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Introduction to Applied Text Mining in Python Before going ahead, it is important to understand, What is Text Mining in Python? Text mining is also known as textanalytics or NaturalLanguageProcessing (NLP). Visualize the sentiment distribution and analyze trends and patterns in the data.
One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? Dataanalysis and interpretation The next step is to examine the extracted patterns, trends and insights to develop meaningful conclusions.
NaturalLanguageProcessing has seen some major breakthroughs in the past years; with the rise of Artificial Intelligence, the attempt at teaching machines to master human language is becoming an increasingly popular field in academia and industry all over the world.
Using causal graphs, LIME, Shapley, and the decision tree surrogate approach, the organization also provides various features to make it easier to develop explainability into predictive analytics models. IBM Watson Studio Since its founding in 1975, SPSS has developed into one of the leading statistical and analytics programs.
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Prescriptive Analytics Projects: Prescriptive analytics takes predictive analysis a step further by recommending actions to optimize future outcomes. NLP techniques help extract insights, sentiment analysis, and topic modeling from textdata.
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