19,370 research outputs found
Computing the Affective-Aesthetic Potential of Literary Texts
In this paper, we compute the affective-aesthetic potential (AAP) of literary texts by using a simple sentiment analysis tool called SentiArt. In contrast to other established tools, SentiArt is based on publicly available vector space models (VSMs) and requires no emotional dictionary, thus making it applicable in any language for which VSMs have been made available (>150 so far) and avoiding issues of low coverage. In a first study, the AAP values of all words of a widely used lexical databank for German were computed and the VSMâs ability in representing concrete and more abstract semantic concepts was demonstrated. In a second study, SentiArt was used to predict ~2800 human word valence ratings and shown to have a high predictive accuracy (R2 > 0.5, p < 0.0001). A third study tested the validity of SentiArt in predicting emotional states over (narrative) time using human liking ratings from reading a story. Again, the predictive accuracy was highly significant: R2adj = 0.46, p < 0.0001, establishing the SentiArt tool as a promising candidate for lexical sentiment analyses at both the micro- and macrolevels, i.e., short and long literary materials. Possibilities and limitations of lexical VSM-based sentiment analyses of diverse complex literary texts are discussed in the light of these results
Hidden topicâemotion transition model for multi-level social emotion detection
With the fast development of online social platforms, social emotion detection, focusing on predicting readersâ emotions evoked by news articles, has been intensively investigated. Considering emotions as latent variables, various probabilistic graphical models have been proposed for emotion detection. However, the bag-of-words assumption prohibits those models from capturing the inter-relations between sentences in a document. Moreover, existing models can only detect emotions at either the document-level or the sentence-level. In this paper, we propose an effective Bayesian model, called hidden TopicâEmotion Transition model, by assuming that words in the same sentence share the same emotion and topic and modeling the emotions and topics in successive sentences as a Markov chain. By doing so, not only the document-level emotion but also the sentence-level emotion can be detected simultaneously. Experimental results on the two public corpora show that the proposed model outperforms state-of-the-art approaches on both document-level and sentence-level emotion detection
REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection
Technological advancements in web platforms allow people to express and share
emotions towards textual write-ups written and shared by others. This brings
about different interesting domains for analysis; emotion expressed by the
writer and emotion elicited from the readers. In this paper, we propose a novel
approach for Readers' Emotion Detection from short-text documents using a deep
learning model called REDAffectiveLM. Within state-of-the-art NLP tasks, it is
well understood that utilizing context-specific representations from
transformer-based pre-trained language models helps achieve improved
performance. Within this affective computing task, we explore how incorporating
affective information can further enhance performance. Towards this, we
leverage context-specific and affect enriched representations by using a
transformer-based pre-trained language model in tandem with affect enriched
Bi-LSTM+Attention. For empirical evaluation, we procure a new dataset REN-20k,
besides using RENh-4k and SemEval-2007. We evaluate the performance of our
REDAffectiveLM rigorously across these datasets, against a vast set of
state-of-the-art baselines, where our model consistently outperforms baselines
and obtains statistically significant results. Our results establish that
utilizing affect enriched representation along with context-specific
representation within a neural architecture can considerably enhance readers'
emotion detection. Since the impact of affect enrichment specifically in
readers' emotion detection isn't well explored, we conduct a detailed analysis
over affect enriched Bi-LSTM+Attention using qualitative and quantitative model
behavior evaluation techniques. We observe that compared to conventional
semantic embedding, affect enriched embedding increases ability of the network
to effectively identify and assign weightage to key terms responsible for
readers' emotion detection
Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction
The most prominent tasks in emotion analysis are to assign emotions to texts
and to understand how emotions manifest in language. An observation for NLP is
that emotions can be communicated implicitly by referring to events, appealing
to an empathetic, intersubjective understanding of events, even without
explicitly mentioning an emotion name. In psychology, the class of emotion
theories known as appraisal theories aims at explaining the link between events
and emotions. Appraisals can be formalized as variables that measure a
cognitive evaluation by people living through an event that they consider
relevant. They include the assessment if an event is novel, if the person
considers themselves to be responsible, if it is in line with the own goals,
and many others. Such appraisals explain which emotions are developed based on
an event, e.g., that a novel situation can induce surprise or one with
uncertain consequences could evoke fear. We analyze the suitability of
appraisal theories for emotion analysis in text with the goal of understanding
if appraisal concepts can reliably be reconstructed by annotators, if they can
be predicted by text classifiers, and if appraisal concepts help to identify
emotion categories. To achieve that, we compile a corpus by asking people to
textually describe events that triggered particular emotions and to disclose
their appraisals. Then, we ask readers to reconstruct emotions and appraisals
from the text. This setup allows us to measure if emotions and appraisals can
be recovered purely from text and provides a human baseline. Our comparison of
text classification methods to human annotators shows that both can reliably
detect emotions and appraisals with similar performance. Therefore, appraisals
constitute an alternative computational emotion analysis paradigm and further
improve the categorization of emotions in text with joint models.Comment: Computational Linguistics Journal in Issue No 1, March 2023; 71
pages, 13 figures, 19 table
On the Detection of False Information: From Rumors to Fake News
Tesis por compendio[ES] En tiempos recientes, el desarrollo de las redes sociales y de las agencias de noticias han traĂdo nuevos retos y amenazas a la web. Estas amenazas han llamado la atenciĂłn de la comunidad investigadora en Procesamiento del Lenguaje Natural (PLN) ya que estĂĄn contaminando las plataformas de redes sociales. Un ejemplo de amenaza serĂan las noticias falsas, en las que los usuarios difunden y comparten informaciĂłn falsa, inexacta o engañosa. La informaciĂłn falsa no se limita a la informaciĂłn verificable, sino que tambiĂ©n incluye informaciĂłn que se utiliza con fines nocivos. AdemĂĄs, uno de los desafĂos a los que se enfrentan los investigadores es la gran cantidad de usuarios en las plataformas de redes sociales, donde detectar a los difusores de informaciĂłn falsa no es tarea fĂĄcil.
Los trabajos previos que se han propuesto para limitar o estudiar el tema de la detecciĂłn de informaciĂłn falsa se han centrado en comprender el lenguaje de la informaciĂłn falsa desde una perspectiva lingĂŒĂstica. En el caso de informaciĂłn verificable, estos enfoques se han propuesto en un entorno monolingĂŒe. AdemĂĄs, apenas se ha investigado la detecciĂłn de las fuentes o los difusores de informaciĂłn falsa en las redes sociales.
En esta tesis estudiamos la informaciĂłn falsa desde varias perspectivas. En primer lugar, dado que los trabajos anteriores se centraron en el estudio de la informaciĂłn falsa en un entorno monolingĂŒe, en esta tesis estudiamos la informaciĂłn falsa en un entorno multilingĂŒe. Proponemos diferentes enfoques multilingĂŒes y los comparamos con un conjunto de baselines monolingĂŒes. AdemĂĄs, proporcionamos estudios sistemĂĄticos para los resultados de la evaluaciĂłn de nuestros enfoques para una mejor comprensiĂłn. En segundo lugar, hemos notado que el papel de la informaciĂłn afectiva no se ha investigado en profundidad. Por lo tanto, la segunda parte de nuestro trabajo de investigaciĂłn estudia el papel de la informaciĂłn afectiva en la informaciĂłn falsa y muestra cĂłmo los autores de contenido falso la emplean para manipular al lector. AquĂ, investigamos varios tipos de informaciĂłn falsa para comprender la correlaciĂłn entre la informaciĂłn afectiva y cada tipo (Propaganda, Trucos / Engaños, Clickbait y SĂĄtira).
Por Ășltimo, aunque no menos importante, en un intento de limitar su propagaciĂłn, tambiĂ©n abordamos el problema de los difusores de informaciĂłn falsa en las redes sociales. En esta direcciĂłn de la investigaciĂłn, nos enfocamos en explotar varias caracterĂsticas basadas en texto extraĂdas de los mensajes de perfiles en lĂnea de tales difusores. Estudiamos diferentes conjuntos de caracterĂsticas que pueden tener el potencial de ayudar a discriminar entre difusores de informaciĂłn falsa y verificadores de hechos.[CA] En temps recents, el desenvolupament de les xarxes socials i de les agĂšncies de notĂcies han portat nous reptes i amenaces a la web. Aquestes amenaces han cridat l'atenciĂł de la comunitat investigadora en Processament de Llenguatge Natural (PLN) ja que estan contaminant les plataformes de xarxes socials. Un exemple d'amenaça serien les notĂcies falses, en quĂš els usuaris difonen i comparteixen informaciĂł falsa, inexacta o enganyosa. La informaciĂł falsa no es limita a la informaciĂł verificable, sinĂł que tambĂ© inclou informaciĂł que s'utilitza amb fins nocius. A mĂ©s, un dels desafiaments als quals s'enfronten els investigadors Ă©s la gran quantitat d'usuaris en les plataformes de xarxes socials, on detectar els difusors d'informaciĂł falsa no Ă©s tasca fĂ cil.
Els treballs previs que s'han proposat per limitar o estudiar el tema de la detecciĂł d'informaciĂł falsa s'han centrat en comprendre el llenguatge de la informaciĂł falsa des d'una perspectiva lingĂŒĂstica. En el cas d'informaciĂł verificable, aquests enfocaments s'han proposat en un entorn monolingĂŒe. A mĂ©s, gairebĂ© no s'ha investigat la detecciĂł de les fonts o els difusors d'informaciĂł falsa a les xarxes socials.
En aquesta tesi estudiem la informaciĂł falsa des de diverses perspectives. En primer lloc, atĂšs que els treballs anteriors es van centrar en l'estudi de la informaciĂł falsa en un entorn monolingĂŒe, en aquesta tesi estudiem la informaciĂł falsa en un entorn multilingĂŒe. Proposem diferents enfocaments multilingĂŒes i els comparem amb un conjunt de baselines monolingĂŒes. A mĂ©s, proporcionem estudis sistemĂ tics per als resultats de l'avaluaciĂł dels nostres enfocaments per a una millor comprensiĂł. En segon lloc, hem notat que el paper de la informaciĂł afectiva no s'ha investigat en profunditat. Per tant, la segona part del nostre treball de recerca estudia el paper de la informaciĂł afectiva en la informaciĂł falsa i mostra com els autors de contingut fals l'empren per manipular el lector. AquĂ, investiguem diversos tipus d'informaciĂł falsa per comprendre la correlaciĂł entre la informaciĂł afectiva i cada tipus (Propaganda, Trucs / Enganys, Clickbait i SĂ tira).
Finalment, perĂČ no menys important, en un intent de limitar la seva propagaciĂł, tambĂ© abordem el problema dels difusors d'informaciĂł falsa a les xarxes socials. En aquesta direcciĂł de la investigaciĂł, ens enfoquem en explotar diverses caracterĂstiques basades en text extretes dels missatges de perfils en lĂnia de tals difusors. Estudiem diferents conjunts de caracterĂstiques que poden tenir el potencial d'ajudar a discriminar entre difusors d'informaciĂł falsa i verificadors de fets.[EN] In the recent years, the development of social media and online news agencies has brought several challenges and threats to the Web. These threats have taken the attention of the Natural Language Processing (NLP) research community as they are polluting the online social media platforms. One of the examples of these threats is false information, in which false, inaccurate, or deceptive information is spread and shared by online users. False information is not limited to verifiable information, but it also involves information that is used for harmful purposes. Also, one of the challenges that researchers have to face is the massive number of users in social media platforms, where detecting false information spreaders is not an easy job.
Previous work that has been proposed for limiting or studying the issue of detecting false information has focused on understanding the language of false information from a linguistic perspective. In the case of verifiable information, approaches have been proposed in a monolingual setting. Moreover, detecting the sources or the spreaders of false information in social media has not been investigated much.
In this thesis we study false information from several aspects. First, since previous work focused on studying false information in a monolingual setting, in this thesis we study false information in a cross-lingual one. We propose different cross-lingual approaches and we compare them to a set of monolingual baselines. Also, we provide systematic studies for the evaluation results of our approaches for better understanding. Second, we noticed that the role of affective information was not investigated in depth. Therefore, the second part of our research work studies the role of the affective information in false information and shows how the authors of false content use it to manipulate the reader. Here, we investigate several types of false information to understand the correlation between affective information and each type (Propaganda, Hoax, Clickbait, Rumor, and Satire).
Last but not least, in an attempt to limit its spread, we also address the problem of detecting false information spreaders in social media. In this research direction, we focus on exploiting several text-based features extracted from the online profile messages of those spreaders. We study different feature sets that can have the potential to help to identify false information spreaders from fact checkers.Ghanem, BHH. (2020). On the Detection of False Information: From Rumors to Fake News [Tesis doctoral]. Universitat PolitĂšcnica de ValĂšncia. https://doi.org/10.4995/Thesis/10251/158570TESISCompendi
LEIA: Linguistic Embeddings for the Identification of Affect
The wealth of text data generated by social media has enabled new kinds of
analysis of emotions with language models. These models are often trained on
small and costly datasets of text annotations produced by readers who guess the
emotions expressed by others in social media posts. This affects the quality of
emotion identification methods due to training data size limitations and noise
in the production of labels used in model development. We present LEIA, a model
for emotion identification in text that has been trained on a dataset of more
than 6 million posts with self-annotated emotion labels for happiness,
affection, sadness, anger, and fear. LEIA is based on a word masking method
that enhances the learning of emotion words during model pre-training. LEIA
achieves macro-F1 values of approximately 73 on three in-domain test datasets,
outperforming other supervised and unsupervised methods in a strong benchmark
that shows that LEIA generalizes across posts, users, and time periods. We
further perform an out-of-domain evaluation on five different datasets of
social media and other sources, showing LEIA's robust performance across media,
data collection methods, and annotation schemes. Our results show that LEIA
generalizes its classification of anger, happiness, and sadness beyond the
domain it was trained on. LEIA can be applied in future research to provide
better identification of emotions in text from the perspective of the writer.
The models produced for this article are publicly available at
https://huggingface.co/LEI
Determinants of impact : towards a better understanding of encounters with the arts
The article argues that current methods for assessing the impact of the arts are largely based on a fragmented and incomplete understanding of the cognitive, psychological and socio-cultural dynamics that govern the aesthetic experience. It postulates that a better grasp of the interaction between the individual and the work of art is the necessary foundation for a genuine understanding of how the arts can affect people. Through a critique of philosophical and empirical attempts to capture the main features of the aesthetic encounter, the article draws attention to the gaps in our current understanding of the responses to art. It proposes a classification and exploration of the factorsâsocial, cultural and psychologicalâthat contribute to shaping the aesthetic experience, thus determining the possibility of impact. The âdeterminants of impactâ identified are distinguished into three groups: those that are inherent to the individual who interacts with the artwork; those that are inherent to the artwork; and âenvironmental factorsâ, which are extrinsic to both the individual and the artwork. The article concludes that any meaningful attempt to assess the impact of the arts would need to take these âdeterminants of impactâ into account, in order to capture the multidimensional and subjective nature of the aesthetic experience
Activating Character Strengths in the EFL/ESL Classroom: A Theoretical Perspective
The application of positive psychology to the language classroom has mainly focused on the first pillar of positive psychology; positive emotions. Recent studies, however, suggest that the second pillar, character traits, contribute to language learner well-being (Oxford, Powerfully Positive, 21-22; Wang et al. 3; Piasecka 75). This research borrows from theories and models in positive psychology but lacks a contextualized theoretical understanding of the relationships between carĂĄcter strengths and other language learner variables such as identity and motivation. The purpose of this paper is to identify areas of theoretical weakness in a recent model of language learner well-being and propose a theory born from contributions of both positive psychology and SLA research.Departamento de FilologĂa InglesaMĂĄster en Estudios Ingleses Avanzados: Lenguas y Culturas en Contact
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Educational use cases from a shared exploration of e-books and iPads
E-books and e-book readers are becoming increasingly widely available, particularly for the general reader, and there have been many studies on their adoption. However, less is known about their use for educational and academic purposes. We report here on work carried out on e-books and e-book applications using iPads by academic and teaching staff. After considering pedagogical issues and reporting survey results, we identify a spiral of six key use case areas for e-books. This spiral of use cases moves from basic e-book use, through situational reading, e-books and learning, using multiple learning resources, collaborative/group learning, to e-book production. We discuss each of these use case areas and provide guidelines that will be of interest to practitioners and researchers alike
User-Centered Categorization of Mood in Fiction
Readers articulate mood in deeply subjective ways, yet the underlying structure of usersâ understanding of the media they consume has important implications for retrieval and access. User articulations might at first seem too idiosyncratic, but organizing them meaningfully has considerable potential to provide a better searching experience for all involved. The current study develops mood categories inductively for fiction organization and retrieval in information systems.We developed and distributed an open-ended survey to 76 fiction readers to understand their preferences with regard to the affective elements in fiction. From the fiction reader responses, the research team identified 161 mood terms and used them for further categorization.Our inductive approach resulted in 30 categories, including angry, cozy, dark, and nostalgic. Results include three overlapping mood families: Emotion, Tone/Narrative, and Atmosphere/Setting, which in turn relate to structures that connect reader-generated data with conceptual frameworks in previous studies.The inherent complexity of âmoodâ should not dissuade us from carefully investigating usersâ preferences in this regard. Adding to the existing efforts of classifying moods conducted by experts, the current study presents mood terms provided by actual end-users when describing different moods in fiction. This study offers a useful roadmap for creating taxonomies for retrieval and description, as well as structures derived from user-provided terms that ultimately have the potential to improve user experience
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