Extending the EmotiNet Knowledge Base to Improve the Automatic Detection of Implicitly Expressed Emotions from Text

Sentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. Most existing approaches are based on word-level analysis of texts and are mostly able to detect only explicit expressions of sentiment. However, in many cases, emotions are not expressed by using words with an affective meaning (e.g. happy), but by describing real-life situations, which readers (based on their commonsense knowledge) detect as being related to a specic emotion. Given the challenges of detecting emotions from contexts in which no lexical clue is present, in this article we present a comparative analysis between the performance of well-established methods for emotion detection (supervised and lexical knowledge-based) and a method we propose and extend, which is based on commonsense knowledge stored in the EmotiNet knowledge base. Our extensive evaluations show that, in the context of this task, the approach based on EmotiNet is the most appropriate.JRC.G.2-Global security and crisis managemen

It's LeVAsa not LevioSA! Latent Encodings for Valence-Arousal Structure Alignment

In recent years, great strides have been made in the field of affective computing. Several models have been developed to represent and quantify emotions. Two popular ones include (i) categorical models which represent emotions as discrete labels, and (ii) dimensional models which represent emotions in a Valence-Arousal (VA) circumplex domain. However, there is no standard for annotation mapping between the two labelling methods. We build a novel algorithm for mapping categorical and dimensional model labels using annotation transfer across affective facial image datasets. Further, we utilize the transferred annotations to learn rich and interpretable data representations using a variational autoencoder (VAE). We present "LeVAsa", a VAE model that learns implicit structure by aligning the latent space with the VA space. We evaluate the efficacy of LeVAsa by comparing performance with the Vanilla VAE using quantitative and qualitative analysis on two benchmark affective image datasets. Our results reveal that LeVAsa achieves high latent-circumplex alignment which leads to improved downstream categorical emotion prediction. The work also demonstrates the trade-off between degree of alignment and quality of reconstructions.Comment: 5 pages, 4 figures and 3 table

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

A review of sentiment analysis research in Arabic language

Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Arabic sentiment analysis so far. In this paper, we carry out an in-depth qualitative study of the most important research works in this context by presenting limits and strengths of existing approaches. In particular, we survey both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language

Sentiment Analysis for micro-blogging platforms in Arabic

Sentiment Analysis (SA) concerns the automatic extraction and classification of sentiments conveyed in a given text, i.e. labelling a text instance as positive, negative or neutral. SA research has attracted increasing interest in the past few years due to its numerous real-world applications. The recent interest in SA is also fuelled by the growing popularity of social media platforms (e.g. Twitter), as they provide large amounts of freely available and highly subjective content that can be readily crawled. Most previous SA work has focused on English with considerable success. In this work, we focus on studying SA in Arabic, as a less-resourced language. This work reports on a wide set of investigations for SA in Arabic tweets, systematically comparing three existing approaches that have been shown successful in English. Specifically, we report experiments evaluating fully-supervised-based (SL), distantsupervision- based (DS), and machine-translation-based (MT) approaches for SA. The investigations cover training SA models on manually-labelled (i.e. in SL methods) and automatically-labelled (i.e. in DS methods) data-sets. In addition, we explored an MT-based approach that utilises existing off-the-shelf SA systems for English with no need for training data, assessing the impact of translation errors on the performance of SA models, which has not been previously addressed for Arabic tweets. Unlike previous work, we benchmark the trained models against an independent test-set of >3.5k instances collected at different points in time to account for topic-shifts issues in the Twitter stream. Despite the challenging noisy medium of Twitter and the mixture use of Dialectal and Standard forms of Arabic, we show that our SA systems are able to attain performance scores on Arabic tweets that are comparable to the state-of-the-art SA systems for English tweets. The thesis also investigates the role of a wide set of features, including syntactic, semantic, morphological, language-style and Twitter-specific features. We introduce a set of affective-cues/social-signals features that capture information about the presence of contextual cues (e.g. prayers, laughter, etc.) to correlate them with the sentiment conveyed in an instance. Our investigations reveal a generally positive impact for utilising these features for SA in Arabic. Specifically, we show that a rich set of morphological features, which has not been previously used, extracted using a publicly-available morphological analyser for Arabic can significantly improve the performance of SA classifiers. We also demonstrate the usefulness of languageindependent features (e.g. Twitter-specific) for SA. Our feature-sets outperform results reported in previous work on a previously built data-set

Emotion Quantification Using Variational Quantum State Fidelity Estimation

Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset

Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data

In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvestigated. Sentiment analysis approaches mostly include spell checking or rule-based normalisation as preprocess- ing and rarely investigate its impact on the task performance. We present an optimised sentiment classifier and investigate to what extent its performance can be enhanced by integrating SMT-based normalisation as preprocessing. Experiments on a test set comprising a variety of user-generated content genres revealed that normalisation improves sentiment classification performance on tweets and blog posts, showing the model’s ability to generalise to other data genres

Irony and Sarcasm Detection in Twitter: The Role of Affective Content

Tesis por compendioSocial media platforms, like Twitter, offer a face-saving ability that allows users to express themselves employing figurative language devices such as irony to achieve different communication purposes. Dealing with such kind of content represents a big challenge for computational linguistics. Irony is closely associated with the indirect expression of feelings, emotions and evaluations. Interest in detecting the presence of irony in social media texts has grown significantly in the recent years. In this thesis, we introduce the problem of detecting irony in social media under a computational linguistics perspective. We propose to address this task by focusing, in particular, on the role of affective information for detecting the presence of such figurative language device. Attempting to take advantage of the subjective intrinsic value enclosed in ironic expressions, we present a novel model, called emotIDM, for detecting irony relying on a wide range of affective features. For characterising an ironic utterance, we used an extensive set of resources covering different facets of affect from sentiment to finer-grained emotions. Results show that emotIDM has a competitive performance across the experiments carried out, validating the effectiveness of the proposed approach. Another objective of the thesis is to investigate the differences among tweets labeled with #irony and #sarcasm. Our aim is to contribute to the less investigated topic in computational linguistics on the separation between irony and sarcasm in social media, again, with a special focus on affective features. We also studied a less explored hashtag: #not. We find data-driven arguments on the differences among tweets containing these hashtags, suggesting that the above mentioned hashtags are used to refer different figurative language devices. We identify promising features based on affect-related phenomena for discriminating among different kinds of figurative language devices. We also analyse the role of polarity reversal in tweets containing ironic hashtags, observing that the impact of such phenomenon varies. In the case of tweets labeled with #sarcasm often there is a full reversal, whereas in the case of those tagged with #irony there is an attenuation of the polarity. We analyse the impact of irony and sarcasm on sentiment analysis, observing a drop in the performance of NLP systems developed for this task when irony is present. Therefore, we explored the possible use of our findings in irony detection for the development of an irony-aware sentiment analysis system, assuming that the identification of ironic content could help to improve the correct identification of sentiment polarity. To this aim, we incorporated emotIDM into a pipeline for determining the polarity of a given Twitter message. We compared our results with the state of the art determined by the "Semeval-2015 Task 11" shared task, demonstrating the relevance of considering affective information together with features alerting on the presence of irony for performing sentiment analysis of figurative language for this kind of social media texts. To summarize, we demonstrated the usefulness of exploiting different facets of affective information for dealing with the presence of irony in Twitter.Las plataformas de redes sociales, como Twitter, ofrecen a los usuarios la posibilidad de expresarse de forma libre y espontanea haciendo uso de diferentes recursos lingüísticos como la ironía para lograr diferentes propósitos de comunicación. Manejar ese tipo de contenido representa un gran reto para la lingüística computacional. La ironía está estrechamente vinculada con la expresión indirecta de sentimientos, emociones y evaluaciones. El interés en detectar la presencia de ironía en textos de redes sociales ha aumentado significativamente en los últimos años. En esta tesis, introducimos el problema de detección de ironía en redes sociales desde una perspectiva de la lingüística computacional. Proponemos abordar dicha tarea enfocándonos, particularmente, en el rol de información relativa al afecto y las emociones para detectar la presencia de dicho recurso lingüístico. Con la intención de aprovechar el valor intrínseco de subjetividad contenido en las expresiones irónicas, presentamos un modelo para detectar la presencia de ironía denominado emotIDM, el cual está basado en una amplia variedad de rasgos afectivos. Para caracterizar instancias irónicas, utilizamos un amplio conjunto de recursos que cubren diferentes ámbitos afectivos: desde sentimientos (positivos o negativos) hasta emociones específicas definidas con una granularidad fina. Los resultados obtenidos muestran que emotIDM tiene un desempeño competitivo en los experimentos realizados, validando la efectividad del enfoque propuesto. Otro objetivo de la tesis es investigar las diferencias entre tweets etiquetados con #irony y #sarcasm. Nuestra finalidad es contribuir a un tema menos investigado en lingüística computacional: la separación entre el uso de ironía y sarcasmo en redes sociales, con especial énfasis en rasgos afectivos. Además, estudiamos un hashtag que ha sido menos analizado: #not. Nuestros resultados parecen evidenciar que existen diferencias entre los tweets que contienen dichos hashtags, sugiriendo que son utilizados para hacer referencia de diferentes recursos lingüísticos. Identificamos un conjunto de características basadas en diferentes fenómenos afectivos que parecen ser útiles para discriminar entre diferentes tipos de recursos lingüísticos. Adicionalmente analizamos la reversión de polaridad en tweets que contienen hashtags irónicos, observamos que el impacto de dicho fenómeno es diferente en cada uno de ellos. En el caso de los tweets que están etiquetados con el hashtag #sarcasm, a menudo hay una reversión total, mientras que en el caso de los tweets etiquetados con el hashtag #irony se produce una atenuación de la polaridad. Llevamos a cabo un estudio del impacto de la ironía y el sarcasmo en el análisis de sentimientos, observamos una disminución en el rendimiento de los sistemas de PLN desarrollados para dicha tarea cuando la ironía está presente. Por consiguiente, exploramos la posibilidad de utilizar nuestros resultados en detección de ironía para el desarrollo de un sistema de análisis de sentimientos que considere de la presencia de ironía, suponiendo que la detección de contenido irónico podría ayudar a mejorar la correcta identificación del sentimiento expresado en un texto dado. Con este objetivo, incorporamos emotIDM como la primera fase en un sistema de análisis de sentimientos para determinar la polaridad de mensajes en Twitter. Comparamos nuestros resultados con el estado del arte establecido en la tarea de evaluación "Semeval-2015 Task 11", demostrando la importancia de utilizar información afectiva en conjunto con características que alertan de la presencia de la ironía para desempeñar análisis de sentimientos en textos con lenguaje figurado que provienen de redes sociales. En resumen, demostramos la utilidad de aprovechar diferentes aspectos de información relativa al afecto y las emociones para tratar cuestiones relativas a la presencia de la ironíLes plataformes de xarxes socials, com Twitter, oferixen als usuaris la possibilitat d'expressar-se de forma lliure i espontània fent ús de diferents recursos lingüístics com la ironia per aconseguir diferents propòsits de comunicació. Manejar aquest tipus de contingut representa un gran repte per a la lingüística computacional. La ironia està estretament vinculada amb l'expressió indirecta de sentiments, emocions i avaluacions. L'interés a detectar la presència d'ironia en textos de xarxes socials ha augmentat significativament en els últims anys. En aquesta tesi, introduïm el problema de detecció d'ironia en xarxes socials des de la perspectiva de la lingüística computacional. Proposem abordar aquesta tasca enfocant-nos, particularment, en el rol d'informació relativa a l'afecte i les emocions per detectar la presència d'aquest recurs lingüístic. Amb la intenció d'aprofitar el valor intrínsec de subjectivitat contingut en les expressions iròniques, presentem un model per a detectar la presència d'ironia denominat emotIDM, el qual està basat en una àmplia varietat de trets afectius. Per caracteritzar instàncies iròniques, utilitzàrem un ampli conjunt de recursos que cobrixen diferents àmbits afectius: des de sentiments (positius o negatius) fins emocions específiques definides de forma molt detallada. Els resultats obtinguts mostres que emotIDM té un rendiment competitiu en els experiments realitzats, validant l'efectivitat de l'enfocament proposat. Un altre objectiu de la tesi és investigar les diferències entre tweets etiquetats com a #irony i #sarcasm. La nostra finalitat és contribuir a un tema menys investigat en lingüística computacional: la separació entre l'ús d'ironia i sarcasme en xarxes socials, amb especial èmfasi amb els trets afectius. A més, estudiem un hashtag que ha sigut menys estudiat: #not. Els nostres resultats pareixen evidenciar que existixen diferències entre els tweets que contenen els hashtags esmentats, cosa que suggerix que s'utilitzen per fer referència de diferents recursos lingüístics. Identifiquem un conjunt de característiques basades en diferents fenòmens afectius que pareixen ser útils per a discriminar entre diferents tipus de recursos lingüístics. Addicionalment analitzem la reversió de polaritat en tweets que continguen hashtags irònics, observant que l'impacte del fenomen esmentat és diferent per a cadascun d'ells. En el cas dels tweet que estan etiquetats amb el hashtag #sarcasm, a sovint hi ha una reversió total, mentre que en el cas dels tweets etiquetats amb el hashtag #irony es produïx una atenuació de polaritat. Duem a terme un estudi de l'impacte de la ironia i el sarcasme en l'anàlisi de sentiments, on observem una disminució en el rendiment dels sistemes de PLN desenvolupats per a aquestes tasques quan la ironia està present. Per consegüent, vam explorar la possibilitat d'utilitzar els nostres resultats en detecció d'ironia per a desenvolupar un sistema d'anàlisi de sentiments que considere la presència d'ironia, suposant que la detecció de contingut irònic podria ajudar a millorar la correcta identificació del sentiment expressat en un text donat. Amb aquest objectiu, incorporem emotIDM com la primera fase en un sistema d'anàlisi de sentiments per determinar la polaritat de missatges en Twitter. Hem comparat els nostres resultats amb l'estat de l'art establert en la tasca d'avaluació "Semeval-2015 Task 11", demostrant la importància d'utilitzar informació afectiva en conjunt amb característiques que alerten de la presència de la ironia per exercir anàlisi de sentiments en textos amb llenguatge figurat que provenen de xarxes socials. En resum, hem demostrat la utilitat d'aprofitar diferents aspectes d'informació relativa a l'afecte i les emocions per tractar qüestions relatives a la presència d'ironia en Twitter.Hernández Farias, DI. (2017). Irony and Sarcasm Detection in Twitter: The Role of Affective Content [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90544TESISCompendi