GitHub - aaryaab/Twitter-Sentiment-Analysis: Tweets are cleaned using concepts of NLP and various ML models are applied for predictions.

aaryaab / Twitter-Sentiment-Analysis Public

Notifications You must be signed in to change notification settings
Fork 0
Star 0

Tweets are cleaned using concepts of NLP and various ML models are applied for predictions.

Notifications

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
ReadMe.txt		ReadMe.txt
twitter.csv		twitter.csv
twitter_1.ipynb		twitter_1.ipynb

Repository files navigation

Twitter Sentiment Analysis

0: Positive Tweet
1: Negative Tweet

The project flow is as follow:

1 Data Cleaning
  - Data Preprocessing: In here tweets are cleaned and few concepts of     Natural Language Processing are used like Stemming.
2 Data Analysis and Visualization
  - WordClouds are generated of all the tweets, positive tweets and     negative tweets
  - Tweets are also analysed by the hashtags used, and a bargraph is     made for comparing their frequency
3 Data Modeling
   - Logistic Regression
   - Naive Bayes
   To improve the score, Bag of Words and TFIDF Vectorizer is used and    models are applied
   - Logistic Regression
   - Naive Bayes
   - Support  Vector Machine
   - Random Forest Classifier