Soccer Events Summarization by Using Sentiment Analysis

Andaloussi, Said Jai; Mourabit, Imane El; Madrane, Nabil; Chaouni, Samia Benabdellah; Sekkaki, Abderrahim

doi:10.1109/csci.2015.59

Cited by 11 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Certainly, one of the work main objectives is the capacity to analyze the sentiment of every highlight moment's tweets and this by measuring the positivity rate for each one of them by category (Positive, Negative and neutral). After using the KDD process that provided us with useful information of the big data recovered previously [24].…”

Section: Methodsmentioning

confidence: 99%

Generating a Highlight Moments Summary Video of Apolitical Event using Ontological Analysis on Social Media Speech Sentiment

Mehdi¹,

Nsiri²,

Serhane³

et al. 2019

ijacsa

View full text Add to dashboard Cite

Numerous viewers choose to watch political or presidential debates highlights via TV or internet, rather than seeing the whole debate nowadays, which requires a lot of time. However, the task of making a debate summary, which can be considered neutral and does not give out a negative nor a positive image of the speaker, has never been an easy one, due to personal or political beliefs bias of the video maker. This study came up with a solution that generates highlights of a political event, based on twitter social network flow. Twitter streaming API is used to detect an event's tweets stream using specific hashtags, and detect on a timescale the extreme changes of volume of tweets, which will determine the highlight moments of our video summary at first, then a process is set up based on a group of ontologies that analyze each tweet of these moments to calculate the percentage of each sentiment's positivity, then classify those moments by category (positive, negative or neutral).

show abstract

Section: Methodsmentioning

confidence: 99%