Understanding human perceptual experience in unstructured data on the web

Lee, Jun; Kim, Kyoung-Sook; Kwon, Yongjin; Ogawa, Hirotaka

doi:10.1145/3106426.3106430

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…The corpus used for KIM ontology consists of 100 HTML documents of news in politics and international business and politics in the United Kingdom. For the tests with DBPedia, a corpus compiled by Lee et al [16] named LP50 5 . It consists of 50 general purpose news documents with lengths between 50 and 126 words.…”

Section: Discussionmentioning

confidence: 99%

“…The large amount information stored and shared on the Web in form of unstructured documents [16] has caused difficulties for its search and retrieval. Traditionally two factors are used to classify the results of a search: 1) the relevance that measures the coincidence of the terms, and 2) the documents popularity, which is a complementary factor to documents ranking.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Annotation Approach for Information Search

Pech-May¹,

Rebollar²,

Magaña-Govea³

et al. 2019

RCS

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Due to the needs to improve the information search process, new strategies have been created to enhance searches. The semantic search performs the search by means of meaning instead of literals. The semantic search in unstructured documents requires to formalize knowledge through an annotation semantic process. Some annotation proposals use natural language processing tools, ontologies to link document terms; others use the similarity of entities through the weight of the edges, association between pair of concepts or the ontology structure. In this paper we present an alternative for semantic annotation in unstructured documents by semantic context extraction of entities. In the approach we detect the named entities through a data dictionary created from Wikipedia and link the instances in the ontology. The context extraction strategy is based on the concepts similarity; each term is associated with an instance of the ontology and the similarity between relationships explicit is measured by the combination of two types of measures: the association between each pair of concepts and the weight of the relationships. The approach was tested with two ontologies and two datasets in news and business, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semantic Annotation Approach for Information Search

Pech-May¹,

Rebollar²,

Magaña-Govea³

et al. 2019

RCS

View full text Add to dashboard Cite

show abstract

“…Although sensory experiences directly affect decision-making, actions, and sentiment formation in humans, few studies have considered sensation information itself in text mining. This paper focused on sensation analysis by extracting, quantifying, and identifying sensation information from sight (ophthalmoception), hearing (audioception), touch (tactioception), smell (olfacception), and taste (gustaoception) as introduced in [28]. Human perception is the process of selecting, organizing, and interpreting sensory information into meanings or concepts on an individualized basis.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, word sense disambiguation (WSD) and metaphor expression create difficulties in distinguishing sensations from opinions or sentiments, such as in, "He gave me a cool reception" and "All people are blind to their own mistakes". Here, quantitative features of sensation were extracted from social media with geographic locations based on a previous study in [28] and geo-spatial pattern differences in social perceptual experiences were investigated. The main contributions of this paper are threefold:…”

Section: Introductionmentioning

confidence: 99%

Spatial Footprints of Human Perceptual Experience in Geo-Social Media

Lee

Ogawa

Kwon

et al. 2018

IJGI

Self Cite

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Analyses of social media have increased in importance for understanding human behaviors, interests, and opinions. Business intelligence based on social media can reduce the costs of managing customer trend complexities. This paper focuses on analyzing sensation information representing human perceptual experiences in social media through the five senses: sight, hearing, touch, smell, and taste. First a measurement is defined to estimate social sensation intensities, and subsequently sensation characteristics on geo-social media are identified using geo-spatial footprints. Finally, we evaluate the accuracy and F-measure of our approach by comparing with baselines.

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Discovering underlying sensations of human emotions based on social media

Lee

Jatowt

Kim

2020

Asso for Info Science & Tech

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Analyzing social media has become a common way for capturing and understanding people's opinions, sentiments, interests, and reactions to ongoing events. Social media has thus become a rich and real‐time source for various kinds of public opinion and sentiment studies. According to psychology and neuroscience, human emotions are known to be strongly dependent on sensory perceptions. Although sensation is the most fundamental antecedent of human emotions, prior works have not looked into their relation to emotions based on social media texts. In this paper, we report the results of our study on sensation effects that underlie human emotions as revealed in social media. We focus on the key five types of sensations: sight, hearing, touch, smell, and taste. We first establish a correlation between emotion and sensation in terms of linguistic expressions. Then, in the second part of the paper, we define novel features useful for extracting sensation information from social media. Finally, we design a method to classify texts into ones associated with different types of sensations. The sensation dataset resulting from this research is opened to the public to foster further studies.

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Understanding human perceptual experience in unstructured data on the web

Cited by 4 publications

References 25 publications

Semantic Annotation Approach for Information Search

Semantic Annotation Approach for Information Search

Spatial Footprints of Human Perceptual Experience in Geo-Social Media

Discovering underlying sensations of human emotions based on social media

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