Twitter Political Leanings

Contents

• Stages of Construction
  • Stage 1 - Simple Neural Network

Stages of Construction

Throughout the construction of the project, mutliple "stages" of neural networks and datasets were created, ranging from simple to more complex designs in order to increase accuracy. The stages are outlined as follows:

Stage 1 - Simple Neural Network

• Stored in simple branch
• First, a list of senators was created on Twitter
  • Multiple Twitter handles were included per politician if available, as long as they were verified accounts
  • Stored in politicians/politicians.json
    • Political party listed, along with Twitter handle
• From this list, the most common words were received
  • Max of 100 Tweets per handle
  • Does not include "stop words" (common words such as 'the' or 'an')
  • Words were deleted by hand that had symbols or are too common/unrelated to politics
  • Stored as list in common_words/words.json
• Training data was generated using these common words
  • Again, a max of 100 Tweets were used per handle
  • An array was created for each Tweet with a 1 indicating the common word was found, and 0 indicating otherwise
    • Same order as common_words/words.json
  • Arrays that contained 5+ of the common words were stored in dataset/data.json
    • Classified under Republicans or Democrats (based on user's party)
• Neural Net
  • Feed forward, 1 hidden layer
  • generic settings (binary crossentropy and adam)
  • Results on test data:
    • Loss: 2.547445950237973
    • Accuracy: 0.6910408433561112