sor8sh / Semisupervised-Sentiment-Analysis

Twitter semisupervised sentiment analysis with AFINN

Twitter Semisupervised Sentiment Analysis

This repository is made for the NLP course project - Apr 2018.

Dependencies:

• JSON
• AFINN
• scikit-learn
• NLTK
• Matplotlib

Dataset:

• AFINN-111
• Tweets of a twitter account

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A semisupervised sentiment analysis for tweets of a twitter account over time.

Steps:

• Collect all Tweets of an account in a json file with the following format:

{
  "source": "Twitter for iPhone",
  "text": "Some text",
  "created_at": "Sun Jul 08 21:58:52 +0000 2018",
  "retweet_count": 64399,
  "favorite_count": 183994,
  "is_retweet": false,
  "id_str": "1016079192604139520"
} 

• Use NLTK for Lemmatization and Tokenization.
• Based on AFINN dataset, each word is given a score, from +5 (very positive) to -5 (very negative).
• Use scikit-learn to calculate precision, recall, and f1-score.
• Use Matplotlib to plot a histogram of the sentiment analysis over time.