Towards a Semantic-Based Approach for Affect and Metaphor Detection

2013 ◽  
Vol 11 (2) ◽  
pp. 48-65 ◽  
Author(s):  
Li Zhang ◽  
John Barnden
Keyword(s):  
Gesture Recognition ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Intelligent Agent ◽  
Semantic Interpretation ◽  
Emotional Expressions ◽  
Sentence Level ◽  
Affect Detection ◽  
Research Goal ◽  
Initial Exploration

Affect detection from open-ended virtual improvisational contexts is a challenging task. To achieve this research goal, the authors developed an intelligent agent which was able to engage in virtual improvisation and perform sentence-level affect detection from user inputs. This affect detection development was efficient for the improvisational inputs with strong emotional indicators. However, it can also be fooled by the diversity of emotional expressions such as expressions with weak or no affect indicators or metaphorical affective inputs. Moreover, since the improvisation often involves multi-party conversations with several threads of discussions happening simultaneously, the previous development was unable to identify the different discussion contexts and the most intended audiences to inform affect detection. Therefore, in this paper, the authors employ latent semantic analysis to find the underlying semantic structures of the emotional expressions and identify topic themes and target audiences especially for those inputs without strong affect indicators to improve affect detection performance. They also discuss how such semantic interpretation of dialog contexts is used to identify metaphorical phenomena. Initial exploration on affect detection from gestures is also discussed to interpret users’ experience of using the system and provide an extra channel to detect affect embedded in the virtual improvisation. Their work contributes to the journal themes on affect sensing from text, semantic-based dialogue processing and emotional gesture recognition.

scholarly journals Affect Detection from Text-Based Virtual Improvisation and Emotional Gesture Recognition

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Li Zhang ◽  
Bryan Yap
Keyword(s):  
Gesture Recognition ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Intelligent Agent ◽  
Body Language ◽  
Semantic Interpretation ◽  
Sentence Level ◽  
Affect Detection ◽  
Language Detection ◽  
Initial Exploration

We have developed an intelligent agent to engage with users in virtual drama improvisation previously. The intelligent agent was able to perform sentence-level affect detection from user inputs with strong emotional indicators. However, we noticed that many inputs with weak or no affect indicators also contain emotional implication but were regarded as neutral expressions by the previous interpretation. In this paper, we employ latent semantic analysis to perform topic theme detection and identify target audiences for such inputs. We also discuss how such semantic interpretation of the dialog contexts is used to interpret affect more appropriately during virtual improvisation. Also, in order to build a reliable affect analyser, it is important to detect and combine weak affect indicators from other channels such as body language. Such emotional body language detection also provides a nonintrusive channel to detect users’ experience without interfering with the primary task. Thus, we also make initial exploration on affect detection from several universally accepted emotional gestures.

scholarly journals Multi-Label Classification of Contributing Causal Factors in Self-Reported Safety Narratives

Safety ◽  
2018 ◽  
Vol 4 (3) ◽  
pp. 30 ◽  
Author(s):  
Saul Robinson
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Aviation Safety ◽  
Automated Classification ◽  
Contributing Factors ◽  
Causal Factors ◽  
Sentence Level ◽  
Primary Problem

Three methods are demonstrated for automated classification of aviation safety narratives within an existing complex taxonomy. Utilizing latent semantic analysis trained against 4497 narratives at the sentence level, primary problem and contributing factor labels were assessed. Results from a sample of 2987 narratives provided a mean unsupervised categorization precision of 0.35% and recall of 0.78% for contributing-factors within the taxonomy. Categorization of the primary problem at the sentence level resulted in a modal accuracy of 0.46%. Overall, the results suggested that the demonstrated approaches were viable in bringing additional tools and insights to safety researchers.

Task Estimation Using Latent Semantic Analysis of Visual Scenes and Spoken Words

2012 ◽  
Vol 132 (9) ◽  
pp. 1473-1480
Author(s):  
Masashi Kimura ◽  
Shinta Sawada ◽  
Yurie Iribe ◽  
Kouichi Katsurada ◽  
Tsuneo Nitta
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Spoken Words ◽  
Visual Scenes

Similarity Detection Using Latent Semantic Analysis Algorithm

2018 ◽  
Vol 6 (8) ◽  
pp. 102
Author(s):  
Priyanka R. Patil ◽  
Shital A. Patil
Keyword(s):  
Latent Semantic Analysis ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Mining Method ◽  
Research Papers ◽  
Information Measures ◽  
Automated Software ◽  
Day By Day ◽  
Ways Of Life ◽  
Dirichlet Allocation

Similarity View is an application for visually comparing and exploring multiple models of text and collection of document. Friendbook finds ways of life of clients from client driven sensor information, measures the closeness of ways of life amongst clients, and prescribes companions to clients if their ways of life have high likeness. Roused by demonstrate a clients day by day life as life records, from their ways of life are separated by utilizing the Latent Dirichlet Allocation Algorithm. Manual techniques can't be utilized for checking research papers, as the doled out commentator may have lacking learning in the exploration disciplines. For different subjective views, causing possible misinterpretations. An urgent need for an effective and feasible approach to check the submitted research papers with support of automated software. A method like text mining method come to solve the problem of automatically checking the research papers semantically. The proposed method to finding the proper similarity of text from the collection of documents by using Latent Dirichlet Allocation (LDA) algorithm and Latent Semantic Analysis (LSA) with synonym algorithm which is used to find synonyms of text index wise by using the English wordnet dictionary, another algorithm is LSA without synonym used to find the similarity of text based on index. LSA with synonym rate of accuracy is greater when the synonym are consider for matching.

LATENT-SEMANTIC ANALYSIS, SOCIAL NETWORKS AND NON-STRUCTURED DATA: INTERACTION METHOD

2019 ◽  
Keyword(s):  
Social Networks ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Statistical Processing ◽  
Unstructured Data ◽  
Average Score ◽  
Text Type ◽  
Internet Users ◽  
The Matrix ◽  
Human Thinking

This article examines the method of latent-semantic analysis, its advantages, disadvantages, and the possibility of further transformation for use in arrays of unstructured data, which make up most of the information that Internet users deal with. To extract context-dependent word meanings through the statistical processing of large sets of textual data, an LSA method is used, based on operations with numeric matrices of the word-text type, the rows of which correspond to words, and the columns of text units to texts. The integration of words into themes and the representation of text units in the theme space is accomplished by applying one of the matrix expansions to the matrix data: singular decomposition or factorization of nonnegative matrices. The results of LSA studies have shown that the content of the similarity of words and text is obtained in such a way that the results obtained closely coincide with human thinking. Based on the methods described above, the author has developed and proposed a new way of finding semantic links between unstructured data, namely, information on social networks. The method is based on latent-semantic and frequency analyzes and involves processing the search result received, splitting each remaining text (post) into separate words, each of which takes the round in n words right and left, counting the number of occurrences of each term, working with a pre-created semantic resource (dictionary, ontology, RDF schema, ...). The developed method and algorithm have been tested on six well-known social networks, the interaction of which occurs through the ARI of the respective social networks. The average score for author's results exceeded that of their own social network search. The results obtained in the course of this dissertation can be used in the development of recommendation, search and other systems related to the search, rubrication and filtering of information.

Web clustering based on hybrid probabilistic latent semantic analysis model

2013 ◽  
Vol 32 (11) ◽  
pp. 3018-3022 ◽  
Author(s):  
Zhi-he WANG ◽  
Ling-yun WANG ◽  
Hui DANG ◽  
Li-na PAN
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Probabilistic Latent Semantic Analysis ◽  
Analysis Model ◽  
Web Clustering
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