• Title/Summary/Keyword: Emotion Prediction System

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Implementing an Adaptive Neuro-Fuzzy Model for Emotion Prediction Based on Heart Rate Variability(HRV) (심박변이도를 이용한 적응적 뉴로 퍼지 감정예측 모형에 관한 연구)

  • Park, Sung Soo;Lee, Kun Chang
    • Journal of Digital Convergence
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    • v.17 no.1
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    • pp.239-247
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    • 2019
  • An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

Improvement of a Context-aware Recommender System through User's Emotional State Prediction (사용자 감정 예측을 통한 상황인지 추천시스템의 개선)

  • Ahn, Hyunchul
    • Journal of Information Technology Applications and Management
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    • v.21 no.4
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    • pp.203-223
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    • 2014
  • This study proposes a novel context-aware recommender system, which is designed to recommend the items according to the customer's responses to the previously recommended item. In specific, our proposed system predicts the user's emotional state from his or her responses (such as facial expressions and movements) to the previous recommended item, and then it recommends the items that are similar to the previous one when his or her emotional state is estimated as positive. If the customer's emotional state on the previously recommended item is regarded as negative, the system recommends the items that have characteristics opposite to the previous item. Our proposed system consists of two sub modules-(1) emotion prediction module, and (2) responsive recommendation module. Emotion prediction module contains the emotion prediction model that predicts a customer's arousal level-a physiological and psychological state of being awake or reactive to stimuli-using the customer's reaction data including facial expressions and body movements, which can be measured using Microsoft's Kinect Sensor. Responsive recommendation module generates a recommendation list by using the results from the first module-emotion prediction module. If a customer shows a high level of arousal on the previously recommended item, the module recommends the items that are most similar to the previous item. Otherwise, it recommends the items that are most dissimilar to the previous one. In order to validate the performance and usefulness of the proposed recommender system, we conducted empirical validation. In total, 30 undergraduate students participated in the experiment. We used 100 trailers of Korean movies that had been released from 2009 to 2012 as the items for recommendation. For the experiment, we manually constructed Korean movie trailer DB which contains the fields such as release date, genre, director, writer, and actors. In order to check if the recommendation using customers' responses outperforms the recommendation using their demographic information, we compared them. The performance of the recommendation was measured using two metrics-satisfaction and arousal levels. Experimental results showed that the recommendation using customers' responses (i.e. our proposed system) outperformed the recommendation using their demographic information with statistical significance.

A Study on Sentiment Pattern Analysis of Video Viewers and Predicting Interest in Video using Facial Emotion Recognition (얼굴 감정을 이용한 시청자 감정 패턴 분석 및 흥미도 예측 연구)

  • Jo, In Gu;Kong, Younwoo;Jeon, Soyi;Cho, Seoyeong;Lee, DoHoon
    • Journal of Korea Multimedia Society
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    • v.25 no.2
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    • pp.215-220
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    • 2022
  • Emotion recognition is one of the most important and challenging areas of computer vision. Nowadays, many studies on emotion recognition were conducted and the performance of models is also improving. but, more research is needed on emotion recognition and sentiment analysis of video viewers. In this paper, we propose an emotion analysis system the includes a sentiment analysis model and an interest prediction model. We analyzed the emotional patterns of people watching popular and unpopular videos and predicted the level of interest using the emotion analysis system. Experimental results showed that certain emotions were strongly related to the popularity of videos and the interest prediction model had high accuracy in predicting the level of interest.

Group Emotion Prediction System based on Modular Bayesian Networks (모듈형 베이지안 네트워크 기반 대중 감성 예측 시스템)

  • Choi, SeulGi;Cho, Sung-Bae
    • Journal of KIISE
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    • v.44 no.11
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    • pp.1149-1155
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    • 2017
  • Recently, with the development of communication technology, it has become possible to collect various sensor data that indicate the environmental stimuli within a space. In this paper, we propose a group emotion prediction system using a modular Bayesian network that was designed considering the psychological impact of environmental stimuli. A Bayesian network can compensate for the uncertain and incomplete characteristics of the sensor data by the probabilistic consideration of the evidence for reasoning. Also, modularizing the Bayesian network has enabled flexible response and efficient reasoning of environmental stimulus fluctuations within the space. To verify the performance of the system, we predict public emotion based on the brightness, volume, temperature, humidity, color temperature, sound, smell, and group emotion data collected in a kindergarten. Experimental results show that the accuracy of the proposed method is 85% greater than that of other classification methods. Using quantitative and qualitative analyses, we explore the possibilities and limitations of probabilistic methodology for predicting group emotion.

Sentiment Prediction using Emotion and Context Information in Unstructured Documents (비정형 문서에서 감정과 상황 정보를 이용한 감성 예측)

  • Kim, Jin-Su
    • Journal of Convergence for Information Technology
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    • v.10 no.10
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    • pp.40-46
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    • 2020
  • With the development of the Internet, users share their experiences and opinions. Since related keywords are used witho0ut considering information such as the general emotion or genre of an unstructured document such as a movie review, the sensitivity accuracy according to the appropriate emotional situation is impaired. Therefore, we propose a system that predicts emotions based on information such as the genre to which the unstructured document created by users belongs or overall emotions. First, representative keyword related to emotion sets such as Joy, Anger, Fear, and Sadness are extracted from the unstructured document, and the normalized weights of the emotional feature words and information of the unstructured document are trained in a system that combines CNN and LSTM as a training set. Finally, by testing the refined words extracted through movie information, morpheme analyzer and n-gram, emoticons, and emojis, it was shown that the accuracy of emotion prediction using emotions and F-measure were improved. The proposed prediction system can predict sentiment appropriately according to the situation by avoiding the error of judging negative due to the use of sad words in sad movies and scary words in horror movies.

Emotion Prediction of Paragraph using Big Data Analysis (빅데이터 분석을 이용한 문단 내의 감정 예측)

  • Kim, Jin-su
    • Journal of Digital Convergence
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    • v.14 no.11
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    • pp.267-273
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    • 2016
  • Creation and Sharing of information which is structured data as well as various unstructured data. makes progress actively through the spread of mobile. Recently, Big Data extracts the semantic information from SNS and data mining is one of the big data technique. Especially, the general emotion analysis that expresses the collective intelligence of the masses is utilized using large and a variety of materials. In this paper, we propose the emotion prediction system architecture which extracts the significant keywords from social network paragraphs using n-gram and Korean morphological analyzer, and predicts the emotion using SVM and these extracted emotion features. The proposed system showed 82.25% more improved recall rate in average than previous systems and it will help extract the semantic keyword using morphological analysis.

An Applicable Verb Prediction in Augmentative Communication System for Korean Language Disorders (언어장애인용 문장발생장치에 적용 가능한 동사예측)

  • 이은실;홍승홍;민홍기
    • Science of Emotion and Sensibility
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    • v.3 no.1
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    • pp.25-32
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    • 2000
  • 본 논문에서는 언어장애인용 문장발생장치의 통신율을 증진시키기 위한 처리방안으로 신경망을 이용하여 문장발생장치에 동사예측을 적용하는 방법을 제안하였다. 각 단어들은 구문론과 의미론에 따른 정보벡터로 표현되며, 언어처리는 전통적으로 사전을 포함하는 것과는 달리, 상태공간에서 다양한 영역으로 분류되어 개념적으로 유사한 단어는 상태공간에서의 위치를 통하여 알게 된다. 사용자가 심볼을 누르면 심볼에 해당하는 단어는 상태공간에서의 위치를 찾아가며, 신경망 학습을 통해 동사를 예측하였고 그 결과 제한된 공간 내에서 약 20% 통신율 증진을 가져올 수 있었다.

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Emotion Prediction System using Movie Script and Cinematography (영화 시나리오와 영화촬영기법을 이용한 감정 예측 시스템)

  • Kim, Jinsu
    • Journal of the Korea Convergence Society
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    • v.9 no.12
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    • pp.33-38
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    • 2018
  • Recently, we are trying to predict the emotion from various information and to convey the emotion information that the supervisor wants to inform the audience. In addition, audiences intend to understand the flow of emotions through various information of non-dialogue parts, such as cinematography, scene background, background sound and so on. In this paper, we propose to extract emotions by mixing not only the context of scripts but also the cinematography information such as color, background sound, composition, arrangement and so on. In other words, we propose an emotional prediction system that learns and distinguishes various emotional expression techniques into dialogue and non-dialogue regions, contributes to the completeness of the movie, and quickly applies them to new changes. The precision of the proposed system is improved by about 5.1% and 0.4%, and the recall is improved by about 4.3% and 1.6%, respectively, when compared with the modified n-gram and morphological analysis.

An Emotion Recognition Technique using Speech Signals (음성신호를 이용한 감정인식)

  • Jung, Byung-Wook;Cheun, Seung-Pyo;Kim, Youn-Tae;Kim, Sung-Shin
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.4
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    • pp.494-500
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    • 2008
  • In the field of development of human interface technology, the interactions between human and machine are important. The research on emotion recognition helps these interactions. This paper presents an algorithm for emotion recognition based on personalized speech signals. The proposed approach is trying to extract the characteristic of speech signal for emotion recognition using PLP (perceptual linear prediction) analysis. The PLP analysis technique was originally designed to suppress speaker dependent components in features used for automatic speech recognition, but later experiments demonstrated the efficiency of their use for speaker recognition tasks. So this paper proposed an algorithm that can easily evaluate the personal emotion from speech signals in real time using personalized emotion patterns that are made by PLP analysis. The experimental results show that the maximum recognition rate for the speaker dependant system is above 90%, whereas the average recognition rate is 75%. The proposed system has a simple structure and but efficient to be used in real time.

Visualization using Emotion Information in Movie Script (영화 스크립트 내 감정 정보를 이용한 시각화)

  • Kim, Jinsu
    • Journal of the Korea Convergence Society
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    • v.9 no.11
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    • pp.69-74
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    • 2018
  • Through the convergence of Internet technology and various information technologies, it is possible to collect and process vast amount of information and to exchange various knowledge according to user's personal preference. Especially, there is a tendency to prefer intimate contents connected with the user's preference through the flow of emotional changes contained in the movie media. Based on the information presented in the script, the user seeks to visualize the flow of the entire emotion, the flow of emotions in a specific scene, or a specific scene in order to understand it more quickly. In this paper, after obtaining the raw data from the movie web page, it transforms it into a standardized scenario format after refining process. After converting the refined data into an XML document to easily obtain various information, various sentences are predicted by inputting each paragraph into the emotion prediction system. We propose a system that can easily understand the change of the emotional state between the characters in the whole or a specific part of the various emotions required by the user by mixing the predicted emotions flow and the amount of information included in the script.