Python Text Mining and Analytics: Transforming Text into Insights
About This Course
This course, Python Text Mining and Analytics: Transforming Text into Insights, is designed to equip professionals with the skills needed to develop effective text analytics solutions using the CRISP-DM framework. Participants will learn to read in text corpuses and perform essential text preprocessing techniques using Python, including tokenization, stemming, lemmatization, and vectorization methods like TF and TF-IDF.
This course delves into advanced text analytics, covering POS tagging, Named Entity Recognition, and text link analysis. Additionally, learners will build and evaluate machine learning models for sentiment analysis from social media data and summarize and visualize their findings. This comprehensive training will empower you to turn unstructured text data into valuable business intelligence.
What You'll Learn
- identify and develop text analytics solutions using Cross-Industry Standard Process for Data Mining (CRISP-DM).
- read in text corpus and perform text pre-processing using Python.
- perform text analytics and modify the data using Python with feature engineering.
- perform sentimental analysis using Python from social media data.
- perform sentiment summarization and visualization using Python.
Course Outline:
Topic 1 Overview of Text Mining and Text Analytics
Introduction to Natural Language Processing (NLP)
Applications of Text Analytics and Text Mining for Business Intelligence
Cross-Industry Standard Process for Data Mining (CRISP-DM)
Topic 2: Text Cleaning and Pre-processing
Install Python NLTK Package
Read In Text Corpus
Remove Punctuation and Stop Words
Pre-process Text using Tokenization, Stemming, Lemmatization
Vectorize Text using Term Frequency (TF) Vectorization, N-gram and Inverse-Document Frequency (TF-IDF)
Topic 3 Text Analytics
Part of Speech (POS) Tagging
Name Entity Recognition (NER)
Text Link Analysis and Feature Engineering
Topic 4: Sentimental Analysis
Overview of Machine Learning
Install Python Scikit Learn Package
Build a Machine Learning Model for Sentimental Analysis
Model Evaluation
Topic 5: Text Summarization
Summarize Sentiment Analysis
Visualize Text Summarization
Entry Requirements
Knowledge and Skills
• Able to operate using computer functions
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.