Directly or indirectly the outcomes of every election affect every citizen of a country. Elections are an interesting thing to analyze since many people would like to know about matters surrounding their favourite candidates.Therefore , this prejoct seeks to show what exactly it is that Nigerians want from their leaders as well as predicting the winning candidate and by what margin they win.
Table of Contents
- About The Project
- Built With
- Getting Started
- Prerequisites
- Installation
- Usage
- Roadmap
- Contributing
- Contact
- Acknowledgments
- Visualization
This project is divided into two parts Firstly, Analysing qualities of a good leader, An analysis done through people’s sentiments was done to know what people are saying concerning each candidate
The other part i analyzed which presidential candidate will win and by what margin. This part will not be for public consumption since it is very sensitive. It is intended that this part of the project will be handed to a NGO so that they can integrate it in their analysis. Access to this information will be restricted to a few people.
Python
Jupyter notebook
Tweepy
Scikit-Learn
Seaborn
streamlit
To get a local copy up and running follow these simple example steps. Prerequisites
The prerequisites needed to successfully run this project are:
Twitter API and Elevated Access.
Twitter API and having an Elevated Developer Account helps scrape data from twitter.
Installation
Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.
Get a Twitter API at Twitter API
Clone the repo
Load all the datasets in your environment
Install all the libraries used in the project
This project is purely meant for self development. The Analyst has no political affiliation whatsoever
This project is divided into 2 notebooks. It follows the following order:
Elections-Analysis,K-Means-Clustering and
Presidential-prediction
Analyze, through people’s sentiments, what are the key things that the people wish most from their leaders. Predict which presidential candidate wins the election and by what margin. And also to determine if the candidate will win in the first round. Clustering the tweets to analyze the words that surround a particular presidential candidate
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
Fork the Project
Create your Feature Branch
Commit your Changes
Push to the Branch
Open a Pull Request
We found the following resources and people helpful and would like to give credit to.
Stackoverflow
Towards Data Science
Twitter Data Community
Kiprop Amos : [TWITTER](https://twitter.com/AmosKiprop15 - amoskiprop5@gmail.com)
Esther Ogutu : [Twitter](https://twitter.com/ogutu_esther - esther.ogutu@gmail.com]
This is the most mentioned aspirants in the scrapded tweets,Eventhough many of the aspirants are called by various names we were able to assign those names to thier official office name Presidential Aspirant Peter obi has has the highest Trajectory in terms of polarity. Peter obi has the highest numberof positve and negative sentiments towards him than the other aspirants by twitter users.
In terms of mentions , Here is the percentage
Percentage of how many times a party is being mentioned in tweets between the 7days period
By assuming that the positive sentiments a presidential aspirant gets equals to support and the negative sentiments gets equals to support of the opponnent, the following results were obtained IN PERCENTAGE;
After thorough analysis and scraping of tweets for several days it is visible that Nigeria presidential election is a tough debate on twitter and many users of this media have been ditching out their own personal opinions without holding back
From our analysis , Peter Obi was the most polarized candidate,having the highest number of both positive and negative sentiments
It was also shown from this analyssis that Peter Obi is the most mentioned political figure in the country while Atiku is the least mentioned
From our election prediction results, factoring in neutral voters as undecided voters, it was concluded that a large part of the electorate is still undecided on who to vote for in the forth coming election.
Without the undecided voters, from the analysis, it was concluded that Peter Obi will most likely win the election in the first round with slightly over 43% of the votes.
This prediction is made using only sentiments sourced from social media hence is not fully representative of Nigeria's electorate