Project completed as dissertation for Masters degree.
This project aimed to determine the extent supervised machine learning techniques could be used to predict the accuracy of tweets in relation to football transfers.
IMPORTANT: ALL CODE CAN BE TAILORED TO YOUR SPECIFIC TWEET GATHERING, LABELLING AND CLASSIFICATION NEEDS.
Project three distinct sections.
- Stage 1: Data Gathering
- Stage 2: Data Labelling
- Stage 2: Classification Experiments
| module | stage | description |
|---|---|---|
| kb_builder.py | 1 | build database of known transfers |
| get_tweets.py | 1 | get transfer tweets |
| db.py | 1/2/3 | build db collections |
| relations.py | 1 | create queries for GetOldTweets |
| ner.py | 1/2/3 | label tweets using Named Entity Recognition (NER) |
| data_checking.py | 1/2/3 | script for checking db values etc |
Tweet gathering done using GetOldTweets. Free workaround to Twitters expensive API. Clone this repo into top directory.
Two approaches, as implemented in classification.ipynd:
- td-idf encoded n-grams of tweets for input features. Simple MLP model.
- Word vectorisation, semantics embedding layer, Separated Convolution Neural Network.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent