Tracking the ‘vibe’ of a soccer match

I am a HUGE fan of FC Barcelona. I try to watch every single regular season game and track the progress of their ups and downs. While watching, I became curious of what people were thinking during the game. I turned to Twitter and live chats to get a ‘vibe’ of the overall match. A lot of tweets contained information such as reactions to goals, injuries, and fouls. Also, people would comment on if Barcelona was playing well or not overall compared to their competition and form. Based on this information I would come to a conclusion on the overall ‘vibe’ or feeling of how Barcelona fans thought the game was going.

Build Time

After doing this a couple times, I decided to create an application that could analyze tweets from a specific match to obtain a ‘vibe’ automatically. I created a backend application that stores and updates match data from an external soccer API (https://www.football-data.org/index).Then, I created a service that reads in tweets based on a hashtag, analyzes the content via IBM sentiment analysis, and outputs the results. The IBM service analyzes the overall tone of tweets and returns a result from -1 to 1, where -1 is negative and 1 is positive. For instance, “FC Barcelona is terrible” scores -0.98 and “FC Barcelona is the best ever!” scores 0.95.