Exploiting metadata for ontology-based visual exploration of weakly structured text documents

2004 ◽  
Author(s):  
C. Seeling ◽  
A. Becks
Keyword(s):  
Visual Exploration ◽  
Text Documents

The Gender Typicality of Faces and Its Impact on Visual Processing and on Hiring Decisions

2013 ◽  
Vol 60 (6) ◽  
pp. 444-452 ◽  
Author(s):  
Lisa von Stockhausen ◽  
Sara Koeser ◽  
Sabine Sczesny
Keyword(s):  
Visual Processing ◽  
Past Research ◽  
Visual Exploration ◽  
Facial Appearance ◽  
Leadership Roles ◽  
Hiring Decisions ◽  
Leadership Selection ◽  
Gender Typicality ◽  
Second Stage ◽  
The Feminine

Past research has shown that the gender typicality of applicants’ faces affects leadership selection irrespective of a candidate’s gender: A masculine facial appearance is congruent with masculine-typed leadership roles, thus masculine-looking applicants are hired more certainly than feminine-looking ones. In the present study, we extended this line of research by investigating hiring decisions for both masculine- and feminine-typed professional roles. Furthermore, we used eye tracking to examine the visual exploration of applicants’ portraits. Our results indicate that masculine-looking applicants were favored for the masculine-typed role (leader) and feminine-looking applicants for the feminine-typed role (team member). Eye movement patterns showed that information about gender category and facial appearance was integrated during first fixations of the portraits. Hiring decisions, however, were not based on this initial analysis, but occurred at a second stage, when the portrait was viewed in the context of considering the applicant for a specific job.

Visual exploration of dynamic real-world scenes in patients with hemispatial neglect

2011 ◽  
Vol 42 (01) ◽  
Author(s):  
J. von der Gablentz ◽  
A. Sprenger ◽  
M. Dorr ◽  
E. Barth ◽  
W. Heide ◽  
...  
Keyword(s):  
Real World ◽  
Visual Exploration ◽  
Hemispatial Neglect

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

2020 ◽  
Vol 16 (4) ◽  
pp. 296-306 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Essam Said Hanandeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Abdallh Otair ◽  
Shishir Kumar Shandilya
Keyword(s):  
Optimization Technique ◽  
Document Clustering ◽  
Text Clustering ◽  
Hill Climbing ◽  
Text Documents ◽  
Clustering Problem ◽  
Text Document ◽  
Text Information ◽  
Amount Of Knowledge ◽  
The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

Link-based multi-verse optimizer for text documents clustering

2020 ◽  
Vol 87 ◽  
pp. 106002 ◽  
Author(s):  
Ammar Kamal Abasi ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Syibrah Naim ◽  
Sharif Naser Makhadmeh ◽  
...  
Keyword(s):  
Text Documents

scholarly journals DESIGNING A DATA VISUALISATION AND ANALYSIS TOOL FOR SUPPORTING DECISION-MAKING WITH PUBLIC TRANSPORTATION NETWORK

2021 ◽  
Vol 1 ◽  
pp. 1093-1102
Author(s):  
Flore Vallet ◽  
Mostepha Khouadjia ◽  
Ahmed Amrani ◽  
Juliette Pouzet
Keyword(s):  
Decision Making ◽  
Public Transportation ◽  
Transportation Network ◽  
Visual Exploration ◽  
Problem Solution ◽  
Analysis Tool ◽  
Complex Data ◽  
Data Visualisation ◽  
Ex Post ◽  
Passenger Flows

AbstractMassive data are surrounding us in our daily lives. Urban mobility generates a very high number of complex data reflecting the mobility of people, vehicles and objects. Transport operators are primary users who strive to discover the meaning of phenomena behind traffic data, aiming at regulation and transport planning. This paper tackles the question "How to design a supportive tool for visual exploration of digital mobility data to help a transport analyst in decision making?” The objective is to support an analyst to conduct an ex post analysis of train circulation and passenger flows, notably in disrupted situations. We propose a problem-solution process combined with data visualisation. It relies on the observation of operational agents, creativity sessions and the development of user scenarios. The process is illustrated for a case study on one of the commuter line of the Paris metropolitan area. Results encompass three different layers and multiple interlinked views to explore spatial patterns, spatio-temporal clusters and passenger flows. We join several transport network indicators whether are measured, forecasted, or estimated. A user scenario is developed to investigate disrupted situations in public transport.

scholarly journals Tracking eye-gaze in smart home systems (SHS): first insights from eye-tracking and self-report measures

2021 ◽  
Author(s):  
Federico Cassioli ◽  
Laura Angioletti ◽  
Michela Balconi
Keyword(s):  
Eye Tracking ◽  
Personality Traits ◽  
Smart Home ◽  
Near Infrared ◽  
Tracking System ◽  
Eye Gaze ◽  
Self Report ◽  
Visual Exploration ◽  
Living Room ◽  
Psychological Traits

AbstractHuman–computer interaction (HCI) is particularly interesting because full-immersive technology may be approached differently by users, depending on the complexity of the interaction, users’ personality traits, and their motivational systems inclination. Therefore, this study investigated the relationship between psychological factors and attention towards specific tech-interactions in a smart home system (SHS). The relation between personal psychological traits and eye-tracking metrics is investigated through self-report measures [locus of control (LoC), user experience (UX), behavioral inhibition system (BIS) and behavioral activation system (BAS)] and a wearable and wireless near-infrared illumination based eye-tracking system applied to an Italian sample (n = 19). Participants were asked to activate and interact with five different tech-interaction areas with different levels of complexity (entrance, kitchen, living room, bathroom, and bedroom) in a smart home system (SHS), while their eye-gaze behavior was recorded. Data showed significant differences between a simpler interaction (entrance) and a more complex one (living room), in terms of number of fixation. Moreover, slower time to first fixation in a multifaceted interaction (bathroom), compared to simpler ones (kitchen and living room) was found. Additionally, in two interaction conditions (living room and bathroom), negative correlations were found between external LoC and fixation count, and between BAS reward responsiveness scores and fixation duration. Findings led to the identification of a two-way process, where both the complexity of the tech-interaction and subjects’ personality traits are important impacting factors on the user’s visual exploration behavior. This research contributes to understand the user responsiveness adding first insights that may help to create more human-centered technology.

Semantic congruency effects of prime words on tool visual exploration

2021 ◽  
Vol 152 ◽  
pp. 105758
Author(s):  
Giovanni Federico ◽  
François Osiurak ◽  
Emanuelle Reynaud ◽  
Maria A. Brandimonte
Keyword(s):  
Visual Exploration ◽  
Semantic Congruency

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

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