TwitterSentiment is a Keyword based Twitter Sentiment Analyzer. It uses tweets based on the input provided by the user to generate a Rudimentry Sentiment Report.

https://twittersa-hyo.oa.r.appspot.com/

• TwitterSentiment
  • Dataset
  • Methodology
  • Docker Container
  • Usage
  • Dependencies

The Sentiment Model is trained on sentiment140 dataset. https://www.kaggle.com/kazanova/sentiment140

The dataset contains:

• 800k Positive Tweets
• 800k Negative Tweets

The 1.4 Million tweets are preprocessed using the following steps:

• LowerCase the letters
• Replacing the urls with "URL"
• Removing Usernames (@donaldtrump)
• Removing Special Characters i.e: Non-Alpha Numeric Characters
• Removing Stopwords
• Word lemmatization

The preprocessed tweets are then vectorized using Tf-idf. The vectorized tweets are used as a input for the Support Vector Machine Classifer.

⚠️ Docker Linux is needed for this!

docker pull realdexter/twitter_sentiment:v1

docker run -p local_port:8501 realdexter/twitter_sentiment:v1

local_port is the Port you want to map to the exposed port of the container.

Visit https://localhost:8501

⚠️ Model files not included in git repo.

git clone https://github.com/AsadAliDD/TwitterSentiment

pip3 install -r requirements.txt

streamlit run SentimentAnalysis.py

• Python3
• Streamlit
• Nltk
• Sklearn
• Pandas
• Numpy
• GetOldTweets3
• Plotly