213 research outputs found

    Workshop Proceedings of the 12th edition of the KONVENS conference

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    The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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    Sentiment Analysis on YouTube Comments : Analysis of prevailing attitude towards Nokia Mobile Phones

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    The volume of textual data, more specifically, the magnitude of opinionated text on social media, has increased the interest of companies to closely analyze what their customers have to say about them and their products. This thesis explores the possibility of performing aspect-based sentiment analysis with YouTube comments. The comments on Nokia Mobile phones are the subject of the study in this thesis. First, manual labeling was performed to identify the aspect terms and sentiment and then categorize the aspects based on the aspect’s functionality on the phone. From the categorization, it was found out that people mainly have shown negative sentiment towards multiple aspects of the phone with maximum negative attitude towards the price of the phone. On the other hand, the only aspect that could gather a positive attitude was the phone’s-built quality. The result shows that there are multiple phone aspects that HMD Global can consider for current and future product improvement. Further, this study used the labeled data to perform supervised learning to classify the aspects and the aspect sentiment from the comments. With two features extraction techniques, BoW and TF-IDF, this paper has explored the performance of different machine learning models on YouTube comments. The models show good results for aspect classification; however, the model’s performance could be further improved for aspect sentiment classification. Overall, little attention to this area has been discussed because of the complexity, highly unstructured, and noisy nature of text on YouTube. However, despite the challenges, this platform can be valuable in producing insightful analysis, as presented in this thesis

    A Survey on Semantic Processing Techniques

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    Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

    CLARIN

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    The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

    The Edges of Meaning. Schizophrenia between Semiotics, Psychopathology and Cognitive Sciences

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    La schizofrenia rappresenta uno dei più grandi enigmi per l’impresa conoscitiva umana: non si conosce la sua eziologia, né le sue basi biologiche e cerebrali. Non è neanche chiaro cosa accada nell’esperienza di chi ne soffre, che sembra vivere in un mondo altro. La scarsa conoscenza dell’esperienza schizofrenica e la distanza tra questa e il senso comune hanno portato molti studiosi a inquadrare questo disturbo come illogico, irrazionale, insensato. Il presente lavoro tenta di confutare tale impostazione, mostrando come il mondo di senso dello schizofrenico si altera, non si disgrega; si trasforma, non si annulla. Il campo di studi all’interno del quale si colloca la ricerca è la semiotica, disciplina che studia i sistemi e i processi di significazione e i modi attraverso cui l’essere umano dà senso al mondo. L’intera indagine è inserita in un quadro interdisciplinare in costante dialogo con la psicopatologia fenomenologica e le scienze cognitive contemporanee, e si sviluppa a partire da numerosi testi autobiografici di pazienti schizofrenici, report psichiatrici, articoli di giornale, film e romanzi sul tema. L’ipotesi su cui si muove il lavoro è che sia possibile comprendere la schizofrenia come un problema costitutivamente semiotico, il cui nucleo è da rintracciarsi in una radicale metamorfosi delle modalità di produrre e interpretare il significato. La scommessa sottesa è che la semiotica possa contribuire in modo sostanziale alla comprensione delle modalità attraverso cui la nostra cultura concettualizza la schizofrenia e dei modi in cui gli schizofrenici danno senso al mondo. Il lavoro indaga, quindi, i legami tra schizofrenia e cultura, la storia del concetto nosografico, e le alterazioni dei processi di significazione nei casi di eloquio disorganizzato, nei racconti autobiografici e nei deliri, cercando anche di fornire strumenti utili alla pratica clinica.Schizophrenia represents one of the greatest enigmas for human knowledge: its aetiology is unknown, as its biological and neurological basis. It is even unclear what happens in the sufferers' experience, who seem to live in another world. The lack of knowledge of the schizophrenic experience and the distance between this experience and common sense have led many scholars to define this disorder as illogical, irrational, and senseless. On the contrary, the present work shows how the schizophrenic world of meaning alters, does not disintegrate; it changes, does not dissolve. The investigation is situated in an interdisciplinary framework where Semiotics, the discipline that accounts for systems and processes of signification, dialogues with phenomenological psychopathology and contemporary cognitive sciences. The inquiry is developed by taking into account several autobiographical texts of schizophrenic patients, psychiatric reports, newspaper articles, movies and novels on this subject. The leading hypothesis is that it is possible to understand schizophrenia as a constitutively semiotic problem, represented by a radical metamorphosis of meaning production and interpretation. So the bet we made is that Semiotics can contribute to understanding how our culture conceptualises schizophrenia, and the ways in which people with schizophrenia make sense of the world. Therefore, the thesis investigates the links between schizophrenia and culture, the history of the nosographic concept, and the alterations of the signification processes in disorganised speech, autobiographical narratives, and delusions, providing valuable tools for clinical practice

    Toward a multitask aspect-based sentiment analysis model using deep learning

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    International audienceSentiment analysis or opinion mining is used to understand the community’s opinions on a particular product. This is a system of selection and classification of opinions on sentences or documents. At a more detailed level, aspect-based sentiment analysis makes an effort to extract and categorize sentiments on aspects of entities in opinion text. In this paper, we propose a novel supervised learning approach using deep learning techniques for a multitasking aspect-based opinion mining system that supports four main subtasks: extract opinion target, classify aspect, classify entity (category) and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of the entity. We have used a part-of-speech (POS) layer to define the words’ morphological features integrated with GloVe word embedding in the previous layer and fed to the convolutional neural network_bidirectional long-short term memory (CNN_BiLSTM) stacked construction to improve the model’s accuracy in the opinion classification process and related tasks. Our multitasking aspect-based sentiment analysis experiments on the dataset of SemEval 2016 showed that our proposed models have obtained and categorized core tasks mentioned above simultaneously and attained considerably better accurateness than the advanced researches

    Temporal Emotion Dynamics in Social Networks

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    [ES] El análisis de sentimientos en redes sociales se ha estudiado ampliamente durante la última década. A pesar de ello, las distintas categorías de sentimientos no se consideran adecuadamente en muchos casos, y el estudio de patrones de difusión de las emociones es limitado. Por lo tanto, comprender la importancia de emociones específicas será más beneficioso para diversas actividades de marketing, toma de decisiones empresariales y campañas políticas. Esta tesis doctoral se centra en el diseño de un marco teórico para analizar el amplio espectro de sentimientos y explicar cómo se propagan las emociones utilizando conceptos de redes temporales y multicapa. Particularmente, nuestro objetivo es proporcionar información sobre el modelado de la influencia de las emociones y como esta afecta a los problemas de estimación de las emociones y a la naturaleza dinámica temporal en la conversación social. Para mostrar la eficacia del modelo propuesto, se han recopilado publicaciones relacionadas con diferentes eventos de Twitter y hemos construido una estructura de red temporal sobre la conversación. En primer lugar, realizamos un análisis de sentimientos adoptando un enfoque basado en el léxico y en el modelo circunflejo de emociones de Russell que mejora la efectividad de la caracterización del sentimiento. A partir de este análisis investigamos la dinámica social de las emociones presente en las opiniones de los usuarios analizando diferentes características de influencia social. A continuación, diseñamos un modelo estocástico temporal basado en emociones para investigar el patrón de participación de los usuarios y predecir las emociones significativas. Nuestra contribución final es el desarrollo de un modelo de influencia secuencial basado en emociones mediante la utilización de redes neuronales recurrentes que permiten predecir emociones de una manera más completa. Finalmente, el documento presenta algunas conclusiones y también describe las direcciones de investigación futuras.[CA] L'anàlisi de sentiments en xarxes socials s'ha estudiat àmpliament durant l'última dècada. Malgrat això, les diferents categories de sentiments no es consideren adequadament en molts casos, i l'estudi de patrons de difusió de les emocions és limitat. Per tant, comprendre la importància d'emocions específiques serà més beneficiós per a diverses activitats de màrqueting, presa de decisions empresarials i campanyes polítiques. Aquesta tesi doctoral se centra en el disseny d'un marc teòric per a analitzar l'ampli espectre de sentiments i explicar com es propaguen les emocions utilitzant conceptes de xarxes temporals i multicapa. Particularment, el nostre objectiu és proporcionar informació sobre el modelatge de la influència de les emocions i com aquesta afecta als problemes d'estimació de les emocions i a la naturalesa dinàmica temporal en la conversa social. Per a mostrar l'eficàcia del model proposat, s'han recopilat publicacions relacionades amb diferents esdeveniments de Twitter i hem construït una estructura de xarxa temporal sobre la conversa. En primer lloc, realitzem una anàlisi de sentiments adoptant un enfocament basat en el lèxic i en el model circumflex d'emocions de Russell que millora l'efectivitat de la caracterització del sentiment. A partir d'aquesta anàlisi investiguem la dinàmica social de les emocions present en les opinions dels usuaris analitzant diferents característiques d'influència social. A continuació, dissenyem un model estocàstic temporal basat en emocions per a investigar el patró de participació dels usuaris i predir les emocions significatives. La nostra contribució final és el desenvolupament d'un model d'influència seqüencial basat en emocions mitjançant la utilització de xarxes neuronals recurrents que permeten predir emocions d'una manera més completa. Finalment, el document presenta algunes conclusions i també descriu les direccions d'investigació futures.[EN] Sentiment analysis in social networks has been widely analysed over the last decade. Despite the amount of research done in sentiment analysis in social networks, the distinct categories are not appropriately considered in many cases, and the study of dissemination patterns of emotions is limited. Therefore, understanding the significance of specific emotions will be more beneficial for various marketing activities, policy-making decisions and political campaigns. The current PhD thesis focuses on designing a theoretical framework for analyzing the broad spectrum of sentiments and explain how emotions are propagated using concepts from temporal and multilayer networks. More precisely, our goal is to provide insights into emotion influence modelling that solves emotion estimation problems and its temporal dynamics nature on social conversation. To exhibit the efficacy of the proposed model, we have collected posts related to different events from Twitter and build a temporal network structure over the conversation. Firstly, we perform sentiment analysis with the adaptation of a lexicon-based approach and the circumplex model of affect that enhances the effectiveness of the sentiment characterization. Subsequently, we investigate the social dynamics of emotion present in users' opinions by analyzing different social influential characteristics. Next, we design a temporal emotion-based stochastic model in order to investigate the engagement pattern and predict the significant emotions. Our ultimate contribution is the development of a sequential emotion-based influence model with the advancement of recurrent neural networks. It offers to predict emotions in a more comprehensive manner. Finally, the document presents some conclusions and also outlines future research directions.Naskar, D. (2022). Temporal Emotion Dynamics in Social Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/180997TESI
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