When was the last time you checked the Amazon reviews of a product before buying it?
Have you used Twitter or Facebook to comment on how good or how bad it was?
This kind of data is extremely valuable to the companies who sell the product and also to social media organisations to detect toxic comments.
Natural language processing is one of the most popular applications of machine learning and deep learning. Since a majority of the data is actually unstructured, Natural language processing is necessary to understand it.
- Sentiment Analysis and Text classification are one of the initial tasks you will come across in your Natural language processing Journey.
- This Sentiment Analysis course is designed to give you hands-on experience in solving a sentiment analysis problem using Python.
- This course will also introduce you to the skills and techniques required to solve text classification/sentiment analysis problems.
- Sentiment Analysis itself is further used in chatbots, business intelligence, and in many more domains.
- A working laptop / desktop with 4 GB RAM
- A working Internet connection
- Basic knowledge of Machine Learning
- Basic knowledge of Python - check out this Course first, if you are new to Python
This is all it takes for you to learn sentiment analysis for text at scale.
What are you waiting for?
What is Sentiment Analysis?
- Sentiment Analysis or Opinion Mining is a technique used to analyse the emotion in a text. We can extract the attitude or the opinion of a piece of text and get insights on it.
- In the context of machine learning, you can think of Sentiment Analysis as a Classification problem where the text can either have a positive sentiment, a negative sentiment or a neutral one.
What are the applications of Sentiment Analysis in the industry?
- In the age of social media, it is extremely common to comment about
- a movie you liked or
- a book you didn’t like or
- a product you bought was not up to the mark.
- Therefore, a lot of companies use sentiment analysis for their products since it provides direct feedback of the customer’s opinion.
- It is also important to detect and remove hateful content from social media and companies like Twitter, Facebook, etc. extensively use sentiment analysis on a daily basis.
On what kind of projects would I implement sentiment analysis?
There are a wide variety of projects where you can use Sentiment Analysis. Here are a couple of popular use cases:
- Sentiment Analysis can not only be used for customer reviews or product feedback, but in other domains as well.
- Analyzing the sentiments on social media on the US Elections, for example, gives useful insights on which candidates are favoured by the public and which are not.
For other interesting problems involving sentiment/emotion detection, you can visit: https://datahack.analyticsvidhya.com/contest/all/
What is the range of sentiments that can be observed and analysed?
- In the earlier days of Natural language processing and Sentiment Analysis, the sentiments could hold only 2 or 3 values: Positive or Negative, and Positive, Neutral or Negative.
- However, with the advent of deep learning, we can now recognize the subtle emotions in a text as well.
- This has made tasks like Sarcasm detection, fake news detection etc. popular in research areas of Natural language processing
I already understand sentiment analysis. What should be the next step in my learning path?
Once you are familiar with Twitter Sentiment Analysis, you can move on to more advanced concepts in Natural language processing. To further explore Natural language processing, you can enroll for this course: https://courses.analyticsvidhya.com/courses/natural-language-processing-nlp
Can I add this project to my resume and use it in my Interview?
- Sentiment Analysis is one of the most popular applications of Machine Learning and Classification in Natural language processing
- We also encourage you to take up more diverse datasets and apply sentiment analysis on them.
- Sentiment Analysis is also one of the first projects you would learn in your Natural language processing journey and as such is commonly asked in interviews.
- Overview of the Course
- Understand the Problem Statement
- Table of Contents
- Loading Libraries and Data
- Data Inspection
- Data Cleaning
- Story Generation and Visualization from Tweets
- Bag-of-Words Features
- TF-IDF Features
- Word2Vec Features
- Logistic Regression
- Support Vector Machine (SVM)
- FineTuning XGBoost + Word2Vec
I really appreciate the effort giving to provide the course in the simple way. It really gives me a clear picture about SA. Thank you so much ,
I really appreciate the effort giving to provide the course in the simple way. It really gives me a clear picture about SA. Thank you so much ,Read Less
At the end during XGBoost+Word2Vec, more explanation could have been provided.
At the end during XGBoost+Word2Vec, more explanation could have been provided.Read Less
Good workRead Less
I have keen interest to work on sentiment analysis not only on English language but Urdu and Hindi as well
I have keen interest to work on sentiment analysis not only on English language but Urdu and Hindi as wellRead Less
A very good course to work on the basic of Natural language processing with well crafted steps from basic to model building and evaluation.
A very good course to work on the basic of Natural language processing with well crafted steps from basic to model building and evaluation.Read Less
Who should take this course?
This course is for people who want to learn Sentiment Analysis and Text Classification using Python.
Do I need to install any software before starting the course?
You should have Python and Jupyter Notebook installed on your system.
How long would I have access to “Twitter Sentiment Analysis” course?
Once you register, you will have a 6-month access to complete the course. If you visit the course 6 months after your initial registration - you will need to enroll in the course again. Your past progress will be lost.
Do I need to take the modules in a specific order?
We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.
Do I get certificate upon completion of the course?
This is a free course and therefore there is no certificate involved.
What is the fee for this course?
The course is free of charge.
Is there any placement support?
This is an introductory course and this does not include any placement support. Once you have worked on a few data science projects and hackathons, you can always apply to jobs on Analytics Vidhya portal.
How can I apply and test my learnings about Sentiment Analysis?
You can start by doing the tests at the end of various chapters. In addition, you can enrol for the Natural Language Processing (NLP) Using Python to continue your Natural language processing Journey.
How much effort will this course take?
You can complete "Twitter Sentiment Analysis" course in a few days. You are also expected to apply the concepts explained and apply them on the data yourself. The time taken in projects varies from person to person.
Support for Twitter Sentiment Analysis course can be availed through any of the following channels: