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footballers_performances's Introduction

ANALYZING PERFORMANCES AND SUPPORTERS’ OPINIONS

This academic project contains an analysis of how the supporter's sentiment is related to the player's performance (april - june 2023).

It includes:

  • Scraping from FotMob for the footballers' data
  • Scraping from Nitter (not available anymore) for the tweets about the footballers in selected time ranges. Dataset also available on Kaggle
  • Sentiment analysis of the tweets
  • Analysis of the relationship with dedicated models
  • Topic modelling of the tweets about 10 representative players

DISCLAIMER: The footballer's performances data were gathered from Fotmob.com and they have been collected for academic purpose only.

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footballers_performances's Issues

Topic modelling: pyLDAvis fix

Only old versions of the library support scikitlearn models.
To perform the topic modelling visualization ensure you installed the version of pyLDAvis indicated in requirements.txt.

Run topic_modelling_tweets and wait the error message that brakes the code.
Click to the last link given by the error message [File "C:\Users\feder\PycharmProjects\footballers_performances\lib\site-packages\pyLDAvis\sklearn.py", line 20, in _get_vocab] and change the function _get_vocab adding "_out" as follows:

def _get_vocab(vectorizer):
return vectorizer.get_feature_names_out()

Save and run again. You will get a new error. Click again on the last blue path given.
Change the code from head(R).drop('saliency', 1) to head(R).drop('saliency', axis = 1).

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