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Emotion and Adaptation

Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

List of figures and tables Acknowledgments Prologue Part I. Theory and Function: 1. The structure of emotions 2. Intuitive and empirical approaches to understanding 3. Rationality and emotions 4. Mutual plans and social emotions Part II. Conflict and Unpredictability: 5. Plans and emotions in fictional narrative 6. Stress and distress 7. Freud's cognitive psychology of intention: the case of Dora Part III. Enjoyment and Creativity: 8. Happiness 9. Putting emotions into words Epilogue Notes References Author index Subject index.

Best-Laid Schemes: A Psychology of Emotions.

The evolution of sentiment analysis—A review of research topics, venues, and top cited papers

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.

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

Learning diaries are instruments through which students can reflect on their learning experience. Students' sentiments, emotions, opinions and attitudes are embedded in their learning diaries as part of the process of understanding their progress during the course and the self-awareness of their goals. Learning diaries are also a very informative feedback source for instructors regarding the students' emotional well-being. However the number of diaries created during a course can become a daunting task to be manually analyzed with care, particularly when the class is large. To tackle this problem, in this paper we present a functional system for analyzing and visualizing student emotions expressed in learning diaries. The system allows instructors to automatically extract emotions and the changes in these emotions throughout students' learning experience as expressed in their diaries. The emotions extracted by the system are based on Plutchik's eight emotion categories, and they are shown over the time period that the diaries were written. The potential impact and usefulness of our system are highlighted during our experiments with promising results for improving the communication between instructors and students and enhancing the learning experience.

https://web-ext.u-aizu.ac.jp/~mozgovoy/homepage/papers/mmms13.pdf

Exploiting sentiment analysis to track emotions in students' learning diaries

A considerable amount of effort has been made to reduce the physical manifestation of antisocial behaviour (ASB) in communities. However, the key to the early detection of ASB is, in many cases, in observing its manifestations in written language which has not been studied in detail. In this work, we search for linguistic features that pertain to ASB in order to use those features for the automatic identification of ASB in text. We use an ASB text corpus we have collected as a machine learning resource and approach the detection of ASB in text as a binary classification problem where discriminating features are taken from the linguistic representation of the text in the form bag-of-words and ontology-based emotion descriptors. Results from preliminary experiments show that by exploiting the emotional information together with Bag-of-Words (BoW) over 90% accuracy in the classification of ASB in text is reached. Our findings have positive implications in the early detection of potentially harmful behaviour. Povzetek: "[Click here and Enter short Abstract in Slovene language]"

/pdf/automatic-detection-of-antisocial-behaviour-in-texts-3oqyyyqmpx.pdf

Automatic Detection of Antisocial Behaviour in Texts

Research has shown that writing styles are influenced by an extensive array of factors that includes text genre and author's 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 author's gender classification.

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

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.

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

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