Handling uncertainty in relation extraction: a case study on tennis tournament results extraction from tweets

Relation extraction involves different types of uncertainty due to the imperfection of the extraction tools and the inherent ambiguity of unstructured text. In this paper, we discuss several ways of handling uncertainties in relation extraction from social media. Our study case is to extract tennis games’ results for two Grand Slam tennis tournaments from tweets. Analysis has been done to find to what extent it is useful to use semantic web, domain knowledge, facts repetition, and authors’ trustworthiness to improve the certainty of the extracted relations

Twitter Sentiment Analysis on Leicester City\u27s Phenomenal 2015/16 EPL Title Winning Season

Microblogging has become one of the most useful tools for sharing everyday life events and news, especially popular sporting events, and for expressing opinions about those events. The English Premier League (EPL), the most popular professional soccer league in the world, is talked about on Twitter every day, and the 2015/16 season, whose title underdogs Leicester City managed to win, was one for the history books to remember. As Twitter posts are short and constantly being generated, they are a great source for providing public sentiment towards events that occurred throughout the 2015/16 EPL season. In this project, we examine the effectiveness of machine learning and text sentiment analysis on classifying the sentiment of tweets about Leicester City. We accomplish this by collecting tweets containing the words “Leicester City” using the python library GetOldTweets3; manually labelling those tweets as positive, negative, or neutral; and training an SVM classifier to classify tweets about Leicester City from the 2015/16 season. Our model achieved an F1-score of 0.76. We use the sentiments returned from the classifier to find correlations between real-life events and sentiment changes throughout the whole season and during individual games. From our analysis, we discovered an increase in tweets about Leicester City but a sentiment change from positive to negative as the season progressed. We also observed a wide range of changes in sentiment during a single match involving Leicester City due to real-life events as well as other factors which we discuss in detail

Towards Commentary-Driven Soccer Player Analytics

Open information extraction (open IE) has been shown to be useful in a number of NLP Tasks, such as question answering, relation extraction, and information retrieval. Soccer is the most watched sport in the world. The dynamic nature of the game corresponds to the team strategy and individual contribution, which are the deciding factors for a team’s success. Generally, companies collect sports event data manually and very rarely they allow free-access to these data by third parties. However, a large amount of data is available freely on various social media platforms where different types of users discuss these very events. To rely on expert data, we are currently using the live-match commentary as our rich and unexplored data-source. Our aim out of this commentary analysis is to initially extract key events from each game and eventually key entities like players involved, player action and other player related attributes from these key events. We propose an end-to-end application to extract commentaries and extract player attributes from it. The study will primarily depend on an extensive crowd labelling of data involving precautionary periodical checks to prevent incorrectly tagged data. This research will contribute significantly towards analysis of commentary and acts as a cheap tool providing player performance analysis for smaller to intermediate budget soccer club

Embed2Detect: temporally clustered embedded words for event detection in social media

Social media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%

Towards Commentary-Driven Soccer Player Analytics

Open information extraction (open IE) has been shown to be useful in a number of NLP Tasks, such as question answering, relation extraction, and information retrieval. Soccer is the most watched sport in the world. The dynamic nature of the game corresponds to the team strategy and individual contribution, which are the deciding factors for a team’s success. Generally, companies collect sports event data manually and very rarely they allow free-access to these data by third parties. However, a large amount of data is available freely on various social media platforms where different types of users discuss these very events. To rely on expert data, we are currently using the live-match commentary as our rich and unexplored data-source. Our aim out of this commentary analysis is to initially extract key events from each game and eventually key entities like players involved, player action and other player related attributes from these key events. We propose an end-to-end application to extract commentaries and extract player attributes from it. The study will primarily depend on an extensive crowd labelling of data involving precautionary periodical checks to prevent incorrectly tagged data. This research will contribute significantly towards analysis of commentary and acts as a cheap tool providing player performance analysis for smaller to intermediate budget soccer club

Plataforma de agregação e análise de informação institucional e nas redes sociais: uma aplicação às camadas jovens de futebol

Atualmente assistimos a uma expansão do futebol português, com diversos jogadores a vingar nos melhores clubes europeus e a serem transferidos por verbas estratosféricas. Alguma da imprensa desportiva existente, tanto em papel como em formato eletrónico, não se dedica a aprofundar o futebol nas camadas jovens por não ter uma grande visibilidade junto do público, tal como tem o futebol sénior e, hoje em dia, até mesmo o futebol feminino. Com a identificação deste problema nas camadas jovens e a análise feita a diversos sites, tanto institucionais como jornalísticos e informativos, surgiu a necessidade de agregar informação dispersa sobre os jovens jogadores de futebol em Portugal. Tendo em conta que as redes sociais estão bastante presentes no nosso quotidiano e são usadas com frequência pelos clubes, jogadores e adeptos de futebol, este tipo de informação revelou-se essencial para o desempenho dos jogadores. Perante este contexto, foi desenvolvida uma plataforma que dará assim mais conteúdo e informação relativamente às camadas jovens de futebol, através de uma abordagem que permite a utilização da plataforma em diversos dispositivos de tamanhos diferentes. Com os testes realizados à plataforma, concluiu-se que a agregação de informação feita é útil e aplicável às camadas jovens de futebol, garantindo em poucos segundos o acesso a dados retirados de várias fontes, o que contribui para a melhoria do futebol e dos clubes em Portugal, mais concretamente clubes amadores que não têm possibilidades financeiras para investir em tecnologia avançada de recolha de informação e análise estatística.We are currently witnessing an expansion of football in Portugal, with several key players thriving in the best European clubs and being transferred by massive compensations. Some of the existing sports press, both in paper and electronic format, are not open to increase football knowledge among the youth players because they do not have a great visibility with the public, as does senior football and nowadays, even women's football. With the identification of this problem among youth teams and the analysis made on several websites, both institutional and journalistic/informative, the need to aggregate dispersed information about youth football players in Portugal was detected. Taking into account that social networks are very present in our daily lives with football clubs, players and supporters using them frequently, the information contained proved to be essential for the performance of the players. In this context, a platform has been developed which will provide more content and information regarding youth football players, through an approach that allows the use of the platform in different devices of different sizes. With the tests carried out on the platform, it was observed that the aggregation of the information obtained is useful and applicable to youth football players, guaranteeing almost instant access to data taken from various sources, which contributes to the improvement of football and its clubs in Portugal, more specifically, amateur clubs that do not have the financial resources to invest in advanced technology for the sourcing of information and statistical analysis

Cartoons as interdiscourse : a quali-quantitative analysis of social representations based on collective imagination in cartoons produced after the Charlie Hebdo attack

The attacks against Charlie Hebdo in Paris at the beginning of the year 2015 urged many cartoonists – most professionals but some laymen as well – to create cartoons as a reaction to this tragedy. The main goal of this article is to show how traumatic events like this one can converge in a rather limited set of metaphors, ranging from easily recognizable topoi to rather vague interdiscourses that circulate in contemporary societies. To do so, we analyzed 450 cartoons that were produced as a reaction to the Charlie Hebdo attacks, and took a quali-quantitative approach that draws both on discourse analysis and semiotics. In this paper, we identified eight main themes and we analyzed the five ones which are anchored in collective imagination (the pen against the sword, the journalist as a modern hero, etc.). Then, we studied the cartoons at figurative, narrative and thematic levels thanks to Greimas’ model of the semiotic square. This paper shows the ways in which these cartoons build upon a memory-based network of events from the recent past (particularly 9/11), and more generally on a collective imagination which can be linked to Western values.SCOPUS: ar.jinfo:eu-repo/semantics/publishe