bazokizo/kenya-election

# Ruto vs Raila: Modelling Kenya general elections using BERT language model > In this ReadMe we'll try to summarize all the results that we have obtained throughout the process of our research ## About The Project Social media has played a crucial role in shaping the worldview during election campaigns. It has been used as medium for political campaigns and tool for organising protests, some of which have been peaceful and while others led to riots. There has been some research that indicates that understanding user behaviour particular in terms of sentiments expressed during elections can provide some indication of election outcome. Recently, there has been tremendous progress in the area of language modeling with deep learning via long short-term memory (LSTM) models and variants known as bidirectional encoder representations from transformers (BERT). Motivated by these innovations, we develop a framework to model the US general elections. In this paper, we use BERT language model for Twitter sentiment analysis leading to the US 2020 presidential elections. We investigate if sentiment analysis can provide an indication of the results outcome using canonical LSTM and BERT language models. Our results show that sentiment analysis can form a general basis for modelling election outcomes where the BERT model indicated Biden winning the elections. ## Dataset & Saved models - Data set (Oct 15th 2020 - Nov 8th 2020, 1.72M Tweets) #### Description ``` created_at: Date and time of tweet creation tweet_id: Unique ID of the tweet tweet: Full tweet text likes: Number of likes retweet_count: Number of retweets source: Utility used to post tweet user_id: User ID of tweet creator user_name: Username of tweet creator user_screen_name: Screen name of tweet creator user_description: Description of self by tweet creator user_join_date: Join date of tweet creator user_followers_count: Followers count on tweet creator user_location: Location given on tweet cre …