Year: 2025
Type: article
Abstract: The rise of social media platforms has led to an unprecedented amount of unstructured text data. This presents Business Intelligence (BI) with a great chance to learn more about public ideas, feelings, and market dynamics. This study looks into new ways to use social media analytics, like opinion mining and sentiment analysis, to improve business intelligence (BI) tools. The report talks about how standard BI systems have trouble interpreting social media data that is noisy, high-volume, and fast-moving, as well as how hard it is to use these insights to make strategic decisions. It stresses the necessity for advanced methods for collecting and preparing data, as well as advanced opinion mining and sentiment analysis using Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL), coupled with comprehending the context and making predictions. The article also talks about how social media analytics may be used in real life to improve customer intelligence and engagement, get a competitive edge, and predict market trends and consumer behaviour. It also points out some of the main problems with analysing social media data, like the amount of data, its quality, the difficulty of the language, and prejudice. It also talks about how hard it is to combine social media data with standard BI systems, like the cost, data silos, governance, and skill gaps. Centralised data repositories, ETL tools, API leveraging, data purification, real-time synchronisation, strong security, advanced analytics tools, and investing in people are all suggested as strategic options for seamless integration. The report says that to turn unstructured social media data into useful business intelligence, you need to take a comprehensive approach that includes technology, better processes, and talented workers. This will give you an edge in the fast-changing global market.
Authors: Upendra Kanuru, Sirisha Cheepuri
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7204992454