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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.

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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)

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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.

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