An Integrated Artificial Intelligence Model for Understanding User Intent: Leveraging Internet Text Data Mining and Intelligence Influence Analysis from Online Reviews and Social Media

An Integrated Artificial Intelligence Model for Understanding User Intent: Leveraging Internet Text Data Mining and Intelligence Influence Analysis from Online Reviews and Social Media

연구 분야: Artificial Intelligence

학회: SN Computer Science

Text data from networks and social media is pervasive and is growing at a very rapid pace eventually meaning that managing these large text data sets has grown to be a demanding undertaking and has emerged as a significant area of study. A thorough examination of text data mining and analysis using various techniques such as clustering, classification, and soft computing techniques has been extensively conducted in the past. This paper presents an integrated Artificial Intelligence (AI) model that leverages internet text data mining and intelligence influence analysis from online reviews and social media to better comprehend user intent. The proposed model combines advanced techniques in natural language processing, sentiment analysis, and data mining. Firstly, it utilizes web scraping to gather a vast corpus of online reviews and social media posts related to specific products or services. It employs sophisticated text mining algorithms to extract key insights and sentiments from this data. Knowledge inference in the industry need to fully comprehend consumer behaviour and buying preferences to attract steady revenue streams and pick up more engaged clients. This Knowledge inference approach incorporates cutting-edge methods for influence analysis and text data mining from internet sources, including social media and reviews. The AI model seeks to gain a better grasp of human intent in digital contexts by utilizing these enormous data repositories. The proposed model incorporates intelligence influence analysis, a novel approach that identifies influential factors shaping user opinions and preferences. By analysing the interplay of sentiments, topics, and user interactions within online content, the model can uncover hidden patterns and correlations.