Text Analytics with R
About This Course
Take your data analysis skills to the next level with our WSQ-accredited Text Analytics with R course. Designed to equip you with practical expertise, this course delves into essential topics such as text mining, sentiment analysis, and natural language processing. You'll learn to use R to effectively analyze text data, recognize patterns, and make data-driven decisions. Hands-on projects and real-world examples are integrated to ensure you can apply what you've learned immediately.
The course not only covers the fundamentals but also introduces advanced techniques to manipulate and analyze text-based data. By the end of this course, you will have the ability to apply text analytics to a range of business scenarios, thereby enhancing your role in any data-centric organization. Transform unstructured text into valuable insights and position yourself at the forefront of the data analytics field.
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 R
- perform text analytics using R and modify the data with feature engineering.
- perform sentimental analysis using R from social media data
- perform sentiment summarization and visualization using R
Course Outline:
Topic 1 Overview of Text Mining and Text Analytics
• Introduction to Text Mining and Text Analytics
• Applications of Text Mining and Text Analytics for Business Intelligence
• Cross-Industry Standard Process for Data Mining (CRISP-DM)
Topic 2: Text Cleaning and Pre-processing
• Install R Text Mining Packages
• Read In Text Corpus
• Remove Punctuation and Stop Words
• Pre-process Text using Tokenization, Stemming, Lemmatization
• Vectorize Text using Term Frequency (Count) Vectorization, N-gram and Inverse-Document Frequency Weighting (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
• Build a Machine Learning Model for Sentimental Analysis
• Model Evaluation
Topic 5: Sentiment 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.