Speech emotion recognition using 2D-convolutional neural network

This research proposes a speech emotion recognition model to predict human emotions using the convolutional neural network (CNN) by learning segmented audio of specific emotions. Speech emotion recognition utilizes the extracted features of audio waves to learn speech emotion characteristics; one of them is mel frequency cepstral coefficient (MFCC). Dataset takes a vital role to obtain valuable results in model learning. Hence this research provides the leverage of dataset combination implementation. The model learns a combined dataset with audio segmentation and zero padding using 2D-CNN. Audio segmentation and zero padding equalize the extracted audio features to learn the characteristics. The model results in 83.69% accuracy to predict seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise from the combined dataset with the segmentation of the audio files

Defining Dynamic Indicators for Social Network Analysis: A Case Study in the Automotive Domain using Twiter

Comunicación pesentada en 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR 2018) (18-20 septiembre Sevilla, España)In this paper we present a framework based on Linked Open Data Infrastructures to perform analysis tasks in social networks based on dynamically defined indicators. Based on the typical stages of business intelligence models, which starts from the definition of strategic goals to define relevant indicators (Key Performance Indicators), we propose a new scenario where the sources of information are the social networks. The fundamental contribution of this work is to provide a framework for easily specifying and monitoring social indicators based on the measures offered by the APIs of the most important social networks. The main novelty of this method is that all the involved data and information is represented and stored as Linked Data. In this work we demonstrate the benefits of using linked open data, especially for processing and publishing company-specific social metrics and indicators

Understanding Usage Intention of Social Media’s Innovative Functions: Based on Expanded Innovation Diffusion Theory

Drawing upon expanded innovation diffusion theory (IDT), this study investigates the social media users’ usage intention toward its innovative functions. 532 data were collected from the Chinese leading social media—WeChat. The results show that the relative advantage, ease of use, trialability, observability, subjective norm and image have positive effect on users’ usage intention. Whereas compatibility has no significant impact. Based upon these findings, we discussed the theoretical contributions and practical implication of this study

Neural Cognition and Affective Computing on Cyber Language

Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance) emotional space

Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks

Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition and physiological indicators, the establishment of a dynamic and complete database, and the addition of high-tech innovative products become recent trends in AC. This research aims to develop a deep gradient convolutional neural network (DGCNN) for classifying affection by using an eye-tracking signals. General signal process tools and pre-processing methods were applied firstly, such as Kalman filter, windowing with hamming, short-time Fourier transform (SIFT), and fast Fourier transform (FTT). Secondly, the eye-moving and tracking signals were converted into images. A convolutional neural networks-based training structure was subsequently applied; the experimental dataset was acquired by an eye-tracking device by assigning four affective stimuli (nervous, calm, happy, and sad) of 16 participants. Finally, the performance of DGCNN was compared with a decision tree (DT), Bayesian Gaussian model (BGM), and k-nearest neighbor (KNN) by using indices of true positive rate (TPR) and false negative rate (FPR). Customizing mini-batch, loss, learning rate, and gradients definition for the training structure of the deep neural network was also deployed finally. The predictive classification matrix showed the effectiveness of the proposed method for eye moving and tracking signals, which performs more than 87.2% inaccuracy. This research provided a feasible way to find more natural human-computer interaction through eye moving and tracking signals and has potential application on the affective production design process

Definición y análisis de indicadores estratégicos para redes sociales : un caso de estudio en el sector automovilístico

Treball Final de Màster Universitari en Sistemes Intel·ligents. Codi: SIU043. Curs: 2015/2016La disciplina Inteligencia de Negocios se dedica a definir indicadores estratégicos a partir de medidas de interés definidas sobre un conjunto de datos temporales recolectados desde diferentes fuentes, e integrados bajo un mismo esquema multidimensional. Tradicionalmente, los datos recolectados tienen un carácter corporativo (ventas, promociones, etc.) y son generados dentro de la misma empresa. Sin embargo, buena parte de la información estratégica relevante que puede afectar a una organización reside actualmente en fuentes externas, principalmente las redes sociales. Desafortunadamente existen pocos trabajos que establezcan los indicadores externos más adecuados para cada dominio, y la forma de calcularlos a partir de las mismas redes sociales. En este trabajo se hace un estudio tanto de los trabajos propuestos en la literatura, como de los sistemas que actualmente ofrecen algún tipo de informes y análisis sobre redes sociales. Una vez realizado este estudio se propondrá un método para definir indicadores sociales, siguiendo la metodología tradicional utilizada en BI para definir indicadores estratégicos. Por último, se desarrollará un caso de estudio sobre la infraestructura SLOD-BI para demostrar la utilidad del método propuesto

Definición y análisis de indicadores estratégicos para redes sociales : un caso de estudio en el sector automovilístico

Treball Final de Màster Universitari en Sistemes Intel·ligents. Codi: SIU043. Curs: 2015/2016La disciplina Inteligencia de Negocios se dedica a definir indicadores estratégicos a partir de medidas de interés definidas sobre un conjunto de datos temporales recolectados desde diferentes fuentes, e integrados bajo un mismo esquema multidimensional. Tradicionalmente, los datos recolectados tienen un carácter corporativo (ventas, promociones, etc.) y son generados dentro de la misma empresa. Sin embargo, buena parte de la información estratégica relevante que puede afectar a una organización reside actualmente en fuentes externas, principalmente las redes sociales. Desafortunadamente existen pocos trabajos que establezcan los indicadores externos más adecuados para cada dominio, y la forma de calcularlos a partir de las mismas redes sociales. En este trabajo se hace un estudio tanto de los trabajos propuestos en la literatura, como de los sistemas que actualmente ofrecen algún tipo de informes y análisis sobre redes sociales. Una vez realizado este estudio se propondrá un método para definir indicadores sociales, siguiendo la metodología tradicional utilizada en BI para definir indicadores estratégicos. Por último, se desarrollará un caso de estudio sobre la infraestructura SLOD-BI para demostrar la utilidad del método propuesto

Emotion Analysis of Telephone Complaints from Customer Based on Affective Computing

Customer complaint has been the important feedback for modern enterprises to improve their product and service quality as well as the customer’s loyalty. As one of the commonly used manners in customer complaint, telephone communication carries rich emotional information of speeches, which provides valuable resources for perceiving the customer’s satisfaction and studying the complaint handling skills. This paper studies the characteristics of telephone complaint speeches and proposes an analysis method based on affective computing technology, which can recognize the dynamic changes of customer emotions from the conversations between the service staff and the customer. The recognition process includes speaker recognition, emotional feature parameter extraction, and dynamic emotion recognition. Experimental results show that this method is effective and can reach high recognition rates of happy and angry states. It has been successfully applied to the operation quality and service administration in telecom and Internet service company

Extracting Information on Affective Computing Research from Data Analysis of Known Digital Platforms: Research into Emotional Artificial Intelligence

The topic of affective computing has been growing rapidly in recent times. In the last five years, the volume of publications in this field has tripled. The question arises which research trends are most in demand today. This can only be judged by analysing the publications that present the results of research. Since researchers have access to the entire global scientific publication space, the task of analysing big data arises. This leads to the problem of identifying the most significant results in the subject area of interest. This paper presents some results of the analysis of semi-structured information from scientific citation databases on the subject of “affective computing”

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

Due to the extensive social influence, public health emergency has attracted great attention in today’s society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event’s social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency