Performing Sentiment Analysis on Customer Reviews & Tweets

🎓 **Performing Sentiment Analysis on Customer Reviews & Tweets: A Deep Dive into Textual Data Insights** --- ### **Course Headline:** 🧠✨ *Master Sentiment Analysis and Emotion Detection with TextBlob, NLTK, BERT, VADER, NRCLex, MultinomialNB!* --- ### **Welcome to the Course!** Dive into the world of data analysis where sentiment analysis is not just a buzzword but a skill that turns customer reviews and tweets into actionable business insights. This course is meticulously crafted to equip you with both theoretical knowledge and practical hands-on experience in sentiment analysis and emotional detection using state-of-the-art models and tools. 🌐📊 --- ### **What You'll Learn:** This comprehensive project-based course is designed to achieve two major learning objectives: 1. **Data Analysis:** Explore customer review and twitter post datasets from multiple perspectives, gaining a deeper understanding of the data you're working with. 2. **Sentiment Analysis:** Detect emotions and biases within customer reviews and tweets, learning to make informed predictions about sentiment. --- ### **Why Sentiment Analysis?** 🤔🚀 In today's digital age, where e-commerce flourishes and online marketplaces are the new frontier, understanding what customers truly think is invaluable. Sentiment analysis can transform customer reviews and social media chatter into strategic insights, enabling businesses to make data-driven decisions, refine products, and enhance customer satisfaction. 🛍️💡 --- ### **Course Outline:** **Introduction to Sentiment Analysis:** - Fundamentals of sentiment analysis and its real-world applications. - Understanding the models we'll be working with in our projects. **Case Study:** - Performing feature extraction on customer reviews to predict sentiment. - Analyzing the factors that introduce bias in customer feedback. **Setting Up Your Environment:** - Introduction to Google Colab IDE for a seamless project experience. - Steps to download customer reviews and twitter posts datasets from Kaggle. **Project Work:** - A step-by-step guide on performing sentiment analysis on customer reviews using TextBlob, focusing on accuracy in predicting customer satisfaction. 📈 - Analyzing the emotional aspect of tweets with Natural Language Toolkit (NLTK) and NRCLex. 💬 - Sentiment analysis on twitter posts data using VADER for a more nuanced understanding of Twitter sentiment. 🐦 - Predicting sentiments of tweets using BERT, the transformer model that understands context better than ever before. 🤖 - Utilizing Multinomial Naive Bayes to predict sentiment of tweets with another perspective. 🔍 **Data Preparation:** - Cleaning dataset by handling missing rows and duplicate values. - Identifying keywords in reviews that correlate with customer ratings. 🗝️ - Emotional aspect analysis using EmoLex for a deeper understanding of the emotional content in text. 🤩 --- ### **What You Can Expect to Master:** - The basics of sentiment analysis and its practical applications. - Conducting a case study on customer reviews to identify positive, negative, or neutral sentiments. - Understanding the biases present in customer feedback and how they influence sentiment analysis. - Finding and downloading datasets from Kaggle for analysis. - Data cleaning techniques to ensure the quality of your insights. - Identifying correlations between customer ratings and sentiment. - Keyword extraction in customer reviews to understand what drives customer satisfaction or dissatisfaction. 🗝️ - Emotional content analysis using EmoLex for a nuanced understanding of emotions in text. - Sentiment analysis on customer reviews with TextBlob for clear and actionable insights. - Emotional aspect detection using NRCLex for Twitter data. - Sentiment prediction of tweets using VADER, a lexicon and rule-based sentiment analysis tool. - Understanding and applying BERT models to predict the sentiment of tweets with high accuracy. - Applying Multinomial Naive Bayes for another approach to sentiment prediction on social media data. - Setting up and utilizing Google Colab IDE for a smooth and efficient working environment. 💻 --- Embark on this journey to become a sentiment analysis expert, ready to harness the power of textual data insights for your business or research endeavors! 🚀📚