الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 92 |  |  |
| | | from | 92 | | |
Abstract
Online social networks like Twitter and Facebook have become essential for connecting people, disseminating information, and sharing interests since the internet’s advent into our daily lives. Twitter, in particular, has grown rapidly and plays a significant role in analyzing user-generated data to personalize experiences and enhance recommendation tasks. Twitter posts serve as an important source of information for identifying users’ positive interests on various topics, including their food preferences. A model of Food Interests Analysis (FIA) is proposed that leverages real-time processing and analytics of Twitter data streams. The primary objective is to extract insights and valuable information about users’ food preferences and interests, enhancing user experiences, advancing the capabilities of personalized food recommendations, and leveraging them for Internet of Things (IoT)-based personalized services and artificial intelligence (AI) applications. Furthermore, business intelligence (BI) has become an important analytical technique for market forecasting and assessing consumer satisfaction and market demand. Since business intelligence requires in-depth analysis, sentiment analysis is the process of using natural language processing (NLP) and machine learning (ML) techniques to identify the emotional tone and attitude in text, making it useful for analyzing Twitter posts and customer reviews to identify user preferences and market demand. As a result, it’s critical to choose relevant advertisements for users at particular locations to capture their attention and generate profit. The proposed FIA Model combines both topic modeling and sentiment analysis techniques by employing Latent Dirichlet Allocation (LDA) with term frequency-inverse document frequency .