Emotion recognition for sentences with unknown expressions based on semantic similarity by using Bag of Concepts

Matsumoto, Kazuyuki; Yoshida, Minoru; Xiao, Qingmei; Luo, Xin; Kita, Kiyoshi

doi:10.1109/fskd.2015.7382148

Cited by 6 publications

(1 citation statement)

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“…Matsumoto et al [11] proposed a method to estimate emotion in utterances including grammatically incorrect expressions such as Internet slangs. In the case of such casual expressions, it is thought to be more effective to take a method by machine learning based on a large scale natural language corpus than to register the knowledge into a dictionary.…”

Section: Introductionmentioning

confidence: 99%

Utterance Emotion Estimation by Using Feature of Syntactic Pattern

Matsumoto¹

2021

Information Systems - Intelligent Information Processing Systems, Natural Language Processing, Affective Computing and Artifici

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Emotion has been defined as basic emotions by various researchers, however, there are not many studies describing the relation between emotion and language patterns in detail based on statistical information. There are various languages all over the world, and even a language of the same country has different writing styles/expressions depending on which language media is used or who is a writer/speaker, which is thought to make it difficult to analyze the relation of emotion and language patterns. The author has been engaged in constructing and analyzing emotion corpora in some domains based on different sources. From the analysis results, emotion expressions started to become more understood that they have differences and tendencies according to the attributes of the writers and the speakers. In this chapter, I focused on the differences detected in the attributes of the writer/speaker with respect to language patterns; in usage tendencies or combinations of words, unknown expressions (slangs), sentence patterns, non-verbal expressions (emoji, emoticon, etc.) with relevant emotions, then introduce the outcome of the analytical survey on a large scale corpus obtained from a social networking service.

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Section: Introductionmentioning

confidence: 99%

Utterance Emotion Estimation by Using Feature of Syntactic Pattern

Matsumoto¹

2021

Information Systems - Intelligent Information Processing Systems, Natural Language Processing, Affective Computing and Artifici

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Prototype Theory Meets Word Embedding: A Novel Approach for Text Categorization via Granular Computing

Santis

Rizzi

2023

Cogn Comput

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The problem of the information representation and interpretation coming from senses by the brain has plagued scientists for decades. The same problems, from a different perspective, hold in automated Pattern Recognition systems. Specifically, in solving various NLP tasks, an ever better and richer semantic representation of text as a set of features is needed and a plethora of text embedding techniques in algebraic spaces are continuously provided by researchers. These spaces are well suited to be conceived as conceptual spaces in light of the Gärdenfors’s Conceptual Space theory, which, within the Cognitive Science paradigm, seeks a geometrization of thought that bridges the gap between an associative lower level and a symbolic higher level in which information is organized and processed and where inductive reasoning is appropriate. Granular Computing can offer the toolbox for granulating text that can be represented by more abstract entities than words, offering a good hierarchical representation of the text embedded in an algebraic space driving Machine Learning applications, specifically, in text mining tasks. In this paper, the Conceptual Space Theory, the Granular Computing approach and Machine Learning are bound in a novel common framework for solving some text categorization tasks with both standard classifiers suited for working with $$\mathbb {R}^n$$ R n vectors and a Recurrent Neural Network (RNN) — an LSTM — able to deal with sequences. Instead of working with word vectors, the algorithms process more abstract entities (concepts), where patterns, in a first approach, are obtained through the construction of a symbolic histogram starting from a suitable set of information granules, representing a document as a distribution of concepts. For the RNN case, as a further novelty, a text is represented as a random walk over prototypes within the conceptual space synthesized over a suitable text embedding procedure. A comparison of the performance and a critical discussion are offered for both a neural embedding technique and the well-known LSA, showing how the conceptual level leads also to Knowledge Discovery applications.

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