Text Mining and Analytics: Hotel Reviews Sentiment Analysis

Natural Language Processing    |    Intermediate
  • 11 Videos | 1h 7m 43s
  • Includes Assessment
  • Earns a Badge
Using natural language processing (NLP) tools, an organization can analyze their review data and predict the sentiments of their customers. In this course, we'll learn how to implement NLP tools to solve a business problem end-to-end. To begin, learn about loading, exploring, and preprocessing business data. Next, explore various linguistic features and feature engineering methods for data and practice building machine learning (ML) models for sentiment prediction. Finally, examine the automation options available for building and deploying models. After completing this course, you will be able to solve NLP problems for enterprises end-to-end by leveraging a variety of concepts and tools.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    demonstrate how to load the hotel review data and recognize its features
    install and import the required libraries and demonstrate data loading
    utilize Exploratory Data Analysis (EDA), topic modelling, sentiment analysis, and preprocessing of data
    demonstrate the use of WordCLoud and sentiment distribution
    build and evaluate simple NLP models
  • interpret the tuning of models for better results and outline their evaluation using different search methods
    deploy AutoML, PyCaret, and Streamlit models
    identify best practices for NLP projects across various industries
    compare challenges and deployment strategies for NLP projects across various industries
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 33s
    UP NEXT
  • Playable
    2. 
    Loading Hotel Reviews Data
    1m 29s
  • Locked
    3. 
    Installing Libraries and Data Loading
    7m 44s
  • Locked
    4. 
    Utilizing Exploratory Data Analysis (EDA)
    14m 56s
  • Locked
    5. 
    Exploring Linguistic Features of Data
    7m 53s
  • Locked
    6. 
    Building NLP Models
    8m 42s
  • Locked
    7. 
    Interpreting Model Tuning
    8m 3s
  • Locked
    8. 
    Deploying AutoML, PyCaret, and Streamlit Models
    13m 43s
  • Locked
    9. 
    NLP Project Best Practices
    1m 51s
  • Locked
    10. 
    NLP Project Challenges and Deployment Strategies
    1m 5s
  • Locked
    11. 
    Course Summary
    44s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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