kkverma / Twitter-Sentiment-Analysis

A basic machine learning model built in python jupyter notebook to classify whether a set of tweets into two categories: racist/sexist non-racist/sexist.

Twitter-Sentiment-Analysis

A basic machine learning model built in python jupyter notebook to classify whether a set of tweets into two categories:

• racist/sexist
• non-racist/sexist

What is Sentiment Analysis?

Sentiment analysis (also known as opinion mining) is one of the many applications of Natural Language Processing. It is a set of methods and techniques used for extracting subjective information from text or speech, such as opinions or attitudes. In simple terms, it involves classifying a piece of text as positive, negative or neutral.

Steps of Data Science Analysis

1. Understand the Problem Statement

2. Tweets Preprocessing and Cleaning

   • Data Inspection
   • Data Cleaning

3. Story Generation and Visualization from Tweets

4. Extracting Features from Cleaned Tweets

   • Bag-of-Words
   • TF-IDF
   • Word Embeddings

5. Model Building: Sentiment Analysis

   • Logistic Regression
   • Support Vector Machine
   • RandomForest
   • XGBoost

6. Model Fine-tuning

7. Summary

Tools

• Anaconda
• Jupyter Notebook
• Python Libraries(pandas,Numpy,seaborn matplotlib,re..etc)