Emotional Tendency Analysis of Twitter Data Streams

The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysi

STANDER: An expert-annotated dataset for news stance detection and evidence retrieval

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The British government, the newspapers and the German problem 1937-1939

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

European atlas of democratic deficit

The article discusses the implications of Panama Papers on the 2017 general election in Malta.peer-reviewe

Looking for the causal values of as and since in large corpora, and how these values compare with each other

This article deals with English causal subordinate clauses introduced by since or as. Both these markers may convey different meanings according to contextual variations, and can express temporal relations, from which their causal value is derived. The semantic closeness between as and since whenever they express a causal relation makes it necessary to harvest a large number of attested examples in order to compare and contrast them. The recourse to large on-line corpora such as the British National Corpus gives rise to specific practical difficulties, however, especially as far as as is concerned; because of the high frequency of the subordinators in question, one is confronted to thousands of examples, few of which turn out to have a clear causal value in the case of as, hence the recourse to other means in order to make up a large enough corpus of examples.The concept of presupposition is examined, as it has often been argued that causal since subordinates, unlike as subordinates, introduce a presupposed content. But when confronted to a large number of examples, this criterion falls short of accounting for the subtle differences between the two conjunctions. A more theoretical approach is required; thanks to the tools provided by A. Culioli’s Theory of Enunciative and Predicative Operations, it becomes possible to formalise the hypothesis that a since causal relationship is presented as unproblematic and is addressee-oriented, whereas an as causal relationship is felt as unproblematic, and is speaker-oriented.This difference can be felt in the fact that it seems to be easier to find examples of causal as clauses in the press, while the causal use of since is more widespread in general, and does not seem to be specific to a genre in particular. In order to put this impression to the test, I have resorted to the multi-lingual parallel corpus Intercorp.Nous étudions dans ce travail les propositions causales introduites par les subordonnants since ou as en anglais. Ces marqueurs peuvent avoir des significations différentes en fonction du contexte et leur valeur causale dérive de leur valeur temporelle. La proximité sémantique entre eux lorsqu’ils expriment la cause rend indispensable de travailler sur de nombreux exemples authentiques, afin de pouvoir les comparer et mettre au jour leurs différences. Néanmoins, dans ce cas précis, et plus particulièrement en ce qui concerne as, le fait d’utiliser de grands corpus disponibles en ligne, tel le British National Corpus, pose des problèmes pratiques bien spécifiques. En effet, en raison de la grande fréquence de ces conjonctions, on se trouve confronté à des milliers d’exemples dont un très petit nombre seulement illustre la valeur causale, surtout en ce qui concerne as, d’où la nécessité d’avoir recours à d’autres moyens afin de constituer un corpus d’exemples suffisant.Nous examinons le concept de « présupposition », dans la mesure où il a souvent été écrit que le contenu des subordonnées causales en since serait préconstruit, contrairement à ce qui se passe avec as. Néanmoins, ce critère est loin d’être suffisant pour expliquer les subtiles nuances entre l’emploi de as ou bien de since avec une valeur causale dans un grand nombre d’exemples. En ayant recours au cadre théorique et aux outils de la Théorie des Opérations Enonciatives d’A. Culioli, nous proposons d’envisager since comme le marqueur d’une relation causale présentée comme non problématique et prenant en compte le co-énonciateur, tandis que as exprime pour sa part une relation causale ressentie par l’énonciateur comme n’étant pas problématique, sans pour autant qu’il ne se préoccupe véritablement de la position du co-énonciateur.La différence d’emploi entre ces deux conjonctions se ressent lors de la recherche d’exemples, dans la mesure où il semble plus facile de trouver des exemples de as avec une valeur causale dans la presse, tandis que cette valeur de since est plus largement répandue d’une manière générale, et ne semble pas devoir correspondre à un genre en particulier. Nous proposons une expérience afin de tester cette impression, et avons recours pour cela au corpus parallèle multi-langues Intercorp

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)