A Comparison Between Text Mining and Text Analytics

Text Mining vs Text Analytics


Before the development of data processing technologies, text mining and analytics used to waste a lot of time. In fact, processing or even executing machine learning algorithms could take days. However, with the advent of these tools, text mining and text analytics have become incredibly popular in the market.


Text mining and text analytics have virtually the same meanings in everyday speech, although they can also signify different things. They both combine ml algorithms, statistics, and languages to find textual trends and patterns in large datasets. Text mining and text analysis enable more quantifiable conclusions to be discovered through text analytics by translating the data into a well-organized format. Then, you can use data visualization tools to share your results with more people.


Differences Between Text Mining and Text Analytics


Text mining vs text analytics have different uses; the former focuses on qualitative insights, while the latter focuses on quantitative insights. Text mining, for instance, may examine polls and reviews to determine if buyers are pleased with a product. Both applications use other techniques, such as natural language processing (NLP), to convert raw data into texts and datasets into organized data (ready for analysis).


According to analytics experts, text mining is a word that is increasingly used in the current day as new fields and AI develop. Text mining gets data about sentiment, mood, and more from complex data using tools like natural language and machine learning understanding. Theoretically, a text mining system uses ratings, reviews, polls, and comments to determine if a consumer is pleased with a product and /or service.


On the contrary, text analytics will examine the recurring patterns and trends that show up in organized data. For example, using text analytics, you can forecast an increase in demand for a certain product or service by counting the number of times the name of the service or product has been referenced online in a period.


Text analytics is a paradigm from computational linguistics that can translate human knowledge into the rules of linguistics. Experts frequently use text mining and analytics services with data analytics, data visualization techniques, and AI recommendations to aid in quick decision-making.


There is no difference in productivity between text analytics and text mining methods. Text mining requires the analyst to manually label cases with results or categories to gather model parameters, whereas text analytics demands a specialist linguist to develop complicated rule sets.

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