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We present results of a sentiment annotation study in the context of historical German plays. Our annotation corpus consists of 200 representative speeches from the German playwright Gotthold Ephraim Lessing. Six annotators, five non-experts and one expert in the domain, annotated the speeches according to different sentiment annotation schemes. They had to annotate the differentiated polarity (very negative, negative, neutral, mixed, positive, very positive), the binary polarity (positive/negative) and the occurrence of eight basic emotions. After the annotation, the participants completed a questionnaire about their experience of the annotation process; additional feedback was gathered in a closing interview. Analysis of the annotations shows that the agreement among annotators ranges from low to mediocre. The non-expert annotators perceive the task as very challenging and report different problems in understanding the language and the context. Although fewer problems occur for the expert annotator, we cannot find any differences in the agreement levels among non-experts and between the expert and the non-experts. At the end of the paper, we discuss the implications of this study and future research plans for this area

No description available.

We present first results of an ongoing research project on sentiment annotation of historical plays by German playwright G. E. Lessing (1729-1781). For a subset of speeches from six of his most famous plays, we gathered sentiment annotations by two independent annotators for each play. The annotators were nine students from a Master’s program of German Literature. Overall, we gathered annotations for 1,183 speeches. We report sentiment distributions and agreement metrics and put the results in the context of current research. A preliminary version of the annotated corpus of speeches is publicly available online and can be used for further investigations, evaluations and computational sentiment analysis approaches.

The use of artificial intelligence (AI), especially ChatGPT, has increased exponentially in the past years, and it can be seen how AI-based tools are being used in several fields, including education. The literature on AI on education (AIEd), how it has been used, its potential uses, opportunities and challenges were reviewed as well as the literature on sentiment analysis on social media to evaluate the best approach. Since education might face notorious changes due to this technology, assessing how people feel about this potential change in the paradigm is essential. Sentiment analysis on YouTube comments of videos related to ChatGPT, the most popular AI tool for education across learners and educators, was performed. It was found that 62.1% of thes ample had a positive feeling regarding this technology for educational purposes, whereas 19.4% had a negative sentiment and 18.5% were neutral. To contribute to the literature on sentiment analysis of YouTube comments, the two most used and best-performing algorithms were used for this task: Naive Bayes and Support Vector Machine. The results show that the first algorithm had a 61.30% accuracy, whereas SVM had a 71.79%.

Los seres humanos han desarrollado especialmente su capacidad perceptiva para procesar caras y extraer información de las características faciales. Usando nuestra capacidad conductual para percibir rostros, hacemos atribuciones tales como personalidad, inteligencia o confiabilidad basadas en la apariencia facial que a menudo tienen un fuerte impacto en el comportamiento social en diferentes dominios. Por lo tanto, las caras desempeñan un papel fundamental en nuestras relaciones con otras personas y en nuestras decisiones cotidianas. Con la popularización de Internet, las personas participan en muchos tipos de interacciones virtuales, desde experiencias sociales, como juegos, citas o comunidades, hasta actividades profesionales, como e-commerce, e-learning, e-therapy o e-health. Estas interacciones virtuales manifiestan la necesidad de caras que representen a las personas reales que interactúan en el mundo digital: así surgió el concepto de avatar. Los avatares se utilizan para representar a los usuarios en diferentes escenarios y ámbitos, desde la vida personal hasta situaciones profesionales. En todos estos casos, la aparición del avatar puede tener un efecto no solo en la opinión y percepción de otra persona, sino en la autopercepción, que influye en la actitud y el comportamiento del sujeto. De hecho, los avatares a menudo se emplean para obtener impresiones o emociones a través de expresiones no verbales, y pueden mejorar las interacciones en línea o incluso son útiles para fines educativos o terapéuticos. Por lo tanto, la posibilidad de generar avatares de aspecto realista que provoquen un determinado conjunto de impresiones sociales supone una herramienta muy interesante y novedosa, útil en un amplio abanico de campos. Esta tesis propone un método novedoso para generar caras de aspecto realistas con un perfil social asociado que comprende 15 impresiones diferentes. Para este propósito, se completaron varios objetivos parciales. En primer lugar, las características faciales se extrajeron de una base de datos de caras reales y se agruparon por aspecto de una manera automática y objetiva empleando técnicas de reducción de dimensionalidad y agrupamiento. Esto produjo una taxonomía que permite codificar de manera sistemática y objetiva las caras de acuerdo con los grupos obtenidos previamente. Además, el uso del método propuesto no se limita a las características faciales, y se podría extender su uso para agrupar automáticamente cualquier otro tipo de imágenes por apariencia. En segundo lugar, se encontraron las relaciones existentes entre las diferentes características faciales y las impresiones sociales. Esto ayuda a saber en qué medida una determinada característica facial influye en la percepción de una determinada impresión social, lo que permite centrarse en la característica o características más importantes al diseñar rostros con una percepción social deseada. En tercer lugar, se implementó un método de edición de imágenes para generar una cara totalmente nueva y realista a partir de una definición de rostro utilizando la taxonomía de rasgos faciales antes mencionada. Finalmente, se desarrolló un sistema para generar caras realistas con un perfil de rasgo social asociado, lo cual cumple el objetivo principal de la presente tesis. La principal novedad de este trabajo reside en la capacidad de trabajar con varias dimensiones de rasgos a la vez en caras realistas. Por lo tanto, en contraste con los trabajos anteriores que usan imágenes con ruido, o caras de dibujos animados o sintéticas, el sistema desarrollado en esta tesis permite generar caras de aspecto realista eligiendo los niveles deseados de quince impresiones: Miedo, Enfado, Atractivo, Cara de niño, Disgustado, Dominante, Femenino, Feliz, Masculino, Prototípico, Triste, Sorprendido, Amenazante, Confiable e Inusual. Los prometedores resultados obtenidos permitirán investigar más a fondo cómo modelar l / Humans have specially developed their perceptual capacity to process faces and to extract information from facial features. Using our behavioral capacity to perceive faces, we make attributions such as personality, intelligence or trustworthiness based on facial appearance that often have a strong impact on social behavior in different domains. Therefore, faces play a central role in our relationships with other people and in our everyday decisions. With the popularization of the Internet, people participate in many kinds of virtual interactions, from social experiences, such as games, dating or communities, to professional activities, such as e-commerce, e-learning, e-therapy or e-health. These virtual interactions manifest the need for faces that represent the actual people interacting in the digital world: thus the concept of avatar emerged. Avatars are used to represent users in different scenarios and scopes, from personal life to professional situations. In all these cases, the appearance of the avatar may have an effect not only on other person's opinion and perception but on self-perception, influencing the subject's own attitude and behavior. In fact, avatars are often employed to elicit impressions or emotions through non-verbal expressions, and are able to improve online interactions or even useful for education purposes or therapy. Then, being able to generate realistic looking avatars which elicit a certain set of desired social impressions poses a very interesting and novel tool, useful in a wide range of fields. This thesis proposes a novel method for generating realistic looking faces with an associated social profile comprising 15 different impressions. For this purpose, several partial objectives were accomplished. First, facial features were extracted from a database of real faces and grouped by appearance in an automatic and objective manner employing dimensionality reduction and clustering techniques. This yielded a taxonomy which allows to systematically and objectively codify faces according to the previously obtained clusters. Furthermore, the use of the proposed method is not restricted to facial features, and it should be possible to extend its use to automatically group any other kind of images by appearance. Second, the existing relationships among the different facial features and the social impressions were found. This helps to know how much a certain facial feature influences the perception of a given social impression, allowing to focus on the most important feature or features when designing faces with a sought social perception. Third, an image editing method was implemented to generate a completely new, realistic face from just a face definition using the aforementioned facial feature taxonomy. Finally, a system to generate realistic faces with an associated social trait profile was developed, which fulfills the main objective of the present thesis. The main novelty of this work resides in the ability to work with several trait dimensions at a time on realistic faces. Thus, in contrast with the previous works that use noisy images, or cartoon-like or synthetic faces, the system developed in this thesis allows to generate realistic looking faces choosing the desired levels of fifteen impressions, namely Afraid, Angry, Attractive, Babyface, Disgusted, Dominant, Feminine, Happy, Masculine, Prototypical, Sad, Surprised, Threatening, Trustworthy and Unusual. The promising results obtained in this thesis will allow to further investigate how to model social perception in faces using a completely new approach. / Els sers humans han desenvolupat especialment la seua capacitat perceptiva per a processar cares i extraure informació de les característiques facials. Usant la nostra capacitat conductual per a percebre rostres, fem atribucions com ara personalitat, intel·ligència o confiabilitat basades en l'aparença facial que sovint tenen un fort impacte en el comportament social en diferents dominis. Per tant, les cares exercixen un paper fonamental en les nostres relacions amb altres persones i en les nostres decisions quotidianes. Amb la popularització d'Internet, les persones participen en molts tipus d'inter- accions virtuals, des d'experiències socials, com a jocs, cites o comunitats, fins a activitats professionals, com e-commerce, e-learning, e-therapy o e-health. Estes interaccions virtuals manifesten la necessitat de cares que representen a les persones reals que interactuen en el món digital: així va sorgir el concepte d'avatar. Els avatars s'utilitzen per a representar als usuaris en diferents escenaris i àmbits, des de la vida personal fins a situacions professionals. En tots estos casos, l'aparició de l'avatar pot tindre un efecte no sols en l'opinió i percepció d'una altra persona, sinó en l'autopercepció, que influïx en l'actitud i el comportament del subjecte. De fet, els avatars sovint s'empren per a obtindre impressions o emocions a través d'expressions no verbals, i poden millorar les interaccions en línia o inclús són útils per a fins educatius o terapèutics. Per tant, la possibilitat de generar avatars d'aspecte realista que provoquen un determinat conjunt d'impressions socials planteja una ferramenta molt interessant i nova, útil en un ampla varietat de camps. Esta tesi proposa un mètode nou per a generar cares d'aspecte realistes amb un perfil social associat que comprén 15 impressions diferents. Per a este propòsit, es van completar diversos objectius parcials. En primer lloc, les característiques facials es van extraure d'una base de dades de cares reals i es van agrupar per aspecte d'una manera automàtica i objectiva emprant tècniques de reducció de dimensionalidad i agrupament. Açò va produir una taxonomia que permet codificar de manera sistemàtica i objectiva les cares d'acord amb els grups obtinguts prèviament. A més, l'ús del mètode proposat no es limita a les característiques facials, i es podria estendre el seu ús per a agrupar automàticament qualsevol altre tipus d'imatges per aparença. En segon lloc, es van trobar les relacions existents entre les diferents característiques facials i les impressions socials. Açò ajuda a saber en quina mesura una determinada característica facial influïx en la percepció d'una determinada impressió social, la qual cosa permet centrar-se en la característica o característiques més importants al dissenyar rostres amb una percepció social desitjada. En tercer lloc, es va implementar un mètode d'edició d'imatges per a generar una cara totalment nova i realista a partir d'una definició de rostre utilitzant la taxonomia de trets facials abans mencionada. Finalment, es va desenrotllar un sistema per a generar cares realistes amb un perfil de tret social associat, la qual cosa complix l'objectiu principal de la present tesi. La principal novetat d'este treball residix en la capacitat de treballar amb diverses dimensions de trets al mateix temps en cares realistes. Per tant, en contrast amb els treballs anteriors que usen imatges amb soroll, o cares de dibuixos animats o sintètiques, el sistema desenrotllat en esta tesi permet generar cares d'aspecte realista triant els nivells desitjats de quinze impressions: Por, Enuig, Atractiu, Cara de xiquet, Disgustat, Dominant, Femení, Feliç, Masculí, Prototípic, Trist, Sorprés, Amenaçador, Confiable i Inusual. Els prometedors resultats obtinguts en esta tesi permetran investigar més a fons com modelar la percepció social en les cares utilitzant un enfocament complet / Fuentes Hurtado, FJ. (2018). A system for modeling social traits in realistic faces with artificial intelligence [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/101943

Civil infrastructure serves as the driving force behind the evolution of a safe, sustainable, and efficient environment. However, the way information about civil infrastructure has been communicated to the public has been insufficient. Since every human is intrinsically different, designing, and dispersing information about civil infrastructure that accommodates everyone, while also being direct and concise has been a challenge for policymakers and other federal, state, local, and tribal civil engineering stakeholders. Though there has been a plethora of research conducted on message design and communication in other disciplines, little research has been done in the US that focuses on designing more accessible, actionable civil infrastructure messages. The objective of this research was to investigate how to improve the accessibility of civil infrastructure messages and communication infrastructure to enhance the public's ability to make daily infrastructure decisions. This research study utilized quantitative and qualitative methods to analyze and discuss various ways that civil infrastructure messages can be improved. Results from this study are based on the exploration of three different ways in which civil infrastructure messaging can be improved: policy, transportation/roadway safety, and emergency response. Data sources include eight publicly accessible energy policies from 1978-2022, a publicly available dataset of more than 75 thousand WEAs, and a dataset retrieved from Shealy et al. (2020), which collected data on 300 Virginia drivers in both rural and urban areas. A descriptive policy analysis and Flesch-Kincaid readability test were conducted to historically analyze energy policies and understand their accessibility impacts for research question 1; a brain activation network analysis was conducted and nodal network measures (i.e., network density, degree centrality) were used to investigate the cognitive response Virginia drivers had for various types of non-traditional traffic safety messages for research question 2; and sentiment analysis, emotion detection analysis, as well as a two-phased qualitative coding analysis (i.e., in-vivo coding, focused coding) were conducted to investigate how WEAs can be better designed to increase public attention and engagement for research question 3. The findings from this study demonstrate how emotional content that is present in tweets authored by community members affected by the natural disaster event can be incorporated into the WEA template. The findings from research question 1 identified potential issues with accessibility and energy policy. Also, the findings from this study describe the content included in the parallel documents that federal agencies use to communicate the most important information of a policy. The findings from research question 2 demonstrate that while the various types of non-traditional traffic safety messages produced variances in cognitive response, messages that included negative emotional content or statistics should be further explored on their impact on evoking safer driving behaviors. The findings from research question 3 reported on how emotional content could be incorporated into the template design of WEAs. The implications from this dissertation provide valuable insights for policymakers, civil engineers, transportation engineers, and emergency response stakeholders and the conclusions set the stage for future research to improve the design of more accessible civil infrastructure messages. / Doctor of Philosophy / Civil infrastructure messages are used daily, but improper design can make them difficult to understand or to continue to use over long periods of time. Also, every human is different and interprets information about civil infrastructure, which adds a level of difficulty to designing effective civil infrastructure messages. Though there has been a lot of research on the effectiveness of civil infrastructure, little research has used a human-centered design approach to improve civil infrastructure messages. This study analyzes three different ways to improve civil infrastructure messages: policy, traffic safety, and emergency response. We used publicly available energy policies from 1978-2022, data collected by co-authors from Shealy et al. (2020) to analyze the cognitive response of 300 Virginia drivers to various types of non-traditional traffic safety messages, a publicly available dataset of more than 75 thousand Wireless Emergency Alerts sent by FEMA, and a publicly available data set of more than 9.1 thousand tweets about Hurricane Harvey. To analyze this data, this research study utilized various methods to understand how easy policies are to read, to understand how the brains of Virginia drivers respond to different types of non-traditional traffic safety messages and to identify the differences between tweets and WEAs. Results from this study suggest that parallel documents should be published alongside energy policies to help the public understand the main points of the policy, establish a readability metric to use for all energy policies, continue to investigate non-traditional traffic safety messages that included negative emotional content or statistics, measure the brain activation and observe long-term driving behaviors, use more negative emotional content in templated WEAs, and use social media data to better design templated WEAs. The findings reported from this study can be beneficial for various types of civil infrastructure stakeholders such as policymakers, utilities, US State Departments of Transportation, FEMA, alerting officials, and the public to further explore ways in which the language of civil infrastructure messages can be improved to address accessibility issues with energy policy, traffic safety, and emergency response to the public.

I examine partisan bias in broadcast and cable news coverage of the first impeachment of President Trump by evaluating how well three theories of news generation—network bias, marketplace incentives, and institutional forces—predict coverage, framing, and tonal biases. While no single theory provides a complete explanation of all partisan bias, institutional forces explain impeachment coverage better than either network bias or marketplace incentives. This research also highlights the nuanced nature of partisan frame representation, and suggests that institutional and marketplace theories better predict partisan frame diversity than theories of a partisan press. Finally, analysis of tonal bias reveals complex variations across and within news mediums, challenging simplistic narratives of network bias. My research shows that many of the professional norms and routines that have long been known to influence news generation continue to do so even as they evolve.

Mobile technology is increasingly leveraged for mental health interventions, with users expressing overall satisfaction and finding the apps helpful and user-friendly. While the apps offer diverse features for symptom management, self-help, and treatment support, evidence regarding their effectiveness remains limited, suggesting a need for further research. Usability, engagement, and tailoring to user preferences emerge as critical factors, emphasizing the importance of customization for different populations. This research presented a systematic literature review aimed at evaluating studies specifically focusing on post-traumatic stress disorder (PTSD) apps, with a subsequent quality assessment using the MARS scale. Additionally, the research involves an in-depth analysis of user reviews for these PTSD apps through thematic, and path analysis. The technology acceptance model (TAM) model serves as the framework for path analysis, and the performance of VADER, Flair, and TextBlob is evaluated. Sentiment analysis is then employed to explore relationships among TAM model factors and additional factors derived from the systematic literature review and thematic analysis. In conclusion, this dissertation contributes to the understanding of PTSD apps, their usability, and their potential for mental health support. It underscores the need for further research, customization, and ongoing collaboration to optimize the effectiveness of these applications in managing PTSD symptoms and supporting individuals in their mental health journey.

Sentimentanalys är en teknik som syftar till att automatiskt identifiera den känslomässiga tonen i text. Vanligtvis klassificeras texten som positiv, neutral eller negativ. Nackdelen med denna indelning är att nyanser går förlorade när texten endast klassificeras i tre kategorier. En vidareutveckling av denna klassificering är att inkludera ytterligare två kategorier: mycket positiv och mycket negativ. Utmaningen med denna femklassificering är att det blir svårare att uppnå hög träffsäkerhet på grund av det ökade antalet kategorier. Detta har lett till behovet av att utforska olika metoder för att lösa problemet. Syftet med studien är därför att utvärdera olika klassificerare, såsom MLP, CNN och Bi-GRU i kombination med word2vec för att klassificera sentiment i text i fem kategorier. Studien syftar också till att utforska vilken förbehandling som ger högre träffsäkerhet för word2vec.   Utvecklingen av modellerna gjordes med hjälp av SST-datasetet, som är en känd dataset inom finfördelad sentimentanalys. För att avgöra vilken förbehandling som ger högre träffsäkerhet för word2vec, förbehandlades datasetet på fyra olika sätt. Dessa innefattar enkel förbehandling (EF), samt kombinationer av vanliga förbehandlingar som att ta bort stoppord (EF+Utan Stoppord) och lemmatisering (EF+Lemmatisering), samt en kombination av båda (EF+Utan Stoppord/Lemmatisering). Dropout användes för att hjälpa modellerna att generalisera bättre, och träningen reglerades med early stopp-teknik. För att utvärdera vilken klassificerare som ger högre träffsäkerhet, användes förbehandlingsmetoden som hade högst träffsäkerhet som identifierades, och de optimala hyperparametrarna utforskades. Måtten som användes i studien för att utvärdera träffsäkerheten är noggrannhet och F1-score.   Resultaten från studien visade att EF-metoden presterade bäst i jämförelse med de andra förbehandlingsmetoderna som utforskades. Den modell som hade högst noggrannhet och F1-score i studien var Bi-GRU. / Sentiment analysis is a technique aimed at automatically identifying the emotional tone in text. Typically, text is classified as positive, neutral, or negative. The downside of this classification is that nuances are lost when text is categorized into only three categories. An advancement of this classification is to include two additional categories: very positive and very negative. The challenge with this five-class classification is that achieving high performance becomes more difficult due to the increased number of categories. This has led to the need to explore different methods to solve the problem. Therefore, the purpose of the study is to evaluate various classifiers, such as MLP, CNN, and Bi-GRU in combination with word2vec, to classify sentiment in text into five categories. The study also aims to explore which preprocessing method yields higher performance for word2vec.   The development of the models was done using the SST dataset, which is a well-known dataset in fine-grained sentiment analysis. To determine which preprocessing method yields higher performance for word2vec, the dataset was preprocessed in four different ways. These include simple preprocessing (EF), as well as combinations of common preprocessing techniques such as removing stop words (EF+Without Stopwords) and lemmatization (EF+Lemmatization), as well as a combination of both (EF+Without Stopwords/Lemmatization). Dropout was used to help the models generalize better, and training was regulated with early stopping technique. To evaluate which classifier yields higher performance, the preprocessing method with the highest performance was used, and the optimal hyperparameters were explored. The metrics used in the study to evaluate performance are accuracy and F1-score.   The results of the study showed that the EF method performed best compared to the other preprocessing methods explored. The model with the highest accuracy and F1-score in the study was Bi-GRU.