Emotion intensities in Tweets

This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best–worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity; and, the extent to which two emotions are similar in terms of how they manifest in language

A sentiment analysis approach to increase authorship identification

Writing style is considered the manner in which an author expresses his thoughts, influenced by language characteristics, period, school, or nation. Often, this writing style can identify the author. One of the most famous examples comes from 1914 in Portuguese literature. With Fernando Pessoa and his heteronyms Alberto Caeiro, alvaro de Campos, and Ricardo Reis, who had completely different writing styles, led people to believe that they were different individuals. Currently, the discussion of authorship identification is more relevant because of the considerable amount of widespread fake news in social media, in which it is hard to identify who authored a text and even a simple quote can impact the public image of an author, especially if these texts or quotes are from politicians. This paper presents a process to analyse the emotion contained in social media messages such as Facebook to identify the author's emotional profile and use it to improve the ability to predict the author of the message. Using preprocessing techniques, lexicon-based approaches, and machine learning, we achieved an authorship identification improvement of approximately 5% in the whole dataset and more than 50% in specific authors when considering the emotional profile on the writing style, thus increasing the ability to identify the author of a text by considering only the author's emotional profile, previously detected from prior texts.FCT has supported this work – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Differential Emotions and the Stock Market - The Case of Company-Specific Trading

Practitioners and researchers alike increasingly use social media messages as an additional source of information to analyse stock price movements. In this regard, previous preliminary findings demonstrate the incremental value of considering the multi-dimensional structure of human emotions in sentiment analysis instead of the predominant assessment of the binary positive-negative valence of emotions. Therefore, based on emotion theory and an established sentiment lexicon, we develop and apply an open source dictionary for the analysis of seven different emotions (affection, happiness, satisfaction, fear, anger, depression, and contempt).To investigate the connection between the differential emotions and stock movements we analyse approximately 5.5 million Twitter messages on 33 S&P 100 companies and their respective NYSE stock prices from Yahoo!Finance over a period of three months. Subsequently, we conduct a lagged fixed-effects panel regression on the daily closing value differences. The results generally support the assumption of the necessity of considering a more differentiated sentiment. Moreover, comparing positive and negative valence, we find that only the average negative emotionality strength has a significant connection with company-specific stock price movements. The emotion specific analysis reveals that an increase in depression and happiness strength isassociated with a significant decrease in company-specific stock prices

WASSA-2017 shared task on emotion intensity

We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best–worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language

Sentiment Analysis of Textual Content in Social Networks. From Hand-Crafted to Deep Learning-Based Models

Aquesta tesi proposa diversos mètodes avançats per analitzar automàticament el contingut textual compartit a les xarxes socials i identificar les opinions, emocions i sentiments a diferents nivells d’anàlisi i en diferents idiomes. Comencem proposant un sistema d’anàlisi de sentiments, anomenat SentiRich, basat en un conjunt ric d’atributs, inclosa la informació extreta de lèxics de sentiments i models de word embedding pre-entrenats. A continuació, proposem un sistema basat en Xarxes Neurals Convolucionals i regressors XGboost per resoldre una sèrie de tasques d’anàlisi de sentiments i emocions a Twitter. Aquestes tasques van des de les tasques típiques d’anàlisi de sentiments fins a determinar automàticament la intensitat d’una emoció (com ara alegria, por, ira, etc.) i la intensitat del sentiment dels autors a partir dels seus tweets. També proposem un nou sistema basat en Deep Learning per solucionar el problema de classificació de les emocions múltiples a Twitter. A més, es va considerar el problema de l’anàlisi del sentiment depenent de l’objectiu. Per a aquest propòsit, proposem un sistema basat en Deep Learning que identifica i extreu l'objectiu dels tweets. Tot i que alguns idiomes, com l’anglès, disposen d’una àmplia gamma de recursos per permetre l’anàlisi del sentiment, a la majoria de llenguatges els hi manca. Per tant, utilitzem la tècnica d'anàlisi de sentiments entre idiomes per desenvolupar un sistema nou, multilingüe i basat en Deep Learning per a llenguatges amb pocs recursos lingüístics. Proposem combinar l’ajuda a la presa de decisions multi-criteri i anàlisis de sentiments per desenvolupar un sistema que permeti als usuaris la possibilitat d’explotar tant les opinions com les seves preferències en el procés de classificació d’alternatives. Finalment, vam aplicar els sistemes desenvolupats al camp de la comunicació de les marques de destinació a través de les xarxes socials. Amb aquesta finalitat, hem recollit tweets de persones locals, visitants i els gabinets oficials de Turisme de diferents destinacions turístiques i es van analitzar les opinions i les emocions compartides en ells. En general, els mètodes proposats en aquesta tesi milloren el rendiment dels enfocaments d’última generació i mostren troballes apassionants.Esta tesis propone varios métodos avanzados para analizar automáticamente el contenido textual compartido en las redes sociales e identificar opiniones, emociones y sentimientos, en diferentes niveles de análisis y en diferentes idiomas. Comenzamos proponiendo un sistema de análisis de sentimientos, llamado SentiRich, que está basado en un conjunto rico de características, que incluyen la información extraída de léxicos de sentimientos y modelos de word embedding previamente entrenados. Luego, proponemos un sistema basado en redes neuronales convolucionales y regresores XGboost para resolver una variedad de tareas de análisis de sentimientos y emociones en Twitter. Estas tareas van desde las típicas tareas de análisis de sentimientos hasta la determinación automática de la intensidad de una emoción (como alegría, miedo, ira, etc.) y la intensidad del sentimiento de los autores de los tweets. También proponemos un novedoso sistema basado en Deep Learning para abordar el problema de clasificación de emociones múltiples en Twitter. Además, consideramos el problema del análisis de sentimientos dependiente del objetivo. Para este propósito, proponemos un sistema basado en Deep Learning que identifica y extrae el objetivo de los tweets. Si bien algunos idiomas, como el inglés, tienen una amplia gama de recursos para permitir el análisis de sentimientos, la mayoría de los idiomas carecen de ellos. Por lo tanto, utilizamos la técnica de Análisis de Sentimiento Inter-lingual para desarrollar un sistema novedoso, multilingüe y basado en Deep Learning para los lenguajes con pocos recursos lingüísticos. Proponemos combinar la Ayuda a la Toma de Decisiones Multi-criterio y el análisis de sentimientos para desarrollar un sistema que brinde a los usuarios la capacidad de explotar las opiniones junto con sus preferencias en el proceso de clasificación de alternativas. Finalmente, aplicamos los sistemas desarrollados al campo de la comunicación de las marcas de destino a través de las redes sociales. Con este fin, recopilamos tweets de personas locales, visitantes, y gabinetes oficiales de Turismo de diferentes destinos turísticos y analizamos las opiniones y las emociones compartidas en ellos. En general, los métodos propuestos en esta tesis mejoran el rendimiento de los enfoques de vanguardia y muestran hallazgos interesa.This thesis proposes several advanced methods to automatically analyse textual content shared on social networks and identify people’ opinions, emotions and feelings at a different level of analysis and in different languages. We start by proposing a sentiment analysis system, called SentiRich, based on a set of rich features, including the information extracted from sentiment lexicons and pre-trained word embedding models. Then, we propose an ensemble system based on Convolutional Neural Networks and XGboost regressors to solve an array of sentiment and emotion analysis tasks on Twitter. These tasks range from the typical sentiment analysis tasks, to automatically determining the intensity of an emotion (such as joy, fear, anger, etc.) and the intensity of sentiment (aka valence) of the authors from their tweets. We also propose a novel Deep Learning-based system to address the multiple emotion classification problem on Twitter. Moreover, we considered the problem of target-dependent sentiment analysis. For this purpose, we propose a Deep Learning-based system that identifies and extracts the target of the tweets. While some languages, such as English, have a vast array of resources to enable sentiment analysis, most low-resource languages lack them. So, we utilise the Cross-lingual Sentiment Analysis technique to develop a novel, multi-lingual and Deep Learning-based system for low resource languages. We propose to combine Multi-Criteria Decision Aid and sentiment analysis to develop a system that gives users the ability to exploit reviews alongside their preferences in the process of alternatives ranking. Finally, we applied the developed systems to the field of communication of destination brands through social networks. To this end, we collected tweets of local people, visitors, and official brand destination offices from different tourist destinations and analysed the opinions and the emotions shared in these tweets

Using electroencephalograms to interpret and monitor the emotions

Le long voyage de la technologie a totalement changé la vie des gens : les humains ont marché sur la lune, les rovers1 découvrent Mars, les ordinateurs sont une partie inséparable de nos vies et dans le domaine de la santé, il y a des traitements pour beaucoup de maladies et l’espérance de vie a été significativement accrue. En plus, les scientifiques ont étudié les émotions humaines et ont essayé de les détecter en se basant sur differents paramètres tels que l'expression faciale, la parole et l'intonation, la réponse électrique et la communication verbale. Ces méthodes fonctionnent sur la base de l'effet des émotions humaines sur le corps et le comportement, mais l'émotion n'est pas le seul paramètre qui affecte le corps et les attitudes des gens. De plus, les personnes pourraient faire semblant, cacher leurs émotions et contrôler leurs réactions. En plus, les personnes ayant une limitation physique réduiront la précision de la reconnaissance correcte des émotions. Les chercheurs ont trouvé une relation directe entre les émotions et les activités cérébrales, les gens ou les limitations physiques ne pourront pas réduire la certitude de l'émotion détectée. Nous avons utilisé les EEG (électroencéphalogramme) pour créer un outil appelé Emotimap, qui détecte les émotions actuelles, surveille l'évolution émotionnelle en temps réel, et détecte également l'émotion générale. L'émotion actuelle est ressentie de façon brève et forte par rapport à des facteurs émotionnels et l’émotion générale est celle qui engage les gens pendant une longue période. Emotimap utilise des formules pour calculer l'Arousal et la Valence des personnes, puis ignore les sensations non stabilisées et cartographie le résultat sur les deux dimensions du diagramme de Circumplex de Russel pour détecter les émotions. Nous avons demandé à 20 personnes de participer à une expérimentation dans laquelle ils sont confrontés à différents facteurs émotionnels dans une réalité virtuelle (RV) isolée pour observer et sauvegarder l'information du facteur émotionnel et de leurs réactions. Les résultats montrent que les participants ont ressenti exactement ce qui était attendu avec le taux de 37,73% lorsque la distance métrique est seulement 1,55%, la distance métrique étant la proximité du ressenti et de l’émotion attendue (un plus petit nombre montre la plus grande similitude). La comparaison des différentes formules montre que les équations les plus similaires sont des formules avec le même nombre de capteurs utilisant avec et low le taux de 40% de similarité, avec une distance métrique entre 20% à 60%. La raison est que certaines équations détectent les émotions plus tôt et d'autres forment des pics d’amplitude plus grands. En détail, les relations mathématiques avec high détectent une émotion un peu plus tôt que les équations avec low. De plus, les formules avec douze capteurs suivent les changements très faibles d’amplitude des EEG, les formules avec quatre capteurs sont plus sensibles dans les pics, et les formules avec deux capteurs sont les plus sensibles. La comparaison de la similarité des réactions des participants avec la similarité des caractéristiques basées sur le test Big 5 montre que l'effet de la mémoire est plus fort que l'effet de leur caractéristiques. En analysant les résultats, Emotionap a pu détecter avec succès l'émotion générale du participant avec un taux de 95%.The long journey of technology has totally changed human’s life: human stepped on the moon, Mars rovers2 are discovering the Mars, computers are one inseparable part of our lives and in the health section, people live longer, there are treatments for lots of sicknesses. Also, scientists have studied human’s emotion and tried to detect their emotions based on different parameters such as facial expression, speech and intonation, electrical response, verbal communication. These methods work based on the effect of human emotions on body and behavior, but the emotion is not the only parameter that effect on body and attitudes of people. Additionally, people could pretend, hide their emotions and control their body and reactions, also, people with a limit on their body movement will reduce the accuracy of correct recognition of emotion. Researchers found a direct relation between emotions and brain activities, people or limit in body movement could not reduce the certainty of detected emotion. We used EEG (Electroencephalogram) to create a tool called Emotimap that detects current emotion, monitor emotional evolution in real-time, also detects the general emotion. Current emotion is brief and strong, emotions that people feel again any emotional factor and general emotion is the emotion that engages people for a long time. Emotimap uses formulas to calculate the Arousal and Valence of people then ignore unstabled feels and maps the result on the two dimensions Russel’s Circumplex diagram to detect the emotions. We asked 20 people to participate in an experimentation in which they faced up with different emotional factors in an isolated Virtual Reality (VR) to observe and save the information of emotional factor and their reactions. The results show participants have felt exactly what was expected with the rate of 37.73% when the metric distance is just 1.55%, metric distance is the proximity of felt and expected emotions (smaller number shows the more similarity). Comparing different formulas shows the most similar equations goes to formulas with the same number of sensors using and low with the rate of 40% of similarity, with metric distance in range of 20% to 60%. This is because some equations detect emotions sooner and some others are more strong in picks amplitude. In detaile, equation with high detects an emotion a little bit sooner then a equation with low, also, formulas with twelve sensors chase the EEG amplitude changes very weak when equation with four sensors are more sensible in picks, and the formulas with two sensors are the most sensible. Comparing the similarity of the participant reactions with their similarity of charactristics based on Big 5 test shows that the effect of memories is more stronger than the effect of their charactristic. Analysing the result shows Emotionap could successfully detect general emotion of participant with the rate of 95%

Exploring the sentiment of entrepreneurs on Twitter

Sentiment analysis is an evolving field of study that employs artificial intelligence techniques to identify the emotions and opinions expressed in a given text. Applying sentiment analysis to study the billions of messages that circulate in popular online social media platforms has raised numerous opportunities for exploring the emotional expressions of their users. In this paper we combine sentiment analysis with natural language processing and topic analysis techniques and conduct two different studies to examine whether engagement in entrepreneurship is associated with more positive emotions expressed on Twitter. In study 1, we investigate three samples with 6.717.308, 13.253.244, and 62.067.509 tweets respectively. We find that entrepreneurs express more positive emotions than non-entrepreneurs for most topics. We also find that social entrepreneurs express more positive emotions, and that serial entrepreneurs express less positive emotions than other entrepreneurs. In study 2, we use 21.491.962 tweets to explore 37.225 job-status changes by individuals who entered or quit entrepreneurship. We find that a job change to entrepreneurship is associated with a shift in the expression of emotions to more positive ones