Free Download Flipkart Review Sentiment Analysis & Spam Comments Detection
Published 4/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 11m | Size: 461 MB
Complete Flipkart Review Sentiment Analysis & Spam Comments Detection
What you'll learn
you'll learn how to leverage machine learning techniques to analyze sentiments
Handle data cleaning tasks such as removing duplicates, handling missing values, and tokenizing text.
Experiment with different algorithms and evaluate their performance using appropriate metrics.
Learn approaches to interpret and explain model predictions in the context of sentiment analysis.
Requirements
Familiarity with data preprocessing and machine learning libraries such as scikit-learn.
Access to a computer with internet connectivity and Python environment setup.
Description
Course Title: Flipkart Review Sentiment Analysis & Spam Comments Detection
Course Description
Welcome to the "Flipkart Review Sentiment Analysis & Spam Comments Detection" course! In this hands-on course, you'll delve into the fascinating world of natural language processing (NLP) and machine learning by exploring how to analyze sentiment in product reviews on Flipkart, one of India's leading e-commerce platforms. You'll learn how to build robust models to classify reviews into positive, negative, or neutral sentiments, as well as detect spam comments, helping businesses gain valuable insights from customer feedback.
What You Will Learn
Introduction to Sentiment Analysis
Understand the importance of sentiment analysis in extracting insights from text data.
Learn about the applications and challenges of sentiment analysis in real-world scenarios.
Flipkart Review Data Collection
Explore methods to collect Flipkart product reviews using web scraping techniques.
Learn how to extract relevant information from CSV and organize it for analysis.
Preprocessing and Text Cleaning
Dive into text preprocessing techniques to clean and normalize review data.
Remove noise, handle special characters, and tokenize text for further analysis.
Feature Extraction and Vectorization
Understand the concept of feature extraction and vectorization in NLP.
Sentiment Analysis Models
Learn how to build machine learning and deep learning models for sentiment analysis.
Experiment with algorithms such as Naive Bayes
Evaluation Metrics for Sentiment Analysis
Explore evaluation metrics such as accuracy, precision, recall, and F1-score for model performance assessment.
Understand how to interpret confusion matrices and ROC curves in the context of sentiment analysis.
Spam Comments Detection
Learn techniques to identify and filter out spam comments from product reviews.
Implement rule-based and machine learning approaches to detect spam patterns and outliers.
Model Deployment and Integration
Explore methods to deploy sentiment analysis models in production environments.
Integrate models with web applications or APIs for real-time sentiment analysis.
Performance Optimization and Scalability
Learn strategies to optimize model performance and scalability for handling large volumes of data.
Explore techniques such as batch processing and parallel computing.
Ethical Considerations and Best Practices
Discuss ethical considerations in sentiment analysis, including privacy concerns and bias mitigation.
Learn best practices for responsible data collection, model development, and deployment.
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Why Enroll
Practical Application: Gain hands-on experience by working with real-world Flipkart review data.
Project-Based Learning: Build end-to-end sentiment analysis and spam detection models from scratch.
Career Advancement: NLP and sentiment analysis skills are in high demand across various industries, offering opportunities for career growth and specialization.
Embark on this exciting journey into sentiment analysis and spam detection with Flipkart reviews, and gain valuable insights from customer feedback. Enroll now to enhance your NLP skills and become proficient in extracting actionable insights from textual data!
Who this course is for
Developers looking to integrate sentiment analysis capabilities into their applications.
Data enthusiasts interested in applying machine learning to analyze textual data.
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