Sentiment Analysis Using Python

Mayank Rasu, Experienced Quant Researcher

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14 Lessons (2h 20m)
    • 1. Why Sentiment Analysis

      6:00
    • 2. Sentiment Analysis - Intuition

      8:09
    • 3. Natural Language Processing Basics

      17:16
    • 4. Lexicon Based Sentiment Analysis

      6:36
    • 5. VADER Introduction

      11:35
    • 6. Textblob Introduction

      7:41
    • 7. Building a Sentiment Analyzer using VADER - Part I

      10:35
    • 8. Building a Sentiment Analyzer using VADER - Part II

      18:21
    • 9. Machine Learning Based Sentiment Analysis

      12:24
    • 10. ML Feature Matrix & TF-IDF Introduction

      9:50
    • 11. Building a ML Based Sentiment Analyzer - Part I

      3:16
    • 12. Building a ML Based Sentiment Analyzer - Part II

      16:59
    • 13. Building a ML Based Sentiment Analyzer - Part III

      6:31
    • 14. Sentiment Analysis Application - Opportunities & Challenges

      4:56

About This Class

Build an automated system for analyzing text documents and finding the polarity of the documents. This course delves into the evolving area of sentiment analysis. The course starts with the basics of sentiment analysis and natural language processing and covers both lexicon based approach and machine learning based methods of sentiment analysis. By the end of this course you will be conversant with popular python libraries such as NLTK, VADER, TextBlob and Sklearn and should be able to build a sophisticated sentiment analyzer with reasonable accuracy.

You can expect to gain the following skills from this course

  • Data mining using web-scraping

  • Natural language processing basics

  • Lexicon based sentiment analysis

  • Machine learning based sentiment analysis

  • Using VADER and TextBlob libraries to perform sentiment analysis

  • Naive Bayes algorithm

  • Building machine learning based sentiment analyzer

Course image by courtesy of rawpixel.com

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