INTRODUCTION
One could argue that data mining is simply the next logical step in developing information technology as we know it today. Scanning, assembling, sorting, and analyzing data could be a “search” method. It is a technique for gleaning useful information from enormous amounts of data in database systems, server farms, and other archives. Data mining techniques include statistics, database technology, artificial intelligence, high-performance computing, machine learning, pattern recognition, visualization, neural networks, information retrieval, and spatial data analysis signals. An essential aspect of data mining is that it permits information retrieval to determine consistencies or elevated data from datasets that can then be examined in multiple ways. Thousands of papers have been digitized and are viewable online. Academics and practitioners are increasingly using text mining in various fields. The corporate trend toward digitization has improved the quality of unstructured data. We can now mine unstructured data using big data analytics. Large-scale unstructured data sentiment analysis is essential for theory development (Ogunleye, 2021).
The term “sentiment analysis” refers to studying people’s feelings and opinions. Sentiment classification can be used to gauge public sentiment in many ways. This data could be used to understand better and predict future social shifts. In the corporate world, sentiment analysis helps companies improve their strategic plans and better understand their customers’ opinions. Customers are essential in today’s customer-centric corporate world.
DISCUSSIONS
Data Mining
Data mining has attracted substantial attention in the information sector due to the amount of data and the pressure to turn it into meaningful facts and communication. This training will benefit management consulting, product testing and market research, structural engineering, and technology breakthroughs. All economic sectors (governments, businesses, influential organizations, etc.) seek vast data for industrial and technological purposes.
To start data mining, acquire, and analyze needs. The business perspective helps data mining experts or consumers define the project scope. The next stage, data investigation, will search for and transform patterns in data. Gathering, evaluating, and investigating the project’s needs are required tasks. Experts create metadata based on their knowledge of the topics. Data mining experts use their abilities to turn data into information. Extraction, transformation, and loading are the three steps of ETL. They are also in charge of creating new data traits. Data is presented in an organized manner while maintaining its worth. All modeling strategies are used to obtain adequate information filtering. To provide objective standards, both modeling and evaluation should be done simultaneously. After finalization, the resulting work could be checked for accuracy. This is the filtration sage that follows efficient modeling. If the result is unsatisfactory, the procedure is repeated. After a successful outcome, no details are missed. Data mining experts evaluate the final result. In the end, this is the most critical step in the overall procedure. Professionals give clients information in tables or charts (Ogunleye, 2021).
Text Mining
Knowledge-based businesses frequently utilize text mining to identify relevant info to solve particular research queries. Information, connections, and statements that might go unnoticed in the sea of literary texts can be uncovered by text mining. An organized version of this data could then be used in additional analysis or presentation, such as mind maps, clustered HTML tables, and charts. Natural Language Processing (NLP) is one of the essential methods used to analyze the information in text mining. For informative, normative, or analytic applications, the structured data generated by text mining could be connected to databases, information repositories, or advanced analytics platforms (Kumar, 2021).
Sentiment Analysis
Sociology, Psychology, machine learning, and NLP are all involved in sentiment classification. Information and computational capabilities have grown tremendously in years, allowing for more actionable insights. As a result, computer vision has become the primary method for analyzing sentiment. Published research on sentiment analysis is plentiful, and several additional types of research on the subject are also available. Algorithms’ capacity to interpret language has dramatically increased thanks to rapid advancements in machine learning. Proper research would benefit from the innovative application of cutting-edge intelligent algorithms (Ligthart, 2021).
SUMMARY
Because of the widespread use of social media technologies, consumers increasingly turn to them to research and evaluate products and services before making a purchasing decision. When consumers or competitors post fraudulent remarks on these websites, a company’s reputation can be affected significantly. This type of activity could potentially hurt the economy of any organization, and it is possible. It is feasible to better understand clients’ needs in the quality and support industries by utilizing data mining, text mining, and natural language processing (NLP).
Because of the rapid expansion of online discussion forums, there is no shortage of people willing to express their concepts and ideas. This increase makes it difficult for corporations to acquire a clearer idea of the collective thoughts and consumer attitudes regarding goods. Advertisers may now learn more about consumer views regarding their goods because of the proliferation of online user-generated material and sentiment analysis tools. Companies can efficiently serve their most vulnerable clients by gleaning insights from user reviews about their feelings about an item.
REFERENCE
Kumar, S., Kar, A. K., & Ilavarasan, P. V. (2021). Applications of text mining in services management: A systematic literature review. International Journal of Information Management Data Insights, 1(1), 100008.
Ligthart, A., Catal, C., & Tekinerdogan, B. (2021). Systematic reviews in sentiment analysis: a tertiary study. Artificial Intelligence Review, 54(7), 4997-5053.
Ogunleye, J. O. (2021). The Concept of Data Mining.
write