Data Science
Sentiment Analysis of Twitter using NL toolkit in Python
Make the internet a safer, more inclusive space for everyone by designing a sentiment analysis model that makes Twitter free of hate and bias!
About this Menternship

Pepper Content brings together the intelligence of humans and machines to create exemplary content for the global market. If you are a writer, Pepper Content has a paid gig for you! And, if you are a company, Pepper can connect you with the best writing talent in the world. Peppertype ai is a product of the company that allows writers to harness the power of AI to generate quick content! Pepper Content is committed to disrupting the world of content creation, and in the process make the internet a kinder, nicer place for companies and humans alike.

6000 people take to Twitter every minute to express an opinion, or share a piece of information. A number of these tweets can be problematic, because they would incite hate and prejudice against a section of the society, or worse - spread false information about critical global issues like the Coronavirus pandemic. It is the task of a data science team at Twitter to create product policies which enable Twitter algorithms to flag and mark problematic tweets - making the platform safer for its user. In this menternship, you will be tasked with creating your own version of a sentiment analysis algorithm that can regulate problematic content on a social media platform.

In this menternship, you will develop a sentiment analysis algorithm to identify and flag problematic Tweets on Twitter
Why take up this Menternship?
On completing this Menternship, you will learn about
You will explore: 
Sentiment Analysis
Naive Bayes Algorithm
SVM (Support Vector Machines)
Logistic Regression

You will apply:
Data cleaning
Data Visualisation
Data Transformation using TF-IDF

You will create:
A Twitter sentiment analysis model that identifies the sentiments of the tweets using various ML Algorithms.

Expected Output
  1. Research Report - Hate speech online, and the role of NLP toolkit in combating it
  2. Report containing the screenshots and one-line summaries of the charts (Target variable) and the summaries of the EDA.
  3. Complete Codes for all previous components
  4. Screenshots of Data Visualisation
  5. Final Report on Findings along the process
  6. Submit your video
Sentiment Analysis of Twitter using NL toolkit in Python
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