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Text Mining Vs Text Analytics
In the early days of the global digitization process, Big Data management used to be a major challenging task for the analytical professionals. Traditional Big Data management tools like Hadoop, Spark etc weren’t that efficient when it comes to handling large volumes of Big Data. This is where Data Science has gained prominence. Having the presence of advanced tools, techniques & Machine Learning algorithms, Data Science is very much capable of handling large reserves of Big Data & also extracts the insights that are encrypted deep inside the data.
Currently, there have been a lot of advancements in the field of Data Science & the modern days Data Scientists are literally playing with Big Data at their finger tips. Now, with the advancements in AI & Machine Learning technologies, Data Scientists are now able to extract & analyze data which is in a textual format. Text Mining & Text Analytics are two major techniques in Data Science that have gathered a lot of prominence.
Now, let’s understand the differences between Text Analysis & Text Mining.
This is an advanced & automated data analysis technique that helps in analyzing textual data either in a structured or unstructured format. Text mining is the concept within Text Analysis. Sentiment Analysis, NLP models, Information Extraction, Document Categorization, etc are the best techniques in use for Text Analysis.
It makes use of advanced frameworks like LearnSec. Programming languages like R & Python are extensively used along with PowerBi, Azure and other languages.
Text Mining is an advanced technique in Data Science that helps the enterprises to make the most out of their textual data. Enterprises will be collecting their textual data across different social medial platforms, online customer reviews, etc & will be analyzing using Textual Analysis techniques to understand their customer opinion.
It makes use of ETL framework & makes use of programming languages like R & Python.
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