Performing Sentiment Analysis on Customer Reviews & Tweets

Description

Welcome to Performing Sentiment Analysis on Customer Reviews & Tweets course. This is a comprehensive project based course where you will learn step by step on how to conduct sentiment analysis and emotional detection on customer review and twitter post datasets using TextBlob, Natural Language Toolkit, and BERT models. This course is a perfect combination between theory and hands-on application, providing you with practical skills to extract valuable insights from textual data. This course will be mainly focusing on two major objectives, the first one is data analysis where you will explore the customer review and twitter post datasets from multiple perspectives, meanwhile the second objective is sentiment analysis where you will learn to detect emotions and bias from customer reviews and twitter posts. In the introduction session, you will learn the basic fundamentals of sentiment analysis, such as getting to know its practical applications and models that will be used in our projects. Then, in the next session, we are going to have a case study where you will learn how sentiment analysis actually works. We are going to use customer reviews dataset to perform feature extraction and make predictions if a review is more likely to be positive, negative, or neutral. Afterward, you will also learn about several factors that contribute to bias in customer reviews, for examples like algorithmic amplification, emotional bias, and financial incentives. After learning all necessary knowledge about sentiment analysis, we will begin the project. Firstly you will be guided step by step on how to set up Google Colab IDE. In addition to that, you will also learn how to find and download customer reviews and twitter post dataset from Kaggle. Once everything is all set, we will enter the main section of the course which is the project section. The project will consist of two main parts, in the first part, you will learn step by step on how to perform sentiment analysis on customer reviews dataset, you will extensively learn how to make accurate predictions whether the review indicates customer’s satisfaction or dissatisfaction based on the training data. Meanwhile, in the second part you will be guided step by step on how to perform sentiment analysis on twitter posts dataset, specifically you will analyse the emotional aspect of the tweets using Natural Language Toolkit.

First of all, before getting into the course, we need to ask ourselves this question: why should we learn sentiment analysis? Well, there are many reasons why, but here is my answer, with the rise of E-commerce and businesses starting to expand their market online, as a result, more and more customers are starting to purchase products online and after purchasing the product, most likely they will also leave reviews telling their opinions about the product. In addition to that, sometimes they also have meaningful discussions about a specific product on social media. However, not a lot of people realize that those customer reviews and social media posts can potentially be transformed into valuable insights for the business, for instance, by evaluating the complaints from the customers in the review section, the company will be able to make better business decisions and improve the quality of their products based on their customer suggestions.

Below are things that you can expect to learn from this course:

• Learn the basic fundamentals of sentiment analysis and its practical applications

• Case study: applying sentiment analysis on customer review dataset and predict if a review is more likely to be positive, negative or neutral

• Learn factors that contribute to bias in customer reviews

• Learn how to find and download datasets from Kaggle

• Learn how to clean dataset by removing missing rows and duplicate values

• Learn how to find correlation between customer ratings and sentiment

• Learn how to identify keywords that are frequently used in positive and negative customer reviews

• Learn how to analyse emotional aspect of customer reviews using EmoLex

• Learn how to perform sentiment analysis on customer review data using TextBlob

• Learn how to analyse emotional aspect of tweets using NRCLex

• Learn how to perform sentiment analysis on twitter post data using VADER

• Learn how to predict sentiment of a tweet using BERT

• Learn how to predict sentiment of a tweet using Multinomial Naive Bayes

• Learn how to set up Google Colab IDE

What You Will Learn!

• Learn how to perform sentiment analysis on customer review data using TextBlob
• Learn how to analyze emotional aspect of customer reviews using EmoLex
• Learn how to perform sentiment analysis on twitter post data using VADER
• Learn how to analyze emotional aspect of tweets using NRCLex
• Learn how to predict sentiment of a tweet using BERT
• Learn how to predict sentiment of a tweet using Multinomial Naive Bayes
• Learn how to identify keywords that are frequently used in positive and negative customer reviews
• Learn how to find correlation between customer ratings and sentiment
• Case study: applying sentiment analysis on customer review dataset and predict if a review is more likely to be positive, negative or neutral
• Learn factors that contribute to bias in customer reviews
• Learn how to clean dataset by removing missing rows and duplicate values
• Learn the basic fundamentals of sentiment analysis and its practical applications

Who Should Attend!

• People who are interested in performing sentiment analysis on customer reviews and tweets dataset
• People who are interested in detecting emotions using NRCLex

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