Journal articles: 'Mood detection'

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Liu, Yu, Kyoung-Don Kang, and Mi Jin Doe. "HADD: High-Accuracy Detection of Depressed Mood." Technologies 10, no. 6 (November 29, 2022): 123. http://dx.doi.org/10.3390/technologies10060123.

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Depression is a serious mood disorder that is under-recognized and under-treated. Recent advances in mobile/wearable technology and ML (machine learning) have provided opportunities to detect the depressed moods of participants in their daily lives with their consent. To support high-accuracy, ubiquitous detection of depressed mood, we propose HADD, which provides new capabilities. First, HADD supports multimodal data analysis in order to enhance the accuracy of ubiquitous depressed mood detection by analyzing not only objective sensor data, but also subjective EMA (ecological momentary assessment) data collected by using mobile devices. In addition, HADD improves upon the accuracy of state-of-the-art ML algorithms for depressed mood detection via effective feature selection, data augmentation, and two-stage outlier detection. In our evaluation, HADD significantly enhanced the accuracy of a comprehensive set of ML models for depressed mood detection.

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Khanolkar, Neil, Ajinkya Sathe, Ketaki Shinde, and Aarti M. Karande. "Mood Detection using Sentiment Analysis." International Journal of Computer Applications 184, no. 26 (August 20, 2022): 16–20. http://dx.doi.org/10.5120/ijca2022922316.

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Howard, Newton, and Mathieu Guidere. "LXIO: The Mood Detection Robopsych." Brain Sciences Journal 1, no. 1 (March 1, 2012): 98–109. http://dx.doi.org/10.7214/brainsciences/2012.01.01.05.

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Gavde, Megna. "Comparative Study on Mood Detection Techniques." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 1456–57. http://dx.doi.org/10.22214/ijraset.2018.4245.

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Pansare, Ashwini, and Monali Shetty. "Mood Detection based on Facial Expressions." International Journal of Engineering Trends and Technology 48, no. 4 (June 25, 2017): 200–204. http://dx.doi.org/10.14445/22315381/ijett-v48p236.

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Shi, Xiaobo, Yixue Hao, Delu Zeng, Lu Wang, M. Shamim Hossain, Sk Md Mizanur Rahman, and Abdulhameed Alelaiwi. "Cloud-Assisted Mood Fatigue Detection System." Mobile Networks and Applications 21, no. 5 (August 11, 2016): 744–52. http://dx.doi.org/10.1007/s11036-016-0757-x.

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Byrne, Angela, and Michael W. Eysenck. "Trait anxiety, anxious mood, and threat detection." Cognition & Emotion 9, no. 6 (November 1995): 549–62. http://dx.doi.org/10.1080/02699939508408982.

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Pyrovolakis, Konstantinos, Paraskevi Tzouveli, and Giorgos Stamou. "Multi-Modal Song Mood Detection with Deep Learning." Sensors 22, no. 3 (January 29, 2022): 1065. http://dx.doi.org/10.3390/s22031065.

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The production and consumption of music in the contemporary era results in big data generation and creates new needs for automated and more effective management of these data. Automated music mood detection constitutes an active task in the field of MIR (Music Information Retrieval). The first approach to correlating music and mood was made in 1990 by Gordon Burner who researched the way that musical emotion affects marketing. In 2016, Lidy and Schiner trained a CNN for the task of genre and mood classification based on audio. In 2018, Delbouys et al. developed a multi-modal Deep Learning system combining CNN and LSTM architectures and concluded that multi-modal approaches overcome single channel models. This work will examine and compare single channel and multi-modal approaches for the task of music mood detection applying Deep Learning architectures. Our first approach tries to utilize the audio signal and the lyrics of a musical track separately, while the second approach applies a uniform multi-modal analysis to classify the given data into mood classes. The available data we will use to train and evaluate our models comes from the MoodyLyrics dataset, which includes 2000 song titles with labels from four mood classes, {happy, angry, sad, relaxed}. The result of this work leads to a uniform prediction of the mood that represents a music track and has usage in many applications.

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Ferdiana, Ridi, Wiiliam Fajar Dicka, and Faturahman Yudanto. "MOOD DETECTION BASED ON LAST SONG LISTENED ON SPOTIFY." ASEAN Engineering Journal 12, no. 3 (August 31, 2022): 123–27. http://dx.doi.org/10.11113/aej.v12.16834.

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A Song Is One Medium Used To Express Someone’s Emotion, Whether As A Performer Or Audience. With The Advancement Of Machine Learning And A Deeper Understanding Of Sentiment Analysis, We Decided To Study Mood Detection Based On The Last Song Listened To. One of the direct ways to measure someone's mood is by using a Four-dimensional Mood Scale (FDMS) device. This device categorized mood into four dimensions: low valence, high valence, low arousal, and high arousal. In this article, we used a variation of FDMS adapted to the Indonesian language called FDMS-55 to compare the result from our model. Our model is trained using song data collected from Spotify and Genius using their respective API (Application Programming Interface). We classified manually into a mood class and then processed further using Azure Cognitive Service Text Analytics API. Based on evaluation conducted on the model, the FastTreeOva algorithm produces the highest accuracy both on valence class with 0.8901 and arousal class with 0.9167. The comparison between the model result and respondent's FDMS-55 device result is made with cosine similarity and yields similarity value of 0.770 with 0.103 standard deviation. It is concluded that someone's mood is related to the song they listened to, and our model can precisely predict someone's mood based on the last song they listened to.

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Sundarrajan, Aksharaa, and M. Aneesha. "Survey on Detection of Metal Illnesses by Analysing Twitter Data." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 37. http://dx.doi.org/10.14419/ijet.v7i2.24.11995.

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Mental illnesses are serious problems that places a burden on individuals, their families and on society in general. Although their symptoms have been known for several years, accurate and quick diagnoses remain a challenge. Inaccurate or delayed diagnoses results in increased frequency and severity of mood episodes, and reduces the benefits of treatment. In this survey paper, we review papers that leverage data from social media and design predictive models. These models utilize patterns of speech and life features of various subjects to determine the onset period of bipolar disorder. This is done by studying the patients, their behaviour, moods and sleeping patterns, and then effectively mapping these features to detect whether they are currently in a prodromal phase before a mood episode or not.

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John, Adebayo Kolawole, Adekoya Adewale M., and Ekwonna Chinnasa. "Temperament and Mood Detection Using Case-Based Reasoning." International Journal of Intelligent Systems and Applications 6, no. 3 (February 8, 2014): 50–61. http://dx.doi.org/10.5815/ijisa.2014.03.05.

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Braverman, Julia. "The Effect of Mood on Detection of Covariation." Personality and Social Psychology Bulletin 31, no. 11 (November 2005): 1487–97. http://dx.doi.org/10.1177/0146167205276152.

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Kalyani, Ekta Gupta, Geetanjali Rathee, Pardeep Kumar, and Durg Singh Chauhan. "Mood Swing Analyser: A Dynamic Sentiment Detection Approach." Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 85, no. 1 (December 16, 2014): 149–57. http://dx.doi.org/10.1007/s40010-014-0169-x.

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Nolazco-Flores, Juan Arturo, Marcos Faundez-Zanuy, Oliver Alejandro Velázquez-Flores, Carolina Del-Valle-Soto, Gennaro Cordasco, and Anna Esposito. "Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning." Sensors 22, no. 4 (February 21, 2022): 1686. http://dx.doi.org/10.3390/s22041686.

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In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen’s position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation–based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively.

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Lee, Dongwook, and Rémi Bourgeois. "GP-MOOD: a positivity-preserving high-order finite volume method for hyperbolic conservation laws." Proceedings of the International Astronomical Union 16, S362 (June 2020): 373–79. http://dx.doi.org/10.1017/s1743921322001363.

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AbstractWe present an a posteriori shock-capturing finite volume method algorithm called GP-MOOD. The method solves a compressible hyperbolic conservative system at high-order solution accuracy in multiple spatial dimensions. The core design principle in GP-MOOD is to combine two recent numerical methods, the polynomial-free spatial reconstruction methods of GP (Gaussian Process) and the a posteriori detection algorithms of MOOD (Multidimensional Optimal Order Detection). We focus on extending GP’s flexible variability of spatial accuracy to an a posteriori detection formalism based on the MOOD approach. The resulting GP-MOOD method is a positivity-preserving method that delivers its solutions at high-order accuracy, selectable among three accuracy choices, including third-order, fifth-order, and seventh-order.

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Majumdar, Srijita, Debabrata Datta, Arpan Deyasi, Soumen Mukherjee, Arup Kumar Bhattacharjee, and Anal Acharya. "Sarcasm Analysis and Mood Retention Using NLP Techniques." International Journal of Information Retrieval Research 12, no. 1 (January 2022): 1–23. http://dx.doi.org/10.4018/ijirr.289952.

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Sarcasm detection in written texts is the Achilles’ heel of research areas in sentiment analysis, especially with the absence of the rightful verbal tone, facial expression or body gesture that leads to random misinterpretations. It is crucial in sectors of social media, advertisements and user feedbacks on services that require proper interpretation for service evaluation and improvisation of their products. The objective here thereby is to identify sarcasm within a given text by experimenting with the original predicted mood of the text and work on its transformation with the several variations in combination of the standard sarcastic elements present in the corresponding writing. Here standard NLP techniques are used for identification and interpretation. This involves detecting primary connotation of the given text (e.g. positive/neutral/negative), followed by detecting elements of sarcasm. Then, under the presence of the sarcasm indicator algorithm, the rightful interpretation of the previously detected mood is attempted.

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M, Dheepthi, and M. Hemalatha. "Dynamic Mood Detection in Chat Application Using Text Pattern Analysis." International Journal of Advances in Applied Sciences 4, no. 4 (December 1, 2015): 124. http://dx.doi.org/10.11591/ijaas.v4.i4.pp124-129.

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<p>In modern communication and social networking the peoples of different ages are of different moods while chatting. This paper deals with detecting the modes and identifying human emotions through text mining. This paper explored to detect the mood variation of different age group that swings is maximum as compared to other age group. The moods can be classified in the basis of gender, age group and the emotion while texting. The random sample is taken from public chat in that the users are manually classified for strength of positive and negative emotions. By classifying emotions and using decision tree different variations are analyzed in this paper. Outlier study is used to recognize emotion distinction in child having any kind of disability. The pattern of the text is analysed and clustered and with the help of Besiyan classifier the text is classified in accordance with their emotions.</p>

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Lee, Jong-In, Dong-Gyu Yeo, and Byeong-Man Kim. "Detection of Music Mood for Context-aware Music Recommendation." KIPS Transactions:PartB 17B, no. 4 (August 31, 2010): 263–74. http://dx.doi.org/10.3745/kipsta.2010.17b.4.263.

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Lie Lu, D. Liu, and Hong-Jiang Zhang. "Automatic mood detection and tracking of music audio signals." IEEE Transactions on Audio, Speech and Language Processing 14, no. 1 (January 2006): 5–18. http://dx.doi.org/10.1109/tsa.2005.860344.

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Rajdeep, Bhoomi, Hardik B. ,. Patel, and Sailesh Iyer. "Human Emotion Identification from Speech using Neural Network." International Journal of Computers 16 (November 10, 2022): 87–103. http://dx.doi.org/10.46300/9108.2022.16.15.

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Detection of mood and behavior by voice analysis which helps to detect the speaker’s mood by the voice frequency. Here, I aim to present the mood like happy, and sad and behavior detection devices using machine learning and artificial intelligence which can be detected by voice analysis. Using this device, it detects the user’s mood. Moreover, this device detects the frequency by trained model and algorithm. The algorithm is well trained to catch the frequency where it helps to identify the mood happy or sad of the speaker and behavior. On the other hand, behavior can be predicted in form, it can be either positive or negative. So, this device helps to prevent mental health issues and is used in medical and gaming testing. Furthermore, it is easy to identify a person’s mood by their expression and by their actions in daily activities. But it is effective and challenging to detect mood and behavior by voice frequency because a rich environment affects the most. Thus, this device works as a signal processing.

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Bhattarai, Bhuwan, and Joonwhoan Lee. "Automatic Music Mood Detection Using Transfer Learning and Multilayer Perceptron." INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS 19, no. 2 (June 30, 2019): 88–96. http://dx.doi.org/10.5391/ijfis.2019.19.2.88.

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Et. al., ShanthaShalini K,. "Facial Emotion Based Music Recommendation System using computer vision and machine learning techiniques." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 912–17. http://dx.doi.org/10.17762/turcomat.v12i2.1101.

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The face is an important aspect in predicting human emotions and mood. Usually the human emotions are extracted with the use of camera. There are many applications getting developed based on detection of human emotions. Few applications of emotion detection are business notification recommendation, e-learning, mental disorder and depression detection, criminal behaviour detection etc. In this proposed system, we develop a prototype in recommendation of dynamic music recommendation system based on human emotions. Based on each human listening pattern, the songs for each emotions are trained. Integration of feature extraction and machine learning techniques, from the real face the emotion are detected and once the mood is derived from the input image, respective songs for the specific mood would be played to hold the users. In this approach, the application gets connected with human feelings thus giving a personal touch to the users. Therefore our projected system concentrate on identifying the human feelings for developing emotion based music player using computer vision and machine learning techniques. For experimental results, we use openCV for emotion detection and music recommendation.

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Kilimci, Zeynep Hilal, Aykut Güven, Mitat Uysal, and Selim Akyokus. "Mood Detection from Physical and Neurophysical Data Using Deep Learning Models." Complexity 2019 (December 14, 2019): 1–15. http://dx.doi.org/10.1155/2019/6434578.

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Nowadays, smart devices as a part of daily life collect data about their users with the help of sensors placed on them. Sensor data are usually physical data but mobile applications collect more than physical data like device usage habits and personal interests. Collected data are usually classified as personal, but they contain valuable information about their users when it is analyzed and interpreted. One of the main purposes of personal data analysis is to make predictions about users. Collected data can be divided into two major categories: physical and behavioral data. Behavioral data are also named as neurophysical data. Physical and neurophysical parameters are collected as a part of this study. Physical data contains measurements of the users like heartbeats, sleep quality, energy, movement/mobility parameters. Neurophysical data contain keystroke patterns like typing speed and typing errors. Users’ emotional/mood statuses are also investigated by asking daily questions. Six questions are asked to the users daily in order to determine the mood of them. These questions are emotion-attached questions, and depending on the answers, users’ emotional states are graded. Our aim is to show that there is a connection between users’ physical/neurophysical parameters and mood/emotional conditions. To prove our hypothesis, we collect and measure physical and neurophysical parameters of 15 users for 1 year. The novelty of this work to the literature is the usage of both combinations of physical and neurophysical parameters. Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. Multinomial Naïve Bayes (MNB), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Decision Integration Strategy (DIS) are evaluated as conventional machine learning algorithms. To the best of our knowledge, this is the very first attempt to analyze the neurophysical conditions of the users by evaluating deep learning models for mood analysis and enriching physical characteristics with neurophysical parameters. Experiment results demonstrate that the utilization of deep learning methodologies and the combination of both physical and neurophysical parameters enhances the classification success of the system to interpret the mood of the users. A wide range of comparative and extensive experiments shows that the proposed model exhibits noteworthy results compared to the state-of-art studies.

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Sierra, Pilar, Lorenzo Livianos, Sergio Arques, Javier Castelló, and Luis Rojo. "Prodromal Symptoms to Relapse in Bipolar Disorder." Australian & New Zealand Journal of Psychiatry 41, no. 5 (May 2007): 385–91. http://dx.doi.org/10.1080/00048670701266854.

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In a cyclical and recurring illness such as bipolar disorder, prodrome detection is of vital importance. This paper describes manic and depressive prodromal symptoms to relapse, methods used in their detection, problems inherent in their assessment, and patients’ coping strategies. A review of the literature on the issue was performed using MEDLINE and EMBASE databases (1965–May 2006). ‘Bipolar disorder’, ‘prodromes’, ‘early symptoms’, ‘coping’, ‘manic’ and ‘depression’ were entered as key words. A hand search was conducted simultaneously and the references of the articles found were used to locate additional articles. The most common depressive prodromes are mood changes, psychomotor symptoms and increased anxiety; the most frequent manic prodromes are sleep disturbances, psychotic symptoms and mood changes. The manic prodromes also last longer. Certain psychological interventions, both at the individual and psychoeducational group level, have proven effective, especially in preventing manic episodes. Bipolar patients are highly capable of detecting prodromal symptoms to relapse, although they do find the depressive ones harder to identify. Learning detection, coping strategies and idiosyncratic prodromes are elements that should be incorporated into daily clinical practice with bipolar patients.

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Orrù, Germano, Mauro Giovanni Carta, and Alessia Bramanti. "Design of FRET Probes for SNP RS1006737, Related to Mood Disorder." Clinical Practice & Epidemiology in Mental Health 14, no. 1 (February 28, 2018): 53–62. http://dx.doi.org/10.2174/1745017901814010053.

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Background:Several studies have shown that the Single Nucleotide Polymorphism (SNP) in the CACAN1C gene, rs1006737, is related to different mood disorder illnesses, such as bipolar disorder and schizophrenia. Current day molecular procedures for allele detection of this gene can be very expensive and time consuming. Hence, a sensitive and specific molecular procedure for detecting these mutations in a large number of subjects is desirable, especially for research groups who have no complex laboratory equipment.Objective:The possibility of using a Fluorescence Resonance Energy Transfer (FRET) probe was evaluated by means of bioinformatic tools, designed for forecasting the molecular behavior of DNA probes used in the research field or for laboratory analysis methods.Method:In this study we used the DINAMelt Web Server to predict theTms of FRET oligo in the presence of the A and/or G allele in rs1006737. The PCR primers were designed by using oligo 4 and oligo 6 primer analysis software,Results:The molecular probe described in this study detected aTm difference of 5-6°C between alleles A and G in rs1006737, which also showed good discrimination for a heterozygous profile for this genomic region.Conclusion:Althoughin silicostudies represent a relatively new avenue of inquiry, they have now started to be used to predict how a molecular probe interacts with its biological target, reducing the time and costs of molecular test tuning. The results of this study seem promising for further laboratory tests on allele detection in rs1006737 region.

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Hussein, Abdelbaset. "Mood Detection Based on Arabic Text Documents using Machine Learning Methods." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4 (August 25, 2020): 4424–36. http://dx.doi.org/10.30534/ijatcse/2020/36942020.

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Elaad, Eitan, and Liza Zvi. "Effects of Mood and Personality on Psychophysiological Detection of Concealed Information." International Journal of Psychophysiology 108 (October 2016): 134. http://dx.doi.org/10.1016/j.ijpsycho.2016.07.394.

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Lam, Kam-Yiu, Jiantao Wang, Joseph Kee-Yin Ng, Song Han, Limei Zheng, Calvin Ho Chuen Kam, and Chun Jiang Zhu. "SmartMood: Toward Pervasive Mood Tracking and Analysis for Manic Episode Detection." IEEE Transactions on Human-Machine Systems 45, no. 1 (February 2015): 126–31. http://dx.doi.org/10.1109/thms.2014.2360469.

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Greenwood, Tiffany, Nancy Downs, Neal Swerdlow, and Victor Ferreira. "F117. Early Detection and Intervention for Mood Disorders: A Pilot Study." Biological Psychiatry 83, no. 9 (May 2018): S283. http://dx.doi.org/10.1016/j.biopsych.2018.02.730.

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Kadam, Abhi, Anupama Mhatre, Sayali Redasani, and Amit Nerurkar. "Light Actuation Based On Facial Mood Recognition." International Journal of Engineering and Computer Science 9, no. 05 (May 20, 2020): 25052–56. http://dx.doi.org/10.18535/ijecs/v9i05.4483.

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Current lighting technologies extend the options for changing the appearance of rooms and closed spaces, as such creating ambiences with an affective meaning. Using intelligence, these ambiences may instantly be adapted to the needs of the room’s occupant(s), possibly improving their well-being. In this paper, we set actuate lighting in our surrounding using mood detection. We analyze the mood of the person by Facial Emotion Recognition using deep learning model such as Convolutional Neural Network (CNN). On recognizing this emotion, we will actuate lighting in our surrounding in accordance with the mood. Based on implementation results, the system needs to be developed further by adding more specific data class and training data.

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Johnson, Richard F., and Donna J. McMenemy. "Target Detection, Rifle Marksmanship, and Mood during Three Hours of Simulated Sentry Duty." Proceedings of the Human Factors Society Annual Meeting 33, no. 20 (October 1989): 1414–18. http://dx.doi.org/10.1177/154193128903302011.

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The purpose of this study was to evaluate the effects of sentry duty time on the soldier's speed of detection of visually presented targets, his ability to hit targets (rifle marksmanship), and his mood. Prior to the test day, each of eight subjects was given five days of training on the Weaponeer Rifle Marksmanship Simulator and was familiarized with the targets to be presented during testing. The test session lasted three hours, during which time the subject assumed a standing foxhole position and monitored the target scene of the Weaponeer. The Weaponeer M16A1 modified rifle lay next to the subject at chest height. When a pop-up target appeared, the subject pressed a telegraph key, lifted the rifle, aimed, and fired at the target. Speed of target detection was measured in terms of the time required by the subject to press the telegraph key in response to the presentation of the target. Marksmanship was measured in terms of number of targets hit. Target detection time and rifle marksmanship were averaged every 30 minutes. At the end of the test session, the subject completed the Profile of Mood States rating scale. The results showed that target detection time deteriorated with time on sentry duty; impairments were not evident within the first hour but were clearly evident by 1.5 hours. Marksmanship remained constant over time; soldiers were just as accurate in hitting the targets at the end of the 3 hours of sentry duty as they were at the beginning. Whereas the soldier's predominant mood during baseline practice sessions was one of vigor, during sentry duty the predominant mood was one of fatigue. The results of this study suggest that sentry duty performance may be optimized if it is limited to one hour or less.

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Shukla, Pushkal Kumar. "ESPOTIFY (An Emotion Based Music Player)." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (July 15, 2021): 604–11. http://dx.doi.org/10.22214/ijraset.2021.36356.

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Human emotion plays an essential role in social relationships. Emotions are reflected from verbalization, hand gestures of a body, through outward appearances and facial expressions. Music is an art form that soothes and calms the human brain and body. To analyze the mood of an individual, we first need to examine its emotions. If we detect an individual's emotions, then we can also detect an individual's mood. Taking the above two aspects and blending them, our system deals with detecting the emotion of a person through facial expression and playing music according to the emotion detected that will alleviate the mood or calm the individual and can also get quicker songs according to the emotion, saving time from looking up different songs. Different expressions of the face could be angry, happy, sad, and neutral. Facial emotions can be captured and detected through an inbuilt camera or a webcam. In our project, the Fisherface Algorithm is used for the detection of human emotions. After detecting an individual's emotion, our system will play the music automatically based on the emotion of an individual.

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Forgas, Joseph P. "Happy Believers and Sad Skeptics? Affective Influences on Gullibility." Current Directions in Psychological Science 28, no. 3 (April 5, 2019): 306–13. http://dx.doi.org/10.1177/0963721419834543.

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How does affect influence gullibility? After a brief consideration of the nature of gullibility, I describe a series of experiments that explored the prediction that in situations in which close attention to stimulus information is required, negative mood can reduce gullibility and positive mood can increase gullibility. The experiments examined mood effects on truth judgments, vulnerability to misleading information, the tendency to uncritically accept interpersonal messages, the detection of deception, and the tendency to see meaning in random or meaningless information. In all of these domains, positive mood promoted gullibility and negative mood reduced it. The practical and theoretical significance of these convergent findings are discussed, and the practical implications of affectively induced gullibility in real-life domains are considered.

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Turska, Elzbieta, Szymon Jurga, and Jaroslaw Piskorski. "Mood Disorder Detection in Adolescents by Classification Trees, Random Forests and XGBoost in Presence of Missing Data." Entropy 23, no. 9 (September 14, 2021): 1210. http://dx.doi.org/10.3390/e23091210.

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We apply tree-based classification algorithms, namely the classification trees, with the use of the rpart algorithm, random forests and XGBoost methods to detect mood disorder in a group of 2508 lower secondary school students. The dataset presents many challenges, the most important of which is many missing data as well as the being heavily unbalanced (there are few severe mood disorder cases). We find that all algorithms are specific, but only the rpart algorithm is sensitive; i.e., it is able to detect cases of real cases mood disorder. The conclusion of this paper is that this is caused by the fact that the rpart algorithm uses the surrogate variables to handle missing data. The most important social-studies-related result is that the adolescents’ relationships with their parents are the single most important factor in developing mood disorders—far more important than other factors, such as the socio-economic status or school success.

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Duberstein, Paul R., Yan Ma, Benjamin P. Chapman, Yeates Conwell, Joanne McGriff, James C. Coyne, Nathan Franus, et al. "Detection of depression in older adults by family and friends: distinguishing mood disorder signals from the noise of personality and everyday life." International Psychogeriatrics 23, no. 4 (September 30, 2010): 634–43. http://dx.doi.org/10.1017/s1041610210001808.

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ABSTRACTBackground: The capacity of friends and family member informants to make judgments about the presence of a mood disorder history in an older primary care patient has theoretical, clinical, and public health significance. This study examined the accuracy of informant-reported mood disorder diagnoses in a sample of primary care patients aged 65 years or older. We hypothesized that the accuracy (sensitivity and specificity) of informant reports would vary with the patient's personality.Methods: Hypotheses were tested in 191 dyads consisting of patients and their friends or relatives (informants) recruited from primary care settings. Gold-standard mood disorder diagnoses were established at consensus conferences based on a review of medical charts and data collected in a structured interview with the patient. Patients completed an assessment battery that included the NEO-Five Factor Inventory.Results: Sensitivity and specificity of informant-derived mood disorder diagnoses were related to patient personality. Sensitivity of informant-derived lifetime mood disorder diagnoses was compromised by higher Extraversion and higher Agreeableness. Specificity of informant-derived lifetime mood disorder diagnoses was compromised by lower Agreeableness and higher Conscientiousness.Conclusion: Patient personality has implications for the accuracy of mood disorder histories provided by friends and family members. Given that false negatives can have grave consequences, we recommend that practitioners be particularly vigilant when interpreting collateral information about their extraverted, agreeable patients.

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Vázquez, Fernando L., Ricardo F. Muñoz, Vanessa Blanco, and María López. "Validation of Muñoz's Mood Screener in a Nonclinical Spanish Population." European Journal of Psychological Assessment 24, no. 1 (January 2008): 57–64. http://dx.doi.org/10.1027/1015-5759.24.1.57.

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Abstract. This study evaluates the utility of Muñoz's Mood Screener for detection of major depressive episodes in a nonclinical Spanish population. The Mood Screener was administered by face-to-face interview to 554 subjects (65.9% women; age 18-34 years) who were recruited by stratified random sampling from a population of 27,587 university students. Thereafter, two expert clinicians who were blind to the Mood Screener results independently administered a clinical interview (SCID-CV) as an aid in evaluating the subjects for the same disorder. κ for agreement between the clinicians' consensus diagnosis and the Mood Screener was 0.758, and with the clinicians' diagnosis as reference the Mood Screener had a sensitivity of 0.969, a specificity of 0.967, positive and negative predictive values of 0.646 and 0.998, respectively, and positive and negative likelihood ratios of 29.75 and 0.032, respectively. These results support the interviewer-administered Mood Screener as a valid instrument for screening for major depressive episodes in the target population.

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Lushnikova, Olga L. "Social mood in single-industry towns of Khakassia." Вестник Пермского университета. Философия. Психология. Социология, no. 1 (2022): 175–85. http://dx.doi.org/10.17072/2078-7898/2022-1-175-185.

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The socio-economic development of any single-industry territory mainly depends on the prosperity of the city-forming enterprise, and this makes the city vulnerable, especially in crisis times. During such periods, social well-being of the population worsens, protest moods increase, social tension accrues, which can lead to open conflicts. The main problem lies in the timely detection of potentially dangerous hotbeds of social explosion. Therefore, it is important to monitor changes in the social moods for regulating the factors of social tension. As a hypothesis, it was suggested that the social moods of residents of single-industry territories depend on the current socio-economic situation: the mood is more optimistic in single-industry territories with a stable situation and is pessimistic in single-industry territories with a difficult situation. There was conducted a survey among the residents of single-industry territories of Khakassia (n=1,000). The results confirmed the main hypothesis of the study. Social moods are more depressive in single-industry territories with the most difficult economic situation. This is evidenced by negative assessments given by the residents with regard to their life, low satisfaction, the lack of prospects for the situation to improve, and dependent psychology — the expectation of help from the state. Despite the tense situation on the labor market, social mood is more optimistic in single-industry territories only being at risk, which may be due to more attractive living conditions. However, the results show that subjective assessments have a more significant impact on social moods than quantitative indicators of living standards. It proves the importance of non-economic mechanisms for regulating the factors of social tension, which can be more effective, especially in times of crisis.

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Kunjali Ajeeth Kumar, Yashaswini, and Adithya Kishore Saxena. "Stochastic modelling of transition dynamic of mixed mood episodes in bipolar disorder." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 620. http://dx.doi.org/10.11591/ijece.v12i1.pp620-629.

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In the present state of health and wellness, mental illness is always deemed less importance compared to other forms of physical illness. In reality, mental illness causes serious multi-dimensional adverse effect to the subject with respect to personal life, social life, as well as financial stability. In the area of mental illness, bipolar disorder is one of the most prominent type which can be triggered by any external stimulation to the subject suffering from this illness. There diagnosis as well as treatment process of bipolar disorder is very much different from other form of illness where the first step of impediment is the correct diagnosis itself. According to the standard body, there are classification of discrete forms of bipolar disorder viz. type-I, type-II, and cyclothymic. Which is characterized by specific mood associated with depression and mania. However, there is no study associated with mixed-mood episode detection which is characterized by combination of various symptoms of bipolar disorder in random, unpredictable, and uncertain manner. Hence, the model contributes to obtain granular information with dynamics of mood transition. The simulated outcome of the proposed system in MATLAB shows that resulting model is capable enough for detection of mixed mood episode precisely

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Cincotto, Fernando H., Daniel A. S. Carvalho, Thiago C. Canevari, Henrique E. Toma, Orlando Fatibello-Filho, and Fernando C. Moraes. "A nano-magnetic electrochemical sensor for the determination of mood disorder related substances." RSC Advances 8, no. 25 (2018): 14040–47. http://dx.doi.org/10.1039/c8ra01857j.

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The simultaneous electrochemical detection of mood disorder related substances, amitriptyline, melatonin and tryptophan, was successfully achieved by using a novel nano-magnetic electrochemical sensor decorated with carbon quantum dots (MagNPs/Cdots).

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Peet, Malcolm. "The Prevention of Suicide in Patients with Recurrent Mood Disorder." Journal of Psychopharmacology 6, no. 2_suppl (March 1992): 334–39. http://dx.doi.org/10.1177/0269881192006002091.

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Recurrent mood disorder carries a risk of suicide of ~ 15%. Patients who do commit suicide have often received inadequate antidepressant or prophylactic lithium treatment. Long-term treatment with lithium normalizes the excess mortality associated with recurrent mood disorders, including that from suicide. A reduced availability of the most lethal methods of suicide may contribute epidemiologically to a reduced rate of suicide, and therefore the differences in overdose toxicity between antidepressants may be pertinent. Education of mental health workers regarding the effective treatment of mood disorders can help to reduce the rate of suicide. Patient education and psychological support can lead to improved compliance with prophylactic medication and early detection of relapse, but more formal psychotherapy does not appear to be helpful. Specialized mood disorder clinics lead to better patient care than is possible in a routine psychiatric out-patient clinic.

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Fantoni, Carlo, Sara Rigutti, and Walter Gerbino. "Bodily action penetrates affective perception." PeerJ 4 (February 15, 2016): e1677. http://dx.doi.org/10.7717/peerj.1677.

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Fantoni & Gerbino (2014) showed that subtle postural shifts associated with reaching can have a strong hedonic impact and affect how actors experience facial expressions of emotion. Using a novel Motor Action Mood Induction Procedure (MAMIP), they found consistent congruency effects in participants who performed a facial emotionidentificationtask after a sequence of visually-guided reaches: a face perceived as neutral in a baseline condition appeared slightly happy after comfortable actions and slightly angry after uncomfortable actions. However, skeptics about the penetrability of perception (Zeimbekis & Raftopoulos, 2015) would consider such evidence insufficient to demonstrate that observer’s internal states induced by action comfort/discomfort affect perception in a top-down fashion. The action-modulated mood might have produced a back-end memory effect capable of affecting post-perceptual and decision processing, but not front-end perception.Here, we present evidence that performing a facial emotiondetection(not identification) task after MAMIP exhibits systematic mood-congruentsensitivitychanges, rather than responsebiaschanges attributable to cognitive set shifts; i.e., we show that observer’s internal states induced by bodily action can modulate affective perception. The detection threshold forhappinesswas lower after fifty comfortable than uncomfortable reaches; while the detection threshold forangerwas lower after fifty uncomfortable than comfortable reaches. Action valence induced an overall sensitivity improvement in detecting subtle variations of congruent facial expressions (happiness afterpositivecomfortable actions, anger afternegativeuncomfortable actions), in the absence of significant response bias shifts. Notably, both comfortable and uncomfortable reaches impact sensitivity in an approximately symmetric way relative to a baseline inaction condition. All of these constitute compelling evidence of a genuine top-down effect on perception: specifically, facial expressions of emotion arepenetrableby action-induced mood. Affective priming by action valence is a candidate mechanism for the influence of observer’s internal states on properties experienced as phenomenally objective and yet loaded with meaning.

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Tsai, Ming-Fong, Pei-Ching Lin, Zi-Hao Huang, and Cheng-Hsun Lin. "Multiple Feature Dependency Detection for Deep Learning Technology—Smart Pet Surveillance System Implementation." Electronics 9, no. 9 (August 27, 2020): 1387. http://dx.doi.org/10.3390/electronics9091387.

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Image identification, machine learning and deep learning technologies have been applied in various fields. However, the application of image identification currently focuses on object detection and identification in order to determine a single momentary picture. This paper not only proposes multiple feature dependency detection to identify key parts of pets (mouth and tail) but also combines the meaning of the pet’s bark (growl and cry) to identify the pet’s mood and state. Therefore, it is necessary to consider changes of pet hair and ages. To this end, we add an automatic optimization identification module subsystem to respond to changes of pet hair and ages in real time. After successfully identifying images of featured parts each time, our system captures images of the identified featured parts and stores them as effective samples for subsequent training and improving the identification ability of the system. When the identification result is transmitted to the owner each time, the owner can get the current mood and state of the pet in real time. According to the experimental results, our system can use a faster R-CNN model to improve 27.47%, 68.17% and 26.23% accuracy of traditional image identification in the mood of happy, angry and sad respectively.

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Sela, Yaron, Lorena Santamaria, Yair Amichai-Hamburge, and Victoria Leong. "Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories." Sensors 20, no. 20 (October 12, 2020): 5781. http://dx.doi.org/10.3390/s20205781.

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The commercial availability of many real-life smart sensors, wearables, and mobile apps provides a valuable source of information about a wide range of human behavioral, physiological, and social markers that can be used to infer the user’s mental state and mood. However, there are currently no commercial digital products that integrate these psychosocial metrics with the real-time measurement of neural activity. In particular, electroencephalography (EEG) is a well-validated and highly sensitive neuroimaging method that yields robust markers of mood and affective processing, and has been widely used in mental health research for decades. The integration of wearable neuro-sensors into existing multimodal sensor arrays could hold great promise for deep digital neurophenotyping in the detection and personalized treatment of mood disorders. In this paper, we propose a multi-domain digital neurophenotyping model based on the socioecological model of health. The proposed model presents a holistic approach to digital mental health, leveraging recent neuroscientific advances, and could deliver highly personalized diagnoses and treatments. The technological and ethical challenges of this model are discussed.

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Jiang, Julie, Nils Murrugara-Llerena, Maarten W. Bos, Yozen Liu, Neil Shah, Leonardo Neves, and Francesco Barbieri. "Sunshine with a Chance of Smiles: How Does Weather Impact Sentiment on Social Media?" Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 393–404. http://dx.doi.org/10.1609/icwsm.v16i1.19301.

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The environment we are in can affect our mood and behavior. One environmental factor is weather, which is linked to sentiment as expressed on social media. However, less is known about how integrating changes in weather, along with time and location contextual cues, can improve sentiment detection and understanding. In this paper, we explore the effects of three contextual features--weather, location, and time--on expressed sentiment in social media. Leveraging a large Snapchat dataset, we provide extensive experimental evidence that including contextual features in addition to textual features significantly improves textual sentiment detection performance by 3% over transformer-based language models. Our results also generalize cross-domain to Twitter. Ablation studies indicate the relative importance of weather compared to location and time. We also conduct correlation analyses on 8 million Snapchat posts to highlight the link between past weather and current sentiment, showing that weather has a lasting impact on mood. Users generally exhibit more positive sentiment in better weather conditions as well as in improved weather conditions. Additionally, we show that temperature's link with mood holds after controlling for time or population density, but there exist geographical differences in how temperature affects mood. Our work demonstrates the effectiveness of including external contexts in linguistic tasks and carries design implications for researchers and designers of social media.

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Nasution, Huwainan Nisa, and Hadiq Firdausi. "PENDEKATAN DIAGNOSIS DAN TATALAKSANA GANGGUAN MOOD PADA USIA LANJUT." JURNAL KEDOKTERAN 6, no. 2 (May 3, 2021): 131. http://dx.doi.org/10.36679/kedokteran.v6i2.333.

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Gangguan mood rentan diderita para penderita berusia lanjut. Statistik menunjukkan, terjadi peningkatan presentasi gangguan ini terutama pada penduduk lanjut usia. Hal ini kemungkinan disebabkan oleh gejala post power syndrome yang menyebabkan para lansia menjadi stres dan depresi. Penyebab lainnya yang dapat mencetuskan adalah penyakit komorbid yang menyertai kemudian menimbulkan pergantian mood yang cepat. Penyakit diabetes, tekanan darah tinggi, dan jantung koroner misalnya, diduga menyebabkan penderita merasa hilang kekuatan, kesulitan menyesuaikan diri, hingga akhirnya depresi. Kejadian bunuh diri pada usia lanjut yang mengalami gangguan mood juga dapat terjadi hampir setiap hari. Pentingnya deteksi dan diagnosis sejak dini merupakan hal yang penting demi mendapatkan terapi lebih dini. Gangguan mood pada usia lanjut bukanlah hal yang natural terkait proses penuaan, melainkan suatu gangguan patologis yang dapat diterapi.Kata kunci: gangguan mood, depresi, manik, usia lanjut, geriatri. ABSTRACTMood disorders are susceptible to elderly sufferers. Statistics show, there is an increase in the presentation of mood disorder, especially in the elderly population. This is probably caused by the symptoms of post power syndrome which causes the elderly to become stressed and depressed. Other causes that can trigger are comorbid diseases that accompany and then cause rapid mood changes. Diabetes, hypertension, and coronary heart disease, for example, are thought to cause lost of strength, difficulty adjusting, and depression. Suicides in the elderly with mood disorders can also occur almost every day. The importance of early detection and diagnosis is important in order to get early therapy. Mood disorders in elderly are not a natural thing related to the aging process, but a pathological disorder that can be treated.Key words: mood disorders, depression, manic, elderly, geriatrics.

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Rastogi, Rohit, Prabhat Yadav, and Jayash Raj Singh Yadav. "Statistical Analysis of Human Emotions to Suggest Suitable Music as per Individual's Mood." International Journal of Cyber Behavior, Psychology and Learning 11, no. 3 (July 2021): 34–67. http://dx.doi.org/10.4018/ijcbpl.2021070104.

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There is music recommendation software and music providers that are well explored and commonly used, but those are generally based on simple similarity calculations and manually tagged parameters. This project proposes a music recommendation system based on emotion detection of users, automatic computing, and classification. Music is recommended based on the emotion expressed and temper of the user. Like artists and genre, emotion of the user can also be a crucial recommendation point for music listeners. The different mооds in whiсh the system will сlаssify the imаges аre hаррy, neutrаl, аnd sаd. The system will рre-sоrt the songs according to their genre in the above-mentioned categories. This research project gives us advancement in the music industry with the help of machine learning and artificial intelligence and will reduce the hassle of selecting songs in our leisure time and will automatically play songs by detecting the emotion of the user. This data can be used to play the songs that match the current mood detected from the provided input by the user.

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Monroe, Lauren E., and Samantha L. Smith. "If You’re Happy and You Know it Stay Alert: The Effects of Lighting on Vigilance Performance and Affective State." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (September 2021): 854–58. http://dx.doi.org/10.1177/1071181321651314.

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Vigilance, or sustained attention tasks involve detecting critical signals, embedded amid more frequent neutral signals, over an extended period of time. A decline in performance, engagement, and arousal over time, as well as high workload and stress, are common outcomes of such tasks. Exposure to broad-spectrum or short wavelength bright light has been found to positively impact alertness, speed of information processing, and mood, but has not been extensively explored in the vigilance domain. The present study explored whether a light therapy lamp could mitigate the negative vigilance outcomes found in both performance and affective state. Results indicated that the therapy light did not prevent a decline in detection of critical signals over time, nor significantly impact workload, sleepiness, or subjective stress state compared to a dim light condition. However, mood questionnaire results suggest that lighting may impact separate constructs of arousal and tiredness, warranting further research.

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Reilley, Sean, Anthony F. Grasha, and John Schafer. "Workload, Error Detection, and Experienced Stress in a Simulated Pharmacy Verification Task." Perceptual and Motor Skills 95, no. 1 (August 2002): 27–46. http://dx.doi.org/10.2466/pms.2002.95.1.27.

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The relationships among workload, stress, and performance efficiency are topics of applied interest and theoretical importance to researchers concerned with human performance. Such interest extends to a variety of occupational areas including inpatient, outpatient, and community pharmacies. In that context, these relationships have become a consumer health issue given concerns that workload contributes to job stress and a significant decline in dispensing accuracy. In the present study, 102 trained college-aged individuals evaluated simulated pharmacy prescriptions for errors under conditions of either low workload (72 orders over 120 min. on task) or high workload (120 orders over 120 min. on task) in a high-fidelity simulated pharmacy environment. Overall, cumulative and detection theory indices of error detection were compatible with estimates from pharmacy field studies. When rates of sensitivity and specificity for detection were examined, substantial variations in the identification of errors (sensitivity) and difficulties with detection of data-entry mistakes were observed in the high workload condition, but only modest effects emerged for the low workload condition. Although increases in objective workload were associated with modest declines in detection accuracy, objective workload did nor significantly affect negative mood (Mood Adjective Checklist) or perceived workload (NASA-Task Load Index) as expected.

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Diot, S., S. Clain, and R. Loubère. "Improved detection criteria for the Multi-dimensional Optimal Order Detection (MOOD) on unstructured meshes with very high-order polynomials." Computers & Fluids 64 (July 2012): 43–63. http://dx.doi.org/10.1016/j.compfluid.2012.05.004.

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Narziev, Nematjon, Hwarang Goh, Kobiljon Toshnazarov, Seung Ah Lee, Kyong-Mee Chung, and Youngtae Noh. "STDD: Short-Term Depression Detection with Passive Sensing." Sensors 20, no. 5 (March 4, 2020): 1396. http://dx.doi.org/10.3390/s20051396.

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It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, physical activity, mood, social activity, sleep, and food intake and extracted features related to each symptom cluster from mobile devices’ sensors. We conducted an experiment, where we recruited 20 participants from four different depression groups based on PHQ-9 (the Patient Health Questionnaire-9, the 9-item depression module from the full PHQ), which are normal, mildly depressed, moderately depressed, and severely depressed and built a machine learning model for automatic classification of depression category in a short period of time. To achieve the aim of short-term depression classification, we developed Short-Term Depression Detector (STDD), a framework that consisted of a smartphone and a wearable device that constantly reported the metrics (sensor data and self-reports) to perform depression group classification. The result of this pilot study revealed high correlations between participants` Ecological Momentary Assessment (EMA) self-reports and passive sensing (sensor data) in physical activity, mood, and sleep levels; STDD demonstrated the feasibility of group classification with an accuracy of 96.00% (standard deviation (SD) = 2.76).

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Journal articles on the topic 'Mood detection'

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