https://github.com/sayamalt/twitter-sentiment-analysis

Successfully established a machine learning model which can accurately classify the sentiment of any particular tweet into either positive, negative or neutral category.
https://github.com/sayamalt/twitter-sentiment-analysis

data-visualization exploratory-data-analysis nlp sentiment-analysis supervised-learning text-processing

Last synced: 8 months ago
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Successfully established a machine learning model which can accurately classify the sentiment of any particular tweet into either positive, negative or neutral category.

• Host: GitHub
• URL: https://github.com/sayamalt/twitter-sentiment-analysis
• Owner: SayamAlt
• Created: 2022-10-11T19:29:21.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-11-13T10:30:16.000Z (over 3 years ago)
• Last Synced: 2024-12-28T08:09:41.674Z (over 1 year ago)
• Topics: data-visualization, exploratory-data-analysis, nlp, sentiment-analysis, supervised-learning, text-processing
• Language: Jupyter Notebook
• Homepage:
• Size: 9.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Twitter-Sentiment-Analysis

Successfully established a machine learning model which can accurately classify the sentiment of any particular tweet into either positive, negative or neutral category.

![Twitter Sentiment Analysis](https://i.ytimg.com/vi/ujId4ipkBio/maxresdefault.jpg)

![Twitter Sentiment Analysis](https://i.ytimg.com/vi/pgZcP852dMg/maxresdefault.jpg)

## Dataset Used

Link: https://www.kaggle.com/datasets/cosmos98/twitter-and-reddit-sentimental-analysis-dataset

## Context

The dataset was created as part of a university project on Sentimental Analysis On Multi-Source Social Media Platforms using PySpark. It comprises tweets from Twitter along with their sentimental label.

## Content

The dataset contains about 163K tweets along with their respective sentiment labels. Overall, the dataset consists of 2 columns, the first column has the cleaned tweets and the second one indicates its sentimental label.

## Technologies Used



  
Numpy

  
Pandas

  
Seaborn

  
Matplotlib

  
NLTK

  
SymSpellPy

  
CatBoost

  
Scikit-learn

  
LightGBM



## Acknowledgements

This Dataset was created with the help of Tweepy Apis.