The proposed system addresses key challenges in sentiment analysis, such as context understanding, sarcasm detection, and fake review identification, making it a comprehensive solution for sentiment-driven decision-making.