Project Based Text Mining in Python
The course is about Text mining with implementation in Python. By the end of the course, there are a couple of projects that help students collectively apply different concepts covered in individual modules, towards a single goal. It has Natural language processing concepts that are required for text analysis which are Text segmentation, tokenization, stemming, lemmatization, Parts of speech tagging, and other text normalization schemes. NLTK library is used for most of these tasks. Sci-kit learns library is used for training models, document classification, grouping similar documents together, and their evaluation. Further, we used sentiment analysis that can be performed both through dictionaries and as a classification problem. We have used topic modeling (Latent Dirichlet Allocation) in particular for extracting useful compact information from a collection of text documents as topics. It is also used as a dimensionality reduction technique. Following is a link to the course.
#textMining #naturallanguageprocessing #NLP #textprocessing
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