National Program of Excellence in Software Center, Chosun University, Gwangju 61452, Korea.
IT Research Institute, Chosun University, Gwangju 61452, Korea.
Sensors (Basel). 2021 Mar 12;21(6):1997. doi: 10.3390/s21061997.
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, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The "genetic algorithms as a feature selection method" (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.
情感信息代表用户当前的情绪状态，可以应用于多种场景，例如根据用户情绪状态推荐音乐的文化内容服务和用户情绪监测。为了提高用户满意度，推荐方法必须理解和反映用户的特点和环境，例如个人偏好和情绪。然而，大多数推荐方法不能准确地反映这些特点，也无法提高用户满意度。在本文中，我们广泛定义了六种人类情绪（中性、快乐、悲伤、愤怒、惊讶和无聊），以考虑用户的语音情绪信息并推荐匹配的内容。我们使用“遗传算法作为特征选择方法”（GAFS）算法根据语音情绪信息对标准化语音进行分类。我们使用支持向量机（SVM）算法为识别六个目标情绪选择了最优核函数。对于每种核函数的性能评估结果表明，径向基函数（RBF）核函数的情绪识别准确率最高，达到 86.98%。此外，我们还基于情绪信息对内容数据（图像和音乐）进行了分类，并使用因子分析、对应分析和欧几里得距离进行了分类。最后，我们使用基于情绪分类的语音信息和通过协作过滤技术识别的情绪信息来预测用户的情绪偏好，并在移动应用程序中推荐与用户情绪相匹配的内容。
