Analysis of Metaphorical Expressions Used in Adele’s Song Lyrics “Someone Like You”

Metaphorical expressions play a crucial role in music, enabling artists to communicate complex emotions and experiences through vivid and imaginative language. This research examined the metaphorical expressions used in the lyrics of Adele's famous song "Someone Like You." By analyzing the metaphorical utilization in this song, it can gain insight into Adele's poetic techniques for evoking strong emotions and establishing rapport with her audience. Using a qualitative methodology, this research begins by identifying and classifying the metaphorical expressions found in the lyrics of "Someone Like You." Examining metaphors at multiple levels, including conceptual metaphors and novel metaphors, reveals their underlying meaning and symbolism. In addition, the study explores the potential cultural, emotional, and psychological implications of these metaphors, casting light on the universal appeal of the song. The lyrics of "Someone Like You" are full of metaphors that depict emotional states, such as love as a journey, heartbreak as physical pain, and time as a healing force. Moreover, Adele's metaphors connect the audience with the songwriter's experiences, resonating with similar emotional journeys. The analysis highlights the effectiveness of metaphors in enhancing the song's lyrical impact and eliciting empathetic responses. Adele's use transforms "Someone Like You" into a universal anthem of sorrow and perseverance. This study contributes to the existing literature on metaphor analysis in music by analyzing the metaphorical expressions in Adele's "Someone Like You." It illustrates the ability of metaphors to encapsulate complex emotions and experiences, thereby enhancing the listener's comprehension and emotional engagement with the music

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

SENTIMENT LABELING AND TEXT CLASSIFICATION MACHINE LEARNING FOR WHATSAPP GROUP

The use of WhatsApp Group (WAG) for communication is increasing nowadays. WAG communication data can be analyzed from various perspectives. However, this data is imported in the form of unstructured text files. The aim of this research is to explore the potential use of the SentiwordNet lexicon for labeling the positive, negative, or neutral sentiment of WAG data from "Alumni94" and training and testing it with machine learning text classification models. The training and testing were conducted on six models, namely Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), and Artificial Neural Network. The labeling results indicate that neutral sentiment is the majority with 7588 samples, followed by 324 negative and 1617 positive samples. Among all the models, Random Forest showed better precision and recall, i.e., 83% and 64%. On the other hand, Decision Tree had slightly lower precision and recall, i.e., 80% and 66%, but exhibited a better f-measure of 71%. The accuracy evaluation results of the Random Forest and Decision Tree models showed significant performance compared to others, achieving an accuracy of 89% in classifying new messages. This research demonstrates the potential use of the SentiwordNet lexicon and machine learning in sentiment analysis of WAG data using the Random Forest and Decision Tree model

Facial Thermal and Blood Perfusion Patterns of Human Emotions: Proof-of-Concept

In this work, a preliminary study of proof-of-concept was conducted to evaluate the performance of the thermographic and blood perfusion data when emotions of positive and negative valence are applied, where the blood perfusion data are obtained from the thermographic data. The images were obtained for baseline, positive, and negative valence according to the protocol of the Geneva Affective Picture Database. Absolute and percentage differences of average values of the data between the valences and the baseline were calculated for different regions of interest (forehead, periorbital eyes, cheeks, nose and upper lips). For negative valence, a decrease in temperature and blood perfusion was observed in the regions of interest, and the effect was greater on the left side than on the right side. In positive valence, the temperature and blood perfusion increased in some cases, showing a complex pattern. The temperature and perfusion of the nose was reduced for both valences, which is indicative of the arousal dimension. The blood perfusion images were found to be greater contrast; the percentage differences in the blood perfusion images are greater than those obtained in thermographic images. Moreover, the blood perfusion images, and vasomotor answer are consistent, therefore, they can be a better biomarker than thermographic analysis in identifying emotions.Comment: 22 pages, 9 figure

Sentiment Analysis on Social media network

Detecting changes in a data stream is very important area of research with several applications. In this project, we use a method for the detection and estimation of change. This strategy mainly dealing with distribution change when learning from data sequences which may vary with time. We use sliding window whose size, rather than being fixed a priori, is recomputed according to the rate of change determined from the data in the window itself. This delivers the user to guess a time-scale for change within the data stream. In this project we tend to use jio tweets in twitter as data stream. Reliance Jio network offers cost free services; the 100% satisfaction of its customer could be a doubtful one. Though the customers are availing Jio services, they spend some amount for using other networks. If Reliance Jio fails to give the full satisfaction to its customer, it is tough to sustain its image in the systematic nation. Hence the study is undertaken for the aim of analyzing the satisfaction level of the customer of Jio network. From Twitter, we gather tweets using Twitter API based on keywords #jio. This project can verify the sentiment orientation of the tweets and also detect the changes in tweeted words in terms of frequencies by applying ADWIN sliding window algorithm. Further we can visualize these results by plotting graphs and can understand how many people are positive and negative towards jio

Emotions in archetypal media content

Emotion is an intriguing and mysterious psychological phenomenon. While everyone seems to know what it is, researchers have not yet come to consensus on its definition, and many questions still remain unanswered. While the nature of emotion is yet to discover, the design community has noticed is importance, and poses the challenge of how emotion could inform design. We see the necessity to follow the state of the art in psychology and initiate the undertaking by exploring the emotional qualities in various types of media content. The first part of this thesis aims at constructing a theoretical framework. Recent years have seen empirical studies suggest that emotion could be unconscious. While this is to be further justified, scientists are motivated to reconsider current theories of emotion to account for this phenomenon. In light of this, we integrate these studies about unconscious emotion into our literature review. An overview from theory to practice is illustrated to provide a reference for viewing the current states in application domains, such as affective computing and emotional design. This review offers a holistic understanding about emotion from various perspectives, which allow us to look for new directions in future studies. Based on our review, we see a promising direction by applying psychoanalysis methods to analyze the media content as affective stimuli, and these stimuli can be evaluated by using quantitative measures to investigate the connection between the content and the corresponding emotions. The analysis on the media content is based on a psychoanalysis theory¿the theory of archetypes¿proposed by Carl Jung. He argues that there exists a universal pattern in humans¿ unconscious thoughts, which can be manifested as symbolic content in various forms of narratives, such as myth and fairy tales. Today, this archetypal symbolic content can be seen in modern media, particularly in movies. By applying the Jungian approach, we analyzed the symbolic meaning in movie scenes and edit these feature scenes into a collection of archetypal media content, which serve as the experimental materials for later explorations. In the second part of this thesis, we present three experimental studies that aim at determining if archetypal media content can be differentiated based on emotional responses. We adopted the psychoanalytical approach described earlier to collect feature scenes in movies as archetypal media content. Meanwhile, affective stimuli of explicit emotions are also included as benchmarks for comparison, such as sadness and joy. Self-reports and physiological signals are both adopted for measuring emotional responses. These three studies follow similar experimental design: presenting stimuli and measuring emotion concurrently. The results of these studies confirm that emotions induced by archetypal content are different from explicit emotions, and the statistical analysis further indicate that the predictive model obtained from physiological signals outperforms the model generated from self-reports while viewing archetypal media content. These results, however, are opposite to the results gained from affective stimuli of explicit emotions, leading us to the conclusion that archetypal media content might induce unconscious emotions, and physiological signals are more effective than self-reports for recognizing emotions induced by archetypal media content.La emoción es un fenómeno psicológico intrigante y misterioso. Aunque todo el mundo parece saber lo que es, los investigadores aún no han llegado a un consenso sobre su definición, y todavía quedan muchas preguntas sin respuesta. Si bien la naturaleza de las emociones está aún por descubrir, la comunidad de profesionales del diseño ha entendido su importancia, y se plantea el desafío de interrelacionar ambos mundos, explorando de las cualidades emocionales en diversos tipos de contenido en medios de comunicación. La primera parte de esta tesis tiene como objetivo la construcción de un marco teórico. Recientemente se han realizado estudios empíricos que sugieren que las emociones puede ser inconscientes. Si bien esto debe justificarse mejor, los científicos están motivados a reconsiderar las teorías actuales de la emoción para explicar este fenómeno. En vista de ello, integramos estos estudios sobre las emociones inconscientes en nuestra revisión de referencias bibliográficas incluyendo dominios de aplicación recientes, tales como la Computación Afectiva y el Diseño Emocional. Una dirección prometedora de investigación se basa en la aplicación de métodos del psicoanálisis para analizar contenidos multimedia como estímulos afectivos, y estos estímulos pueden ser evaluados mediante el uso de medidas cuantitativas para investigar la conexión entre el contenido y las emociones correspondientes. Este análisis se basa en la teoría de los arquetipos propuesto por el psicólogo Carl Jung. El autor sostiene que existe una patrón universal en los pensamientos inconscientes de los personas, que puede manifestarse como un símbolo contenido en las diversas formas de narrativas, como en los mitos y los cuentos de hadas. Hoy en día, estos arquetipos de contenido simbólico se puede ver frecuentemente en los contenidos multimedia modernos, sobre todo en las películas. Mediante la aplicación del enfoque de Jung, analizamos el significado simbólico en escenas de películas seleccionando las correspondientes a diversos arquetipos, que servirá como material experimental para exploraciones posteriores. En la segunda parte de esta tesis, se presentan tres estudios experimentales que apuntan a determinar si el contenido multimedia arquetípico puede diferenciarse en base a respuestas emocionales. Con el enfoque psicoanalítico descrito anteriormente para los arquetipos, también se incluye los estímulos afectivos de emociones explícitas son como puntos de referencia para la comparación, como la tristeza y la alegría. Se realizan auto-informes y se miden señales fisiológicas para la determinación de las respuestas emocionales en todos los experimentos realizados. Los resultados de estos estudios confirman que las emociones inducidas por arquetipos son diferentes de las emociones explícitas, y el análisis estadístico indica además que el modelo predictivo obtenido a partir de señales fisiológicas supera el modelo generado por los auto-informes durante la visualización de contenidos multimedia arquetípicos. Estos resultados, sin embargo, son opuestos a los resultados obtenidos a partir de los estímulos afectivos de emociones explícitas, llevándonos a la conclusión de que los contenidos de los medios arquetípicos podría inducir emociones inconscientes, y que las señales fisiológicas son más eficaces que los auto informes para el reconocimiento de las emociones inducidas por el contenido de medios arquetípico. En la tercera parte de esta tesis, exploramos cómo los contenidos arquetípicos podrían utilizarse para diseñar contenido multimedia mediante "mood boards". Se realizaron dos estudios con diseñadores para responder a la pregunta de investigación de si es posible generar contenido emocionalmente rico a través de la generación automática de contenido arquetípico por "mood boards" en comparación con el contenido multimedia no arquetípico

Using neural and distance-based machine learning techniques in order to identify genuine and acted emotions from facial expressions

Facial expressions are part of human non-verbal communication. Automatically discriminating between genuine and acted emotion can help psychologists, judges, human-machine interface, and so on. The problems for researchers starts when there are few real emotion facial datasets available, and thus, most of experimentation for evaluation is done by using fake emotions from actors. Thus, this paper explores the problem of classifying emotions from facial expressions as genuine or acted. We propose to extract facial features from images and to classify using k-Means, k-Nearest Neighbor and Neural Network. The best results obtained presented a promising 98.6% of precision for happiness emotion and 92% for sadness emotion

Opinion Mining for Software Development: A Systematic Literature Review

Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present 1) well-defined categories of opinion mining-related software development activities, 2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, 3) available datasets for performance evaluation and tool customization, and 4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities, and provide critical insights for the further development of opinion mining techniques in the SE domain