26 research outputs found

    Computational Sarcasm Analysis on Social Media: A Systematic Review

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    Sarcasm can be defined as saying or writing the opposite of what one truly wants to express, usually to insult, irritate, or amuse someone. Because of the obscure nature of sarcasm in textual data, detecting it is difficult and of great interest to the sentiment analysis research community. Though the research in sarcasm detection spans more than a decade, some significant advancements have been made recently, including employing unsupervised pre-trained transformers in multimodal environments and integrating context to identify sarcasm. In this study, we aim to provide a brief overview of recent advancements and trends in computational sarcasm research for the English language. We describe relevant datasets, methodologies, trends, issues, challenges, and tasks relating to sarcasm that are beyond detection. Our study provides well-summarized tables of sarcasm datasets, sarcastic features and their extraction methods, and performance analysis of various approaches which can help researchers in related domains understand current state-of-the-art practices in sarcasm detection.Comment: 50 pages, 3 tables, Submitted to 'Data Mining and Knowledge Discovery' for possible publicatio

    A novel Auto-ML Framework for Sarcasm Detection

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    Many domains have sarcasm or verbal irony presented in the text of reviews, tweets, comments, and dialog discussions. The purpose of this research is to classify sarcasm for multiple domains using the deep learning based AutoML framework. The proposed AutoML framework has five models in the model search pipeline, these five models are the combination of convolutional neural network (CNN), Long Short-Term Memory (LSTM), deep neural network (DNN), and Bidirectional Long Short-Term Memory (BiLSTM). The hybrid combination of CNN, LSTM, and DNN models are presented as CNN-LSTM-DNN, LSTM-DNN, BiLSTM-DNN, and CNN-BiLSTM-DNN. This work has proposed the algorithms that contrast polarities between terms and phrases, which are categorized into implicit and explicit incongruity categories. The incongruity and pragmatic features like punctuation, exclamation marks, and others integrated into the AutoML DeepConcat framework models. That integration was possible when the DeepConcat AutoML framework initiate a model search pipeline for five models to achieve better performance. Conceptually, DeepConcat means that model will integrate with generalized features. It was evident that the pretrain model BiLSTM achieved a better performance of 0.98 F1 when compared with the other five model performances. Similarly, the AutoML based BiLSTM-DNN model achieved the best performance of 0.98 F1, which is better than core approaches and existing state-of-the-art Tweeter tweet dataset, Amazon reviews, and dialog discussion comments. The proposed AutoML framework has compared performance metrics F1 and AUC and discovered that F1 is better than AUC. The integration of all feature categories achieved a better performance than the individual category of pragmatic and incongruity features. This research also evaluated the performance of the dropout layer hyperparameter and it achieved better performance than the fixed percentage like 10% of dropout parameter of the AutoML based Bayesian optimization. Proposed AutoML framework DeepConcat evaluated best pretrain models BiLSTM-DNN and CNN-CNN-DNN to transfer knowledge across domains like Amazon reviews and Dialog discussion comments (text) using the last strategy, full layer, and our fade-out freezing strategies. In the transfer learning fade-out strategy outperformed the existing state-of-the-art model BiLSTM-DNN, the performance is 0.98 F1 on tweets, 0.85 F1 on Amazon reviews, and 0.87 F1 on the dialog discussion SCV2-Gen dataset. Further, all strategies with various domains can be compared for the best model selection

    Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization

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    [ES] Hoy en día, la sociedad tiene acceso y posibilidad de contribuir a grandes cantidades de contenidos presentes en Internet, como redes sociales, periódicos online, foros, blogs o plataformas de contenido multimedia. Todo este tipo de medios han tenido, durante los últimos años, un impacto abrumador en el día a día de individuos y organizaciones, siendo actualmente medios predominantes para compartir, debatir y analizar contenidos online. Por este motivo, resulta de interés trabajar sobre este tipo de plataformas, desde diferentes puntos de vista, bajo el paraguas del Procesamiento del Lenguaje Natural. En esta tesis nos centramos en dos áreas amplias dentro de este campo, aplicadas al análisis de contenido en línea: análisis de texto en redes sociales y resumen automático. En paralelo, las redes neuronales también son un tema central de esta tesis, donde toda la experimentación se ha realizado utilizando enfoques de aprendizaje profundo, principalmente basados en mecanismos de atención. Además, trabajamos mayoritariamente con el idioma español, por ser un idioma poco explorado y de gran interés para los proyectos de investigación en los que participamos. Por un lado, para el análisis de texto en redes sociales, nos enfocamos en tareas de análisis afectivo, incluyendo análisis de sentimientos y detección de emociones, junto con el análisis de la ironía. En este sentido, se presenta un enfoque basado en Transformer Encoders, que consiste en contextualizar \textit{word embeddings} pre-entrenados con tweets en español, para abordar tareas de análisis de sentimiento y detección de ironía. También proponemos el uso de métricas de evaluación como funciones de pérdida, con el fin de entrenar redes neuronales, para reducir el impacto del desequilibrio de clases en tareas \textit{multi-class} y \textit{multi-label} de detección de emociones. Adicionalmente, se presenta una especialización de BERT tanto para el idioma español como para el dominio de Twitter, que tiene en cuenta la coherencia entre tweets en conversaciones de Twitter. El desempeño de todos estos enfoques ha sido probado con diferentes corpus, a partir de varios \textit{benchmarks} de referencia, mostrando resultados muy competitivos en todas las tareas abordadas. Por otro lado, nos centramos en el resumen extractivo de artículos periodísticos y de programas televisivos de debate. Con respecto al resumen de artículos, se presenta un marco teórico para el resumen extractivo, basado en redes jerárquicas siamesas con mecanismos de atención. También presentamos dos instancias de este marco: \textit{Siamese Hierarchical Attention Networks} y \textit{Siamese Hierarchical Transformer Encoders}. Estos sistemas han sido evaluados en los corpora CNN/DailyMail y NewsRoom, obteniendo resultados competitivos en comparación con otros enfoques extractivos coetáneos. Con respecto a los programas de debate, se ha propuesto una tarea que consiste en resumir las intervenciones transcritas de los ponentes, sobre un tema determinado, en el programa "La Noche en 24 Horas". Además, se propone un corpus de artículos periodísticos, recogidos de varios periódicos españoles en línea, con el fin de estudiar la transferibilidad de los enfoques propuestos, entre artículos e intervenciones de los participantes en los debates. Este enfoque muestra mejores resultados que otras técnicas extractivas, junto con una transferibilidad de dominio muy prometedora.[CA] Avui en dia, la societat té accés i possibilitat de contribuir a grans quantitats de continguts presents a Internet, com xarxes socials, diaris online, fòrums, blocs o plataformes de contingut multimèdia. Tot aquest tipus de mitjans han tingut, durant els darrers anys, un impacte aclaparador en el dia a dia d'individus i organitzacions, sent actualment mitjans predominants per compartir, debatre i analitzar continguts en línia. Per aquest motiu, resulta d'interès treballar sobre aquest tipus de plataformes, des de diferents punts de vista, sota el paraigua de l'Processament de el Llenguatge Natural. En aquesta tesi ens centrem en dues àrees àmplies dins d'aquest camp, aplicades a l'anàlisi de contingut en línia: anàlisi de text en xarxes socials i resum automàtic. En paral·lel, les xarxes neuronals també són un tema central d'aquesta tesi, on tota l'experimentació s'ha realitzat utilitzant enfocaments d'aprenentatge profund, principalment basats en mecanismes d'atenció. A més, treballem majoritàriament amb l'idioma espanyol, per ser un idioma poc explorat i de gran interès per als projectes de recerca en els que participem. D'una banda, per a l'anàlisi de text en xarxes socials, ens enfoquem en tasques d'anàlisi afectiu, incloent anàlisi de sentiments i detecció d'emocions, juntament amb l'anàlisi de la ironia. En aquest sentit, es presenta una aproximació basada en Transformer Encoders, que consisteix en contextualitzar \textit{word embeddings} pre-entrenats amb tweets en espanyol, per abordar tasques d'anàlisi de sentiment i detecció d'ironia. També proposem l'ús de mètriques d'avaluació com a funcions de pèrdua, per tal d'entrenar xarxes neuronals, per reduir l'impacte de l'desequilibri de classes en tasques \textit{multi-class} i \textit{multi-label} de detecció d'emocions. Addicionalment, es presenta una especialització de BERT tant per l'idioma espanyol com per al domini de Twitter, que té en compte la coherència entre tweets en converses de Twitter. El comportament de tots aquests enfocaments s'ha provat amb diferents corpus, a partir de diversos \textit{benchmarks} de referència, mostrant resultats molt competitius en totes les tasques abordades. D'altra banda, ens centrem en el resum extractiu d'articles periodístics i de programes televisius de debat. Pel que fa a l'resum d'articles, es presenta un marc teòric per al resum extractiu, basat en xarxes jeràrquiques siameses amb mecanismes d'atenció. També presentem dues instàncies d'aquest marc: \textit{Siamese Hierarchical Attention Networks} i \textit{Siamese Hierarchical Transformer Encoders}. Aquests sistemes s'han avaluat en els corpora CNN/DailyMail i Newsroom, obtenint resultats competitius en comparació amb altres enfocaments extractius coetanis. Pel que fa als programes de debat, s'ha proposat una tasca que consisteix a resumir les intervencions transcrites dels ponents, sobre un tema determinat, al programa "La Noche en 24 Horas". A més, es proposa un corpus d'articles periodístics, recollits de diversos diaris espanyols en línia, per tal d'estudiar la transferibilitat dels enfocaments proposats, entre articles i intervencions dels participants en els debats. Aquesta aproximació mostra millors resultats que altres tècniques extractives, juntament amb una transferibilitat de domini molt prometedora.[EN] Nowadays, society has access, and the possibility to contribute, to large amounts of the content present on the internet, such as social networks, online newspapers, forums, blogs, or multimedia content platforms. These platforms have had, during the last years, an overwhelming impact on the daily life of individuals and organizations, becoming the predominant ways for sharing, discussing, and analyzing online content. Therefore, it is very interesting to work with these platforms, from different points of view, under the umbrella of Natural Language Processing. In this thesis, we focus on two broad areas inside this field, applied to analyze online content: text analytics in social media and automatic summarization. Neural networks are also a central topic in this thesis, where all the experimentation has been performed by using deep learning approaches, mainly based on attention mechanisms. Besides, we mostly work with the Spanish language, due to it is an interesting and underexplored language with a great interest in the research projects we participated in. On the one hand, for text analytics in social media, we focused on affective analysis tasks, including sentiment analysis and emotion detection, along with the analysis of the irony. In this regard, an approach based on Transformer Encoders, based on contextualizing pretrained Spanish word embeddings from Twitter, to address sentiment analysis and irony detection tasks, is presented. We also propose the use of evaluation metrics as loss functions, in order to train neural networks for reducing the impact of the class imbalance in multi-class and multi-label emotion detection tasks. Additionally, a specialization of BERT both for the Spanish language and the Twitter domain, that takes into account inter-sentence coherence in Twitter conversation flows, is presented. The performance of all these approaches has been tested with different corpora, from several reference evaluation benchmarks, showing very competitive results in all the tasks addressed. On the other hand, we focused on extractive summarization of news articles and TV talk shows. Regarding the summarization of news articles, a theoretical framework for extractive summarization, based on siamese hierarchical networks with attention mechanisms, is presented. Also, we present two instantiations of this framework: Siamese Hierarchical Attention Networks and Siamese Hierarchical Transformer Encoders. These systems were evaluated on the CNN/DailyMail and the NewsRoom corpora, obtaining competitive results in comparison to other contemporary extractive approaches. Concerning the TV talk shows, we proposed a text summarization task, for summarizing the transcribed interventions of the speakers, about a given topic, in the Spanish TV talk shows of the ``La Noche en 24 Horas" program. In addition, a corpus of news articles, collected from several Spanish online newspapers, is proposed, in order to study the domain transferability of siamese hierarchical approaches, between news articles and interventions of debate participants. This approach shows better results than other extractive techniques, along with a very promising domain transferability.González Barba, JÁ. (2021). Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172245TESI

    A Comprehensive Review of Sentiment Analysis on Indian Regional Languages: Techniques, Challenges, and Trends

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    Sentiment analysis (SA) is the process of understanding emotion within a text. It helps identify the opinion, attitude, and tone of a text categorizing it into positive, negative, or neutral. SA is frequently used today as more and more people get a chance to put out their thoughts due to the advent of social media. Sentiment analysis benefits industries around the globe, like finance, advertising, marketing, travel, hospitality, etc. Although the majority of work done in this field is on global languages like English, in recent years, the importance of SA in local languages has also been widely recognized. This has led to considerable research in the analysis of Indian regional languages. This paper comprehensively reviews SA in the following major Indian Regional languages: Marathi, Hindi, Tamil, Telugu, Malayalam, Bengali, Gujarati, and Urdu. Furthermore, this paper presents techniques, challenges, findings, recent research trends, and future scope for enhancing results accuracy

    Image Sentiment Analysis of Social Media Data

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    Often a picture is worth a thousand words, and this is a small statement that represents one of the biggest challenges in the Image Sentiment Analysis area. The main theme of this dissertation is the Image Sentiment Analysis of social media, mainly from Twitter, so that it is identified as situations that represent risks (identification of negative situations) or that become a risk (prediction of negative situations). Despite the diversity of work done in the area of image sentiment analysis, it is still a challenging task. Several factors contribute to the difficulty, both more global factors likewise sociocultural issues, and issues within the scope of the analysis of feeling in images, such as the difficulty in finding reliable and properly labeled data to be used, as well as factors faced during the classification, for example, it is normal to associate images with darker colors and low brightness to negative feelings, after all, most are like that, but some cases escape this rule, and it is these cases that affect the accuracy of the developed models. However, in order to overcome these problems faced in classification, a multitasking model was developed, which will consider the entire image information, information from the salient areas in the images, and the facial expressions of faces contained in the images, and textual information, so that each component complements the other during classification. During the experiments it was possible to observe that the use of the proposed models can bring advantages for the classification of feeling in images and even work around some problems evidenced in existing works, such as the irony of the text. Therefore, this work aims to present the state of the art and the study carried out, in order to enable the presentation and implementation of the proposed model and carrying out the experiments and discussion of the results obtained, in order to verify the effectiveness of what was proposed. Finally, conclusions about the work done and future work will be presented.Muitas vezes uma imagem vale mais que mil palavras, e esta é uma pequena afirmação que representa um dos maiores desafios da área de classificação do sentimento contido nas imagens. O principal tema desta dissertação é a realização da análise do sentimento contido em imagens das mídias sociais, principalmente do Twitter, de modo que possam ser identificadas as situações que representam riscos (identificação de situações negativas) ou as quais possam se tornar um (previsão de situações negativas). Apesar da diversidade de trabalhos feitos na área da análise de sentimento em imagens, ainda é uma tarefa desafiante. Diversos fatores contribuem para a dificuldade , tantos fatores mais globais como questões socioculturais, quanto questões do próprio âmbito de análise de sentimento em imagens, como a dificuldade em achar dados confiáveis e devidamente etiquetados para serem utilizados, quanto fatores enfrentados durante a classificação, como por exemplo, é normal associar imagens com cores mais escuras e pouco brilho à sentimentos negativos, afinal a maioria é assim, entretanto há casos que fogem dessa regra, e são esses casos que afetam a precisão dos modelos desenvolvidos. Porém, visando contornar esses problemas enfrentados na classificação, foi desenvolvido um modelo multitarefas, o qual irá considerar informações globais, áreas salientes nas imagens, expressões faciais de rostos contidos nas imagens e informação textual, de modo que cada componente se complemente durante a classificação. Durante os experimentos foi possível observar que o uso dos modelos propostos podem trazer vantagens para a classificação do sentimento em imagens e até mesmo contornar alguns problemas evidenciados nos trabalhos já existentes, como por exemplo a ironia do texto. Assim sendo, este trabalho tem como objetivo apresentar o estado da arte e o estudo realizado, de modo a possibilitar a apresentação e implementação do modelo multitarefas proposto e realização das experiências e discussão dos resultados obtidos, de forma a verificar a eficácia do método proposto. Por fim, as conclusões sobre o trabalho feito e trabalho futuro serão apresentados

    Expressions of psychological stress on Twitter: detection and characterisation

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    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Long-term psychological stress is a significant predictive factor for individual mental health and short-term stress is a useful indicator of an immediate problem. Traditional psychology studies have relied on surveys to understand reasons for stress in general and in specific contexts. The popularity and ubiquity of social media make it a potential data source for identifying and characterising aspects of stress. Previous studies of stress in social media have focused on users responding to stressful personal life events. Prior social media research has not explored expressions of stress in other important domains, however, including travel and politics. This thesis detects and analyses expressions of psychological stress in social media. So far, TensiStrength is the only existing lexicon for stress and relaxation scores in social media. Using a word-vector based word sense disambiguation method, the TensiStrength lexicon was modified to include the stress scores of the different senses of the same word. On a dataset of 1000 tweets containing ambiguous stress-related words, the accuracy of the modified TensiStrength increased by 4.3%. This thesis also finds and reports characteristics of a multiple-domain stress dataset of 12000 tweets, 3000 each for airlines, personal events, UK politics, and London traffic. A two-step method for identifying stressors in tweets was implemented. The first step used LDA topic modelling and k-means clustering to find a set of types of stressors (e.g., delay, accident). Second, three word-vector based methods - maximum-word similarity, context-vector similarity, and cluster-vector similarity - were used to detect the stressors in each tweet. The cluster vector similarity method was found to identify the stressors in tweets in all four domains better than machine learning classifiers, based on the performance metrics of accuracy, precision, recall, and f-measure. Swearing and sarcasm were also analysed in high-stress and no-stress datasets from the four domains using a Convolutional Neural Network and Multilayer Perceptron, respectively. The presence of swearing and sarcasm was higher in the high-stress tweets compared to no-stress tweets in all the domains. The stressors in each domain with higher percentages of swearing or sarcasm were identified. Furthermore, the distribution of the temporal classes (past, present, future, and atemporal) in high-stress tweets was found using an ensemble classifier. The distribution depended on the domain and the stressors. This study contributes a modified and improved lexicon for the identification of stress scores in social media texts. The two-step method to identify stressors follows a general framework that can be used for domains other than those which were studied. The presence of swearing, sarcasm, and the temporal classes of high-stress tweets belonging to different domains are found and compared to the findings from traditional psychology, for the first time. The algorithms and knowledge may be useful for travel, political, and personal life systems that need to identify stressful events in order to take appropriate action.European Union's Horizon 2020 research and innovation programme under grant agreement No 636160-2, the Optimum project (www.optimumproject.eu)

    Experiments on Satire Detection for Norwegian News Articles

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    Masteroppgave i informasjonsvitenskapINFO390MASV-INF

    Mapping (Dis-)Information Flow about the MH17 Plane Crash

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    Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators
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