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This article was published as a part of the DataScience Blogathon. Introduction TextMining is also known as TextDataMining or TextAnalytics or is an artificial intelligence (AI) technology that uses natural language processing (NLP) to extract essential data from standard language text.
One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is textmining? Text analysis takes it a step farther by focusing on pattern identification across large datasets, producing more quantitative results.
Predictive analytics is a standard tool that we utilize without much thought. Predictive analytics uses methods from datamining, statistics, machine learning, mathematical modeling, and artificial intelligence to make future predictions about unknowable events. It creates forecasts using historical data.
While ETH does not have a Linguistics department, its DataAnalytics Lab , lead by Thomas Hofmann , focuses on topics in machine learning, natural language processing and understanding, datamining and information retrieval. University of St. Gallen The University of St.
The surge of digitization and its growing penetration across the industry spectrum has increased the relevance of textmining in DataScience. Textmining is primarily a technique in the field of DataScience that encompasses the extraction of meaningful insights and information from unstructured textual data.
Fast-forward a couple of decades: I was (and still am) working at Lexalytics, a text-analytics company that has a comprehensive NLP stack developed over many years. In fact, Lexalytics has incorporated BERT into our textanalytics engine. I was out of the neural net biz. BERT is just too good not to use.
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