Lexical Resources and Semantic Similarity for Affective Evaluative Expressions Generation

Valitutti, Alessandro; Strapparava, Carlo; Stock, Oliviero

doi:10.1007/11573548_61

Cited by 18 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other dimensional models have been used, but generally with dimensions not driven by psychological theories but the output of corpora based approaches such as Latent Semantic Analysis (LSA). Example of this work are Bellegarda (2010) and Valitutti, Strapparava, and Stock (2005).…”

Section: Emotions In Textmentioning

confidence: 99%

Emotions in Text: Dimensional and Categorical Models

Calvo

Kim

2012

Computational Intelligence

153

View full text Add to dashboard Cite

GUEST EDITORS: DIANA INKPEN AND CARLO STRAPPARAVA Text often expresses the writer's emotional state or evokes emotions in the reader. The nature of emotional phenomena like reading and writing can be interpreted in different ways and represented with different computational models. Affective computing (AC) researchers often use a categorical model in which text data are associated with emotional labels. We introduce a new way of using normative databases as a way of processing text with a dimensional model and compare it with different categorical approaches. The approach is evaluated using four data sets of texts reflecting different emotional phenomena. An emotional thesaurus and a bag-of-words model are used to generate vectors for each pseudo-document, then for the categorical models three dimensionality reduction techniques are evaluated: Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Non-negative Matrix Factorization (NMF). For the dimensional model a normative database is used to produce three-dimensional vectors (valence, arousal, dominance) for each pseudo-document. This three-dimensional model can be used to generate psychologically driven visualizations. Both models can be used for affect detection based on distances amongst categories and pseudo-documents. Experiments show that the categorical model using NMF and the dimensional model tend to perform best.

show abstract

Section: Emotions In Textmentioning

confidence: 99%

Emotions in Text: Dimensional and Categorical Models

Calvo

Kim

2012

Computational Intelligence

153

View full text Add to dashboard Cite

show abstract

“…Starting from WordNet Affect, Valitutti et al [4] proposed a simple word presence method in order to detect emotions. Ma et al [12] designed an emotion extractor from chat logs, based on the same simple word presence.…”

Section: Related Workmentioning

confidence: 99%

“…In the field of Sentiment Analysis and Emotion Detection based on text data, two main directions for research exist: one concentrating on building better annotations of linguistic resources, such as dictionaries or ontologies [2], [3], and the other on building better classifiers for valence, sentiment or emotion detection [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Smile, Valence and NGram Features for Automatic Affect Detection

Șerban

Castellano

Pauchet

et al. 2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

View full text Add to dashboard Cite

This paper addresses the problem of feature fusion between smile, as a visual feature, and text, as a transcription result. The influence of smile over semantic data has been considered before, without investigating multiple approaches for the fusion. This problem is multi-modal, which makes it more difficult.The goal of this article is to investigate how this fusion could increase the current interactivity of a dialogue system by boosting the automatic detection rate of the sentiments expressed by a human user.There are two original propositions in our approach. The first lies in the use of a segmented detection for text data, rather than predicting a single label for every document (video). Second, this paper studies the importance of several features in the process of multi-modal fusion.Our approach uses basic features, such as NGrams, Smile Presence or Valence to find the best fusion approach. Moreover, we test a two level classification approach, using a SVM.

show abstract

“…Textual Semantics posits that the relevant semantic content of the lexicon actually corresponds to highly contextual categories rather than generic ones (this differs from the introduction of generic emotional tags [33] and corresponds to specific types of applications). This leads to a redefinition of lexical content in context, based on the identification of the most relevant semantic domains.…”

Section: Affinity(e R Low) Anger(e R High) Affinity(e C High)]mentioning

confidence: 99%

Interactive Storytelling with Literary Feelings

Pizzi

Charles

Lugrin

et al. 2007

Affective Computing and Intelligent Interaction

View full text Add to dashboard Cite

Abstract. In this paper, we describe the integration of Natural Language Processing (NLP) within an emotional planner to support Interactive Storytelling. Our emotional planner is based on a standard HSP planner, whose originality is drawn from altering the agents' beliefs and emotional states. Each character is driven by its own planner, while characters are able to operate on their reciprocal feelings thus affecting each other. Our baseline story is constituted by a classic XIX th century French novel from Gustave Flaubert in which characters feelings play a dominant role. This approach benefits from the fact that Flaubert has described a specific ontology for his characters feelings. The objective of NLP should be to uncover from natural language utterances the same kind of affective elements, which requires an integration between NLP and the planning component at the level of semantic content. This research is illustrated with examples from a first fully integrated prototype comprising NLP, emotional planning and real-time 3D animation.

show abstract

Lexical Resources and Semantic Similarity for Affective Evaluative Expressions Generation

Cited by 18 publications

References 7 publications

Emotions in Text: Dimensional and Categorical Models

Emotions in Text: Dimensional and Categorical Models

Fusion of Smile, Valence and NGram Features for Automatic Affect Detection

Interactive Storytelling with Literary Feelings

Contact Info

Product

Resources

About