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Journal articles on the topic 'Emotional filtering'

Author: Grafiati

Published: 11 March 2023

Last updated: 31 July 2025

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1

Ismatun Nisak, Isnatul Mukarromah, Luthfiyah Mesi Aditama, and Muhammad Nofan Zulfahmi. "Pentingnya Filterisasi Konten Dewasa pada Perkembangan Sosial Emosional Anak Sekolah Dasar." Jurnal Bintang Pendidikan Indonesia 3, no. 1 (2024): 199–209. https://doi.org/10.55606/jubpi.v3i1.3580.

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This study aims to provide information and insight into the importance of filtering adult content in the social-emotional development of elementary school children. In today's digital era, technological advances make it easy to access information through various devices such as gadgets. This shows both positive and negative impacts on education, especially if there is no filtering, so children are free to search for information such as adult content that can affect their social emotional development. This study uses a qualitative approach to explain the importance of adult content filtering in

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RULE, R. R., A. P. SHIMAMURA, and R. T. KNIGHT. "Orbitofrontal cortex and dynamic filtering of emotional stimuli." Cognitive, Affective, & Behavioral Neuroscience 2, no. 3 (2002): 264–70. http://dx.doi.org/10.3758/cabn.2.3.264.

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Prete, Giulia, Bruno Laeng, and Luca Tommasi. "Modulating adaptation to emotional faces by spatial frequency filtering." Psychological Research 82, no. 2 (2016): 310–23. http://dx.doi.org/10.1007/s00426-016-0830-x.

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Jenkins, Jeffrey. "Detecting emotional ambiguity in text." MOJ Applied Bionics and Biomechanics 4, no. 3 (2020): 55–57. http://dx.doi.org/10.15406/mojabb.2020.04.00134.

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An approach for determining emotional ambiguity in text data is described in this paper. The prediction confidences output from a text classifier are used to measure amount of ambiguity found in target entries. This measure can be used as a filtering mechanism to identify entries that require human feedback. This feedback loop can be implemented in a workflow which retrains a classifier model including newly disambiguated entries and resulting in a boost to classifier accuracy. This emotion ambiguity measure can be utilized to discover concrete emotional content in text data as well as reveal

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Kadiri, Sudarsana Reddy, and B. Yegnanarayana. "Epoch extraction from emotional speech using single frequency filtering approach." Speech Communication 86 (February 2017): 52–63. http://dx.doi.org/10.1016/j.specom.2016.11.005.

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Denis, Patrice, Vincent Courboulay, Arnaud Revel, Syntyche Gbèhounou, François Lecellier, and Christine Fernandez-Maloigne. "Improvement of natural image search engines results by emotional filtering." EAI Endorsed Transactions on Creative Technologies 3, no. 6 (2016): 151164. http://dx.doi.org/10.4108/eai.25-4-2016.151164.

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Sarwath Unnisa and Akshaya N S. "Personalized Mood-Centric Book Recommendation Integrating Machine Learning with Content Based Filtering." International Journal of Information Technology, Research and Applications 3, no. 3 (2024): 15–22. http://dx.doi.org/10.59461/ijitra.v3i3.100.

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This paper introduces a novel personalized book recommendation system aimed at enhancing subjective well-being (SWB). Despite the vast array of books available on the internet, people often struggle to find literature that aligns with their current emotional state. The system dynamically detects users' emotional states and recommends books tailored to their mood. It utilizes a content-based filtering algorithm to suggest top-rated books in real-time based on the user's current emotional state. For users with low mood, uplifting and inspirational books are recommended, while a mix of happy and

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Shinde, Vishakha Rajan, and Dr Arati Deshpande. "A Brief Review on Audiobook Recommendation System Based on Contextual and Emotional Cues." International Journal of Research and Innovation in Applied Science X, no. VI (2025): 1341–45. https://doi.org/10.51584/ijrias.2025.100600102.

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The exponential growth of audio book platforms has resulted in an overwhelming volume of content, necessitating intelligent recommendation systems that go beyond traditional filtering approaches. Conventional collaborative and content-based filtering methods often overlook critical aspects such as user emotions and real-time contextual factors, leading to suboptimal personalization. This survey presents a comprehensive review of recent developments in audio book recommendation systems that incorporate hybrid deep learning models, emotion recognition, and context- awareness. It evaluates variou

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Xu, Qinfu, Shaozu Yuan, Yiwei Wei, Jie Wu, Leiquan Wang, and Chunlei Wu. "Multiple Feature Refining Network for Visual Emotion Distribution Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 9 (2025): 8924–32. https://doi.org/10.1609/aaai.v39i9.32965.

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The significance of visual emotion distribution learning (VEDL) has surged, particularly with the growing inclination to convey emotions through images. The key of VEDL lies in capturing both low- and high-level features within the same visual content, thus promoting the model for salient and subtle emotion awareness. To learn the distribution of emotions involved in images, most previous works learn coarse semantic knowledge with unbiased filtering. Consequently, they focus on the entire scene and suffer from the redundancy of semantic-irrelevant information, which diminishes the affective co

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Kim, Tae-Yeun, Hoon Ko, Sung-Hwan Kim, and Ho-Da Kim. "Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering." Sensors 21, no. 6 (2021): 1997. http://dx.doi.org/10.3390/s21061997.

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Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised,

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Iryna, Dmytriieva, and Bimalov Dmytro. "Development of a software module for the identification of the emotional state of the user." System technologies 4, no. 147 (2023): 29–34. http://dx.doi.org/10.34185/1562-9945-4-147-2023-03.

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A huge number of spheres of human activity leads to the emergence of information re-sources that reflect social communication. The study of the identification of emotions in text communication is an actual direction of research in the field of natural language processing and machine learning. The main goal of the work is to develop a software module that implements algorithms and models that can automatically determine a person's emotional state based on text messages. This work is de-voted to the review of some models and an algorithm for improving data processing in the middle of text commun

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Nosshi, Anthony, Aziza Saad Asem, and Mohammed Badr Senousy. "Hybrid Recommender System Using Emotional Fingerprints Model." International Journal of Information Retrieval Research 9, no. 3 (2019): 48–70. http://dx.doi.org/10.4018/ijirr.2019070104.

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With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of th

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Mičieta, Branislav, Vladimíra Biňasová, Beáta Furmannová, Gabriela Gabajová, and Marta Kasajová. "Emotional intelligence as an aspect in the performance of the work of a global manager." SHS Web of Conferences 129 (2021): 12002. http://dx.doi.org/10.1051/shsconf/202112912002.

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Research background: Emotional intelligence is a set of emotional and social abilities and skills of a manager. Nowadays, the environment is global and very complex, and the association between emotional intelligence and performance in enterprises remains an important area of worry for managers and employees' globally. The article focuses on the aspect and abilities of managers dealing with increasing the performance of their subordinates, their relationships in the workplace, division of labour and the overall organization of the team regarding their emotions and individual feeling of importa

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Guerzoni, Michael A. "Vicarious trauma and emotional labour in researching child sexual abuse and child protection: A postdoctoral reflection." Methodological Innovations 13, no. 2 (2020): 205979912092634. http://dx.doi.org/10.1177/2059799120926342.

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Criminology almost inevitably involves the study of sensitive and sorrowful research topics. Consequently, criminologists fall victim to the inherent risks of exposure to vicarious trauma, requiring many to practice emotional labour in the field, in the lecture hall, and perhaps, even along the corridors of the university campus itself. This article offers a reflective account of the experiences of vicarious trauma and the self-imposed, protective practice of emotional labour within doctoral research on child protection initiatives within a religious institution. It explores my experience of s

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Belinskiy, Artem Viktorovich, Vazha Mikhailovich Devishvili, Aleksandr Mikhailovich Chernorizov та Mikhail Aleksandrovich Lobin. "Method of Еmotional State Assessment Using a Complex of Psychophysiological and Tensotremorometric Methods". Психология и Психотехника, № 1 (січень 2023): 26–37. http://dx.doi.org/10.7256/2454-0722.2023.1.39849.

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The object of research is emotional tension. The subject of the study is assessment of emotional tension in the process of presentation of emotionally significant stimuli in the form of images and sounds according to the parameters of physiological activity and tensotremorometry in the process of maintaining isometric effort. Particular attention is paid to the consideration of methods of determination of tremor and its connection with emotional tension. Key aspects are the consideration of the key frequency range of physiological tremor analysis 8-16 hertz, methods of tremor measurement such

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Wahyuni, Hari, Eni Erwantiningsih, and Agnes Ratna Pudyaningsih. "Analysis Financial Management Behavior through Financial Literacy: Gen Z's Preferences Fintech, FoMO, Love of Money." Jurnal Manajemen dan Kewirausahaan 13, no. 1 (2025): 102–14. https://doi.org/10.26905/jmdk.v13i1.15526.

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In the digital era, Gen Z's financial behavior is shaped by technology and emotions. Financial literacy plays a key role in filtering emotional influences and guiding Gen Z toward smarter financial decisions. This study explores how Gen Z in Pasuruan City uses financial technology (abbreviated as fintech), and how their fear of missing out (FoMO) and attitudes toward money influence their financial habits—particularly through the lens of financial literacy. Using purposive sampling, the research finds that fintech offers real benefits for Gen Z, especially in terms of convenience, security, an

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Kang, Xinxin, and Yong Nie. "Design and analysis of teaching early warning system based on multimodal data in an intelligent learning environment." PeerJ Computer Science 11 (March 4, 2025): e2692. https://doi.org/10.7717/peerj-cs.2692.

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In online teaching environments, the lack of direct emotional interaction between teachers and students poses challenges for teachers to consciously and effectively manage their emotional expressions. The design and implementation of an early warning system for teaching provide a novel approach to intelligent evaluation and improvement of online education. This study focuses on segmenting different emotional segments and recognizing emotions in instructional videos. An efficient long-video emotional transition point search algorithm is proposed for segmenting video emotional segments. Leveragi

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Guitart, Miguel. "Limit Geometries of Architectural Filters: Precise Rationality and Poetic Emotion." ZARCH, no. 15 (January 27, 2021): 222–33. http://dx.doi.org/10.26754/ojs_zarch/zarch.2020154648.

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An architectural filter is a porous material construction that regulates transverse visual relationships, and establishes degrees of connection through the intervention of light and gaze. Filtering boundaries display variable proportions of mass and air, which are instrumental to the production of the spatial experience behind the mediation of matter and geometry. A filter's structural system synthesizes geometric relations with the capacity to cause architectural atmospheres, as a result of the active border that is technically precise and sensorially ambiguous at the same time. The text sust

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Georgiewa, Petra, Agnieszka J. Szczepek, Matthias Rose, Burghard F. Klapp, and Birgit Mazurek. "Cerebral Processing of Emotionally Loaded Acoustic Signals by Tinnitus Patients." Audiology and Neurotology 21, no. 2 (2016): 80–87. http://dx.doi.org/10.1159/000443364.

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This exploratory study determined the activation pattern in nonauditory brain areas in response to acoustic, emotionally positive, negative or neutral stimuli presented to tinnitus patients and control subjects. Ten patients with chronic tinnitus and without measurable hearing loss and 13 matched control subjects were included in the study and subjected to fMRI with a 1.5-tesla scanner. During the scanning procedure, acoustic stimuli of different emotional value were presented to the subjects. Statistical analyses were performed using statistical parametric mapping (SPM 99). The activation pat

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Micucci, Antonia, Vera Ferrari, Andrea De Cesarei, and Maurizio Codispoti. "Contextual Modulation of Emotional Distraction: Attentional Capture and Motivational Significance." Journal of Cognitive Neuroscience 32, no. 4 (2020): 621–33. http://dx.doi.org/10.1162/jocn_a_01505.

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Emotional stimuli engage corticolimbic circuits and capture attention even when they are task-irrelevant distractors. Whether top–down or contextual factors can modulate the filtering of emotional distractors is a matter of debate. Recent studies have indicated that behavioral interference by emotional distractors habituates rapidly when the same stimuli are repeated across trials. However, little is known as to whether we can attenuate the impact of novel (never repeated) emotional distractors when they occur frequently. In two experiments, we investigated the effects of distractor frequency

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Druhak, Adrian, Andrii Kovalenko, and Olena Chaikovska. "Book Recommendation with Consideration of Emotions Extracted from Reader Text Reviews." Digital Platform: Information Technologies in Sociocultural Sphere 8, no. 1 (2025): 41–51. https://doi.org/10.31866/2617-796x.8.1.2025.335530.

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The purpose of the article is to study methods for obtaining emotional analysis data from readers’ text reviews for use in the recommendation functions of e-commerce systems that provide online book reading or audiobook listening services. The research methodology consists of systems approach, systems analysis and synthesis methods, and structural modelling of relational databases. The scientific novelty lies in developing a modified method for determining user-based collaborative filtering recommendations, which considers data from the emotional analysis of text reviews of books read. Conclus

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Kuliahin, Аndrii. "PERSONALIZATION OF VISUAL CONTENT OF INTERACTIVE ART IN AUGMENTED REALITY BASED ON INDIVIDUAL USER PREFERENCES." Системи управління, навігації та зв’язку. Збірник наукових праць 1, no. 75 (2024): 115–17. http://dx.doi.org/10.26906/sunz.2024.1.115.

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Topicality. In connection with the development of AR technologies and their use in interactive art, there is a growing need to develop methods of personalizing visual content, focused on the individual preferences of users. Research methods. Neural collaborative filtering method, generalized matrix factorization method, mood analysis on video. The purpose of the article: Researching the possibilities of improving the personalization of visual content in interactive art by evaluating the emotional reactions of users and their implicit feedback. The results obtained. The application of neural co

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Tirajaya, Juana Rosa Gabriel, Rosalía Zarate Barrial, Zoila Esther Cherres López, et al. "Emotional Intelligence in Improving Academic Performance in College Students: A Systematic Review." International Journal of Religion 5, no. 5 (2024): 183–90. http://dx.doi.org/10.61707/a8yc4c46.

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The present investigation was carried out with the purpose of publicizing how emotional intelligence contributes to academic performance; posing as a research question: What is the impact of emotional intelligence in improving academic performance in university students in the last five years?; Due to this, the methodology used for this purpose was qualitative, basic and a review of articles published in the SCOPUS and Web of Science databases; following a search protocol, collection, extraction, analysis and selection of articles under the eligibility criteria: academic publications with SJR

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RYBAKOVA, E. I., and I. V. SHARUN. "DEVELOPMENT OF TOOLS FOR MODERATING COMMENTS BASEDON SOLIDITY ANALYSIS." Applied Mathematics and Fundamental Informatics 10, no. 2 (2024): 39–44. https://doi.org/10.25206/2311-4908-2023-10-2-39-44.

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The problem of comment moderation with negative content filtering is considered. The PySpark framework is used to analyze the sentiment of comments based on the Apache Spark library. The algorithm reads the text of the comment and determines its emotional coloring. The toolkit can be used on educational web services where negative comments can be filtered and prevented from appearing on the site, which in turn improves content quality and user safety.

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Li, Tingting, Yingli Wu, Yuqing Liu, and Jingqi Li. "Deep Learning-Based Analysis of E-Commerce Enterprises." Journal of Organizational and End User Computing 37, no. 1 (2025): 1–36. https://doi.org/10.4018/joeuc.379722.

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The exponential growth of e-commerce platforms has generated vast amounts of user behavior data, making it increasingly important to predict consumer preferences and spending patterns. Traditional recommendation systems often struggle with challenges such as data sparsity, the cold-start problem, and the inability to capture the dynamic nature of user behavior. These limitations hinder the accurate prediction of consumer actions, especially in evolving markets where user preferences change over time. To address these challenges, the authors propose deep behavioral and sentiment-aware personali

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Byun, Jeong, and Dong Keun Kim. "Design and Implementation of Location Recommending Services using Personal Emotional Information based on Collaborative Filtering." Journal of the Korea Institute of Information and Communication Engineering 20, no. 8 (2016): 1407–14. http://dx.doi.org/10.6109/jkiice.2016.20.8.1407.

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Ye, Chaoxiong, Qianru Xu, Qiang Liu, et al. "The impact of visual working memory capacity on the filtering efficiency of emotional face distractors." Biological Psychology 138 (October 2018): 63–72. http://dx.doi.org/10.1016/j.biopsycho.2018.08.009.

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Patel, Jigna, Ali Asgar Padaria, Aryan Mehta, Aaryan Chokshi, Jitali Dineshkumar Patel, and Rupal Kapdi. "ConCollA - A Smart Emotion-based Music Recommendation System for Drivers." Scalable Computing: Practice and Experience 24, no. 4 (2023): 919–39. http://dx.doi.org/10.12694/scpe.v24i4.2467.

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Music recommender system is an area of information retrieval system that suggests customized music recommendations to users based on their previous preferences and experiences with music. While existing systems often overlook the emotional state of the driver, we propose a hybrid music recommendation system - ConCollA to provide a personalized experience based on user emotions. By incorporating facial expression recognition, ConCollA accurately identifies the driver’s emotions using convolution neural network(CNN) model and suggests music tailored to their emotional state. ConCollA combines co

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Huria, Sampada. "Tune into Emotions: A Study of Musical Therapy’s Influence on Facial Expression Recognition." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5598–603. http://dx.doi.org/10.22214/ijraset.2024.62870.

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Abstract: This research paper introduces an innovative approach to music recommendation systems, utilizing facial expression analysis for the delivery of personalized music suggestions. Through the utilization of machine learning algorithms, the system assesses the user's emotional state by analyzing facial expressions, subsequently offering music recommendations aligned with their mood. The proposed methodology incorporates a deep learning-based model for the detection and classification of facial expressions, complemented by a collaborative filtering algorithm to generatepersonalized music r

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Subrhamanyam, Kolusu, Aduri DharaniSri, Devarakonda HemaSri, Anumanedi Yamini, Chakarajamula Denith Siva Sai, and Bandela Jaswanth. "A HYBRID APPROACH FOR EMOTION-DRIVEN GAME RECOMMENDATIONS USING TEXT, VOICE AND IMAGE RECOGNITION." Industrial Engineering Journal 54, no. 02 (2025): 81–89. https://doi.org/10.36893/iej.2025.v52i2.009.

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This implementation presents a game recommendation system that utilizes natural language processing (NLP) techniques to provide personalized game suggestions based on user preferences. The system processes a dataset of games containing descriptions and emotional tones to determine relevant recommendations. It employs TF-IDF (Term Frequency-Inverse Document Frequency) vectorization to transform textual data into numerical representations, enabling meaningful comparisons between game content and user input. The cosine similarity metric is then used to assess the closeness of games to the given p

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Asprer, Jessica, Eleanor Marie Escalante, and Jeremaiah Opiniano. "What causes reticence in publicly correcting false information online? A case study from the Philippines." Romanian Journal of Communication and Public Relations 26, no. 2 (2025): 57–75. https://doi.org/10.21018/rjcpr.2024.2.595.

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News audiences on social media succumb to filtering systems to navigate the overabundance of information. However, filtering systems get bolstered by echo chambers, increasing social media polarization, especially when false information hinders better-informed viewpoints. Reticence, though understudied, has the ability to hamper the spread of factual information. Hence, this study aims to investigate why social media users showcase reticence toward publicly correcting false information on their feeds, and how this disposition can affect ideological polarization. Eight interviews were conducted

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Petridis, Sergios, Theodoros Giannakopoulos, and Constantine D. Spyropoulos. "A Low Cost Pupillometry Approach." International Journal of E-Health and Medical Communications 6, no. 4 (2015): 49–61. http://dx.doi.org/10.4018/ijehmc.2015100104.

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The need for low-cost health monitoring is increasing with the continuous increase of the elderly population. In this context, unobtrusive audiovisual monitoring methods can be of great importance. More particularly, the diameter of the pupil is a valuable source of information, since, apart from pathological cases, it can reveal the emotional state, the fatigue and the ageing. To allow for unobtrusive monitoring to gain acceptance, one should seek for efficient methods of monitoring using common low-cost hardware. This paper describes a method for monitoring pupil sizes using a common, low-co

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Shanmuga Sundari, P., and M. Subaji. "Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment." International Journal of Information Technology & Decision Making 19, no. 02 (2020): 385–412. http://dx.doi.org/10.1142/s0219622020500108.

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Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to r

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More, Avadhut P., Swanand P. Gholap, Aniket A. Gayke, Uddhav D. Hon, and Sharad M. Rokade. "Music Recommendation System Using Facial Emotion Gestures." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 419–21. http://dx.doi.org/10.22214/ijraset.2024.65090.

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Abstract: Music recommendation systems are transformingthe way users interact with music streaming servicesby personalizing experiences. This paper presents a novel approach that leverages facial emotion recognition (FER) to suggest music dynamically based on users' emotional states. Using machinelearning and deep learning models, facial expressions are analyzed in real-time through video input. The system integrates emotion-based recommendations with collaborative and content- based filtering techniques to enhance accuracy. Thisapproach offers a more intuitive and human-centered interaction c

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Siveri, Cila. "KEY CHARACTERISTICS OF COMMUNICATIONS BARRIERS IN ORGANIZATIONAL CONTEX." International Journal of Management Trends: Key Concepts and Research 4, no. 1 (2025): 79–91. https://doi.org/10.58898/ijmt.v4i1.79-91.

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This paper explores the primary communication barriers within organizational settings and the implications they have on effective interpersonal and intergroup communication. Through a theoretical overview and empirical research conducted in Zrenjanin, Serbia, this study identifies specific types of barriers—ambiguity, noise, emotional factors, distrust, data filtering, language and jargon, and cultural elements—and analyzes their frequency and perception among employees. Results highlight the relatively low presence of cultural and trust-based barriers, while ambiguity and noise are more preva

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Charquero-Ballester, Marina, Jessica G. Walter, Ida A. Nissen, and Anja Bechmann. "Different types of COVID-19 misinformation have different emotional valence on Twitter." Big Data & Society 8, no. 2 (2021): 205395172110412. http://dx.doi.org/10.1177/20539517211041279.

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The spreading of COVID-19 misinformation on social media could have severe consequences on people's behavior. In this paper, we investigated the emotional expression of misinformation related to the COVID-19 crisis on Twitter and whether emotional valence differed depending on the type of misinformation. We collected 17,463,220 English tweets with 76 COVID-19-related hashtags for March 2020. Using Google Fact Check Explorer API we identified 226 unique COVID-19 false stories for March 2020. These were clustered into six types of misinformation (cures, virus, vaccine, politics, conspiracy theor

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Cheng, Xinquan, and Wenlong Su. "Recommendation Model of Tourist Attractions Based on Deep Learning." Mathematical Problems in Engineering 2022 (August 28, 2022): 1–7. http://dx.doi.org/10.1155/2022/9080818.

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In order to solve the problem of tourism information overload caused by the rapid development of tourism and the Internet era, the author proposes a tourist attraction recommendation model based on deep learning. Convolutional Neural Network (CNN) is used to extract the sentiment of text comments, the Pearson similarity formula is used to calculate similar user groups, and the mean absolute error (MAE) is used to evaluate the resulting error. Compare with traditional collaborative filtering methods. Experimental results show that: the MAE value is smaller than the MAE value of the collaborativ

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Ahamad, Shahbaz, Noorishta Hashmi, and Ehtesham Hussain. "SENTIMENT-GUIDED MULTIMODAL CONTENT RECOMMENDATION USING GRU AND CNN-LSTM ARCHITECTURES." Journal of Dynamics and Control 9, no. 5 (2025): 46–56. https://doi.org/10.71058/jodac.v9i5005.

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In the era of social commerce, recommendation systems must transcend traditional paradigms by incorporating multimodal data to personalize content more effectively. This study proposes a sentiment-guided, multimodal recommendation framework that integrates text and image-based features to enhance product recommendation strategies, particularly in influencer marketing and fashion retail. The proposed architecture utilizes Gated Recurrent Units (GRUs) for analyzing user-generated textual comments to capture emotional tone and sentiment directionality. Concurrently, a hybrid Convolutional Neural

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Ahamad, Shahbaz, Noorishta Hashmi, and Ehtesham Hussain. "SENTIMENT-GUIDED MULTIMODAL CONTENT RECOMMENDATION USING GRU AND CNN-LSTM ARCHITECTURES." Journal of Dynamics and Control 9, no. 5 (2025): 182–92. https://doi.org/10.71058/jodac.v9i5015.

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In the era of social commerce, recommendation systems must transcend traditional paradigms by incorporating multimodal data to personalize content more effectively. This study proposes a sentiment-guided, multimodal recommendation framework that integrates text and image-based features to enhance product recommendation strategies, particularly in influencer marketing and fashion retail. The proposed architecture utilizes Gated Recurrent Units (GRUs) for analyzing user-generated textual comments to capture emotional tone and sentiment directionality. Concurrently, a hybrid Convolutional Neural

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Li, Shun, Liqing Cui, Changye Zhu, Baobin Li, Nan Zhao, and Tingshao Zhu. "Emotion recognition using Kinect motion capture data of human gaits." PeerJ 4 (September 15, 2016): e2364. http://dx.doi.org/10.7717/peerj.2364.

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Automatic emotion recognition is of great value in many applications, however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Human gaits could reflect the walker’s emotional state, and could be an information source for emotion recognition. This paper proposed a novel method to recognize emotional state through human gaits by using Microsoft Kinect, a low-cost, portable, camera-based sensor. Fifty-nine participants’ gaits under neutral state, induced anger and induced happiness were recorded by two Ki

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Jayatissa, Dimani. "Prioritizing Mental Well-being in Emerging Educational Models: Strategies for Integrating Social and Emotional Learning (SEL) to Support Student Mental Health." International Journal of Studies in Education and Science 5, no. 3 (2024): 293–303. http://dx.doi.org/10.46328/ijses.101.

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This paper conducts a comprehensive review of the effectiveness of integrating Social and Emotional Learning (SEL) practices into emerging educational models to support student mental well-being, with a particular focus on Canadian contexts. Employing a systematic literature review methodology, the study utilized Google Scholar as the primary database, filtering results based on inclusion and exclusion criteria, emphasizing relevance, quality, and recency. The main findings underscore a research gap between acknowledgment of the importance of SEL, and its implementation in Canadian schools. St

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Anil, Kumar, and Chawla Sonal. "HSBRS: Hybrid Sentiment-based Collaborative Architecture for Book Recommendation System." Indian Journal of Science and Technology 17, no. 11 (2024): 1003–15. https://doi.org/10.17485/IJST/v17i11.115.

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Abstract <strong>Objectives:</strong> This study presents an efficient approach "Hybrid Sentiment-based Collaborative Architecture" to enhance book recommendation systems. This novel approach integrates sentiment analysis methodologies that encompass Lexicon-based and Deep Learning-based techniques, in conjunction with Collaborative Filtering to offer a more personalized recommendation experience. <strong>Methods:</strong> This study outlines the methodology for comparing and analyzing various Collaborative Filtering and sentiment analysis techniques to identify an optimal combi

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Murugappan, Murugappan, Waleed Alshuaib, Ali K. Bourisly, Smith K. Khare, Sai Sruthi, and Varun Bajaj. "Tunable Q wavelet transform based emotion classification in Parkinson’s disease using Electroencephalography." PLOS ONE 15, no. 11 (2020): e0242014. http://dx.doi.org/10.1371/journal.pone.0242014.

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Parkinson’s disease (PD) is a severe incurable neurological disorder. It is mostly characterized by non-motor symptoms like fatigue, dementia, anxiety, speech and communication problems, depression, and so on. Electroencephalography (EEG) play a key role in the detection of the true emotional state of a person. Various studies have been proposed for the detection of emotional impairment in PD using filtering, Fourier transforms, wavelet transforms, and non-linear methods. However, these methods require a selection of basis and are confined in terms of accuracy. In this paper, tunable Q wavelet

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Hupont, Isabelle, Eva Cerezo, Sergio Ballano, and Sandra Baldassarri. "On the origin of the methodology for the scalable fusion of affective channels in a continuous emotional space and the “emotional kinematics” filtering technique - A correction." Information Fusion 67 (March 2021): 1–2. http://dx.doi.org/10.1016/j.inffus.2020.09.009.

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LIU, GAI. "An Analysis of Affective Factors of Deaf college Students' English Learning Based on Affective Filtering Hypothesis." Pacific International Journal 5, no. 3 (2022): 150–54. http://dx.doi.org/10.55014/pij.v5i3.216.

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The College English course for deaf students is a compulsory course for students during the university period. The influence of learning motivation, learning anxiety and self-confidence on deaf students' English learning should also be considered. By balancing various emotional factors, students can obtain better learning results. The influence of affective factors on learning is undeniable. Therefore, in view of these major affective factors, combined with the learning reality of deaf students and the observation of front-line teachers and front-line classrooms in the investigation, the autho

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Osório, Neila Barbosa, Deuzivania Carlos de Oliveira, Leda Santana de Noleto, and Luiz Sinésio da Silva Neto. "UMANIZING IN TIME OF COVID-19: Quality information." Revista Observatório 6, no. 3 (2020): a1en. http://dx.doi.org/10.20873/uft.2447-4266.2020v6n3a1en.

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This article aims to relate students belonging to the University of Maturity in times of social isolation due to a pandemic of COVID-19, as well as actions carried out by the students of the UMAnizando project, guiding them with quality information, such as activities carried out are being important in the sense of filtering information that is really useful, for the purpose of appropriate prevention at the moment, the project made it possible to guide with safety and protection, so that afflictions can be minimized in a coherent way thinking about the emotional and physical well-being of the

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Fu, Shang, Shi Jiatu, Shi Yadong, and Zhou Shuwen. "Enhancing E-Commerce Recommendation Systems with Deep Learning-based Sentiment Analysis of User Reviews." International Journal of Engineering and Management Research 14, no. 4 (2024): 19–34. https://doi.org/10.5281/zenodo.13221409.

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This study introduces a novel approach to enhancing e-commerce recommendation systems by integrating deep learning-based sentiment analysis of user reviews. We propose a sentiment-aware neural collaborative filtering model that leverages the emotional content of reviews to enrich user and item representations. Our method employs a hierarchical attention network for fine-grained sentiment analysis, capturing nuanced user opinions at both word and sentence levels. The sentiment information is then incorporated into a neural collaborative filtering framework, allowing for more personalized and co

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Palanichamy, Naveen, Su Cheng Haw, Revathi K, Kok Why Ng, and Suthent Tamilselvam. "Emotion Recognition with Multi Physiological Signals: A Deep Learning Approach." Journal of Advanced Research in Applied Sciences and Engineering Technology 63, no. 1 (2025): 188–205. https://doi.org/10.37934/araset.63.1.188205.

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Human emotions, a complex interplay of psychological and physiological signals are a critical aspect of human interaction and well-being. Emotion recognition models in general capture human behaviour via facial features, voice/speech, and physiological signals, and evaluate and predict emotional states. The physiological signals, like brainwave, heart rate, eye movement, or galvanic skin response are the major cause of emotional changes. The combination of these signals contributes to the emotion change. Thus, effective models with combined physiological signals will serve better solutions com

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Ren, Ye. "A Multidimensional Analysis of Foreign Language Anxiety among Students: Implications for Academic." Lecture Notes in Education Psychology and Public Media 75, no. 1 (2024): 133–38. https://doi.org/10.54254/2753-7048/2024.17929.

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Inside the realm of second language acquisition, Chinese university students encounter significant subjective challenges, particularly Foreign Language Anxiety (FLA), which is widely recognized for its broad impact. As globalization and foreign language instruction intensify, FLA has become a key research area in education and psychology. A wealth of literature has delved into FLAs influence on learners psychological well-being, cognition, and behavior. This study systematically reviews the literature to explore the role and effects of FLA in Chinese students second language learning. It revea

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Cao, Zheng, Heng Xu, and Brian Sheng-Xian Teo. "Sentiment of Chinese Tourists towards Malaysia Cultural Heritage Based on Online Travel Reviews." Sustainability 15, no. 4 (2023): 3478. http://dx.doi.org/10.3390/su15043478.

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Analyzing the perception differences and influencing factors of cross-cultural groups in heritage tourism can help heritage sites to formulate differentiated service and improve tourist satisfaction. This research adopted the BERT model to undertake sentiment analysis of 17,555 Chinese online reviews for nine scenic spots in Melaka. Using vocabulary filtering, co-occurrence analysis, and semantic clustering technology, the emotional characteristics of Chinese outbound tourists when they visited heritage sites in Melaka were analyzed, which revealed the factors influencing their positive and ne

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