Calkin Suero Montero

Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text

A major limitation in the automatic detection of affect, feelings, emotions, sentiments, and opinions in text is the lack of proper differentiation between these subjective terms and understanding of how they relate to one another. This lack of differentiation not only leads to inconsistency in terminology usage but also makes the subtleties and nuances expressed by the five terms difficult to understand, resulting in subpar detection of the terms in text. In light of such limitation, this paper clarifies the differences between these five subjective terms and reveals significant concepts to the computational linguistics community for their effective detection and processing in text.

Serious storytelling --- a first definition and review

In human culture, storytelling is a long-established tradition. The reasons people tell stories are manifold: to entertain, to transfer knowledge between generations, to maintain cultural heritage, or to warn others of dangers. With the emergence of the digitisation of media, many new possibilities to tell stories in serious and non-entertainment contexts emerged. A very simple example is the idea of serious gaming, as in, digital games without the primary purpose of entertainment. In this paper, we introduce the term serious storytelling as a new potential media genre – defining serious storytelling as storytelling with a purpose beyond entertainment. We also put forward a review of existing potential application areas, and develop a framework for serious storytelling. We foresee several application areas for this fundamental concept, including wellbeing and health, medicine, psychology, education, ethical problem solving, e-leadership and management, qualitative journalism, serious digital games, simulations and virtual training, user experience studies, and online communication.

An Empirically Informed Taxonomy for the Maker Movement

The Maker Movement emerged from a renewed interest in the physical side of innovation following the dot-com bubble and the rise of the participatory Web 2.0 and the decreasing costs of many digital fabrication technologies. Classifying concepts, i.e. building taxonomies, is a fundamental practice when developing a topic of interest into a research field. Taking advantage of the growth of the Social Web and participation platforms, this paper suggests a multidisciplinary analysis of communications and online behaviors related to the Maker community in order to develop a taxonomy informed by current practices and ongoing discussions. We analyze a number of sources such as Twitter, Wikipedia and Google Trends, applying co-word analysis, trend visualizations and emotional analysis. Whereas co-words and trends extract structural characteristics of the movement, emotional analysis is non-topical, extracting emotional interpretations.

Investigating the Role of Emotion-Based Features in Author Gender Classification of Text

Research has shown that writing styles are influenced by an extensive array of factors that includes text genre and authors gender. Going beyond the analysis of linguistic features, such as n-grams, stylometric variables and word categories, this paper presents an exploratory study of the role that emotions expressed in writing play to aid discriminating author gender in different text genres. In this work, the gender classification task is seen as a binary classification problem where discriminating features are taken from a vectorial space that includes emotion-based features. Results show that by exploiting the emotional information present in personal journal diary texts, up to 80% cross-validation accuracy with support vector machine SVM algorithm can be reached. Over 75% cross-validation accuracy is reached when classifying the author gender of blog texts. Our findings show positive implications of emotion-based features on assisting authors gender classification.

Young people's engagement with their school grounds expressed through colour, symbol and lexical associations: a Finnish–British comparative study

This paper reports a comparative study in a Finnish school and a British school of the emotional associations made by 10–11-year-olds with their school grounds. Colour and symbol associations were used as a stimulus for getting students to engage with their school grounds, describe their feelings about them, and produce a lexicon of ‘emotion words’. The lexicon was used by the students in writing descriptive texts explaining ‘place meanings’ and ‘place attachments’. In addition to content analysis, sentiment analysis algorithms were used to yield hierarchical representations of the affective content of the texts. The detail of the most frequently used emotion words is reported and similarities and differences between the associations in the two different cultural settings are discussed. The lexicon associated with the school grounds in both schools was largely positive. The educational implications of supporting students in engaging with learning environments are briefly discussed.

TrustAider – Enhancing Trust in e-Leadership

Trust in leadership is significantly influenced by the current IT dominated business environment. We introduce TrustAider, a model for supporting trust building in a business environment through the use of text analysis methods and an interactive user interface. TrustAider integrates natural language processing technologies to provide feedback and suggestions on how to interact in a way that enhances mutual trust during the process of computer mediated communication between leaders, employees, and customers. This paper presents an overview of the TrustAider’s architecture and its key components. The effective functionalities of TrustAider for promoting trust are also demonstrated with a use case.

Towards automated e-counselling system based on counsellors emotion perception

Emotions are a core semantic component of human communication. Since counsellors are humans we assume that their own state of emotions could affect their intuitional effort when taking decisions concerning their clients. Therefore, the accuracy of detected emotions by counsellors could be doubtful. And this highlights the need for complementing the intuitional effort of counsellors by computational approach. Therefore, ascertaining the efficacy of computational algorithm, there is the need to benchmark with humans. In this paper, we explore empirically, the extent to which counsellors own emotional states influence their perception of emotions expressed in text. This influence is investigated through the level of agreement among counsellors when annotating emotions expressed in students’ personal life’s stories. The result shows strong intra-counsellor annotation agreement of emotions while inter-counsellors annotation agreement was low. Furthermore, the intra-annotation agreement of emotions was found to be strongly correlated to the counsellors’ self-reported emotions. We speculate, based on the findings, that the emotional state of counsellors influences their emotion perception while tracking emotions in text. Based on the results, we discuss the advantages of using an automated e-counselling system for emotion analysis.

Detecting the Likely Causes Behind the Emotion Spikes of Influential Twitter Users

Understanding the causes of spikes in the emotion flow of influential social media users is a key component when analyzing the diffusion and adoption of opinions and trends. Hence, in this work we focus on detecting the likely reasons or causes of spikes within influential Twitter users’ emotion flow. To achieve this, once an emotion spike is identified we use linguistic and statistical analyses on the tweets surrounding the spike in order to reveal the spike’s likely explanations or causes in the form of keyphrases. Experimental evaluation on emotion flow visualization, emotion spikes identification and likely cause extraction for several influential Twitter users shows that our method is effective for pinpointing interesting insights behind the causes of the emotion fluctuation. Implications of our work are highlighted by relating emotion flow spikes to real-world events and by the transversal application of our technique to other types of timestamped text.

EmoTwitter – A Fine-Grained Visualization System for Identifying Enduring Sentiments in Tweets

Traditionally, work on sentiment analysis focuses on detecting the positive and negative attributes of sentiments. To broaden the scope, we introduce the concept of enduring sentiments based on psychological descriptions of sentiments as enduring emotional dispositions that have formed over time. To aid us identify the enduring sentiments, we present a fine-grained functional visualization system, EmoTwitter, that takes tweets written over a period of time as input for analysis. Adopting a lexicon-based approach, the system identifies the Plutchik’s eight emotion categories and shows them over the time period that the tweets were written. The enduring sentiment patters of like and dislike are then calculated over the time period using the flow of the emotion categories. The potential impact and usefulness of our system are highlighted during a user-based evaluation. Moreover, the new concept and technique introduced in this paper for extracting enduring sentiments from text shows great potential, for instance, in business decision making.

EmotionExpert: Facebook game for crowdsourcing annotations for emotion detection

The current paper explores the use of the social network platform Facebook, as a source of emotion annotated textual data as well as a source of annotators. The traditional approach of hiring experts to provide manually labeled (annotated) data for NLP research is time-consuming, tedious and expensive. Hence, crowdsourcing has emerged as a useful method for obtaining annotated data for natural language processing (NLP) research. We have developed a purposeful innovative Facebook game called EmotionExpert in order to collect human annotated textual data for emotion detection from text. The game provides a means to reach a large number of players, while making the annotation of emotional content of texts an enjoyable and social activity. The findings reported in this paper indicate that EmotionExpert is a useful resource for reaching a large number of people to produce reliable annotations.