And the winner is... : Predicting the outcome of Melodifestivalen by analyzing the sentiment value of Tweets

University essay from KTH/Skolan för datavetenskap och kommunikation (CSC); KTH/Skolan för datavetenskap och kommunikation (CSC)

Author: Alexander Koski; Jennifer Persson; [2017]

Keywords: ;

Abstract: In a world where a lot of people post their feelings about things on social media, the interest in using sentiment analysis to be able to collect and understand these feelings has arisen. This thesis aims to investigate the possibility of predicting the outcome of a television competition, decided partly by the viewers’ votes, using sentiment analysis on tweets. The lexicon AFINN and a Swedish translated version of it was used for the lexical sentiment analysis of this report. After pre-processing the tweets gathered from Twitter with the competition's hashtag, the tweets were analysed and mapped to the different competitors. Each mapped tweet was scored with a sentiment value according to the lexicons. Six different predictions were derived from the sentiment values of the tweets. The predictions was compared to the real result of the competition using Kendall tau distance, where shorter distance indicates more similarities between the lists. The result show that it is possible to make a rough prediction of the outcome of the competition where the best prediction was achieved by ranking the top 5 artists based on the sum of positive sentiment value for the songs.

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