Jesús M. Hermida

Detecting implicit expressions of emotion in text: A comparative analysis

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 specific 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.

Building and Exploiting EmotiNet, a Knowledge Base for Emotion Detection Based on the Appraisal Theory Model

The task of automatically detecting emotion in text is challenging. This is due to the fact that most of the times, textual expressions of affect are not direct-using emotion words-but result from the interpretation and assessment of the meaning of the concepts and interaction of concepts described in the text. This paper presents the core of EmotiNet, a new knowledge base (KB) for representing and storing affective reaction to real-life contexts, and the methodology employed in designing, populating, and evaluating it. The basis of the design process is given by a set of self-reported affective situations in the International Survey on Emotion Antecedents and Reactions (ISEAR) corpus. We cluster the examples and extract triples using Semantic Roles. We subsequently extend our model using other resources, such as VerbOcean, ConceptNet, and SentiWordNet, with the aim of generalizing the knowledge contained. Finally, we evaluate the approach using the representations of other examples in the ISEAR corpus. We conclude that EmotiNet, although limited by the domain and small quantity of knowledge it presently contains, represents a semantic resource appropriate for capturing and storing the structure and the semantics of real events and predicting the emotional responses triggered by chains of actions.

EmotiNet: a knowledge base for emotion detection in text built on the appraisal theories

The automatic detection of emotions is a difficult task in Artificial Intelligence. In the field of Natural Language Processing, the challenge of automatically detecting emotion from text has been tackled from many perspectives. Nonetheless, the majority of the approaches contemplated only the word level. Due to the fact that emotion is most of the times not expressed through specific words, but by evoking situations that have a commonsense affective meaning, the performance of existing systems is low. This article presents the EmotiNet knowledge base - a resource for the detection of emotion from text based on commonsense knowledge on concepts, their interaction and their affective consequence. The core of the resource is built from a set of self-reported affective situations and extended with external sources of commonsense knowledge on emotion-triggering concepts. The results of the preliminary evaluations show that the approach is appropriate for capturing and storing the structure and the semantics of real situations and predict the emotional responses triggered by actions presented in text.

Comparison of a textual versus a graphical notation for the maintainability of MDE domain models: an empirical pilot study

Models are a useful tool to increase the developer’s productivity and satisfaction when performing maintenance tasks. However, in order to maximise these advantages, the right selection of notations must be made. Unfortunately, the software engineering field lacks a body of empirical evidence that supports such selection. A suboptimal decision in this regard may have negative consequences over the maintenance process. The aim of the study was to compare a textual and a graphical notation with respect to the efficiency, effectiveness and satisfaction of junior software developers while performing analysability and modifiability tasks on two different applications. We have carried out a quasi-experiment with 86 third-year students of the Computer Engineering degree at the University of Alicante. Subjects were randomly classified into two groups, and each group performed 20 maintenance tasks with a textual and a graphical notation. We measured and compared the efficiency, effectiveness and satisfaction of subjects assigned to each treatment. The analysed data show that only the actual analysability coverage (AACov) and the actual modifiability efficiency (AMEffc) are affected by the type of notation used, regardless of the application. In both cases, subjects using the textual notation performed significantly better, although the effect size was low to moderate (AACov

Developing semantic rich internet applications using a model-driven approach

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Developing Rich Internet Applications as Social Sites on the Semantic Web: A Model-Driven Approach

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Developing semantic rich internet applications with the S m 4RIA extension for OIDE

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