Sentiment Analysis for the Football Clubs Using Twitter Data

This paper provides a thorough examination of sentiment analysis of Twitter data, with a specific emphasis on the methodologies and approaches employed to extract sentiment information from tweets. The objective of this study was to do sentiment analysis on Twitter data pertaining to two globally renowned football clubs, FC Barcelona and Real Madrid. The study was conducted utilizing the R programming language, employing the twitteR package, as well as the dplyr, purrr, plyr, stringr, and tibble libraries. A function named “score value” was developed to conduct sentiment analysis. It accepts phrases as input, along with two-word lists that comprise positive and negative words. The function thereafter computes the emotion score for each sentence by considering the quantity of positive and negative words contained within the sentence. Following the loading of necessary libraries and establishment of OAuth authorization, Twitter data was gathered by utilizing the user Timeline () function for FC Barcelona and Real Madrid. The gathered data was subsequently transformed into data frames with the map_df () function from the purrr package. The sentiment scores of the collected tweets were calculated using the “score value” function and presented as histograms. Employing sentiment analysis can provide valuable insights into the current public sentiment towards football teams, so allowing these clubs to improve their brand image and establish stronger connections with their fan base.