Sentiment analysis of pets using deep learning technologies in artificial intelligence of things system

Ming-Fong Tsai,Jhao-Yang Huang

doi:10.1007/s00500-021-06038-z

Abstract

This research paper proposes sentiment analysis of pets using deep learning technologies in the artificial intelligence of things system. Mask R-CNN is used to detect image objects and generate contour mask maps, pose analysis algorithms are used to obtain object posture information, and at the same time object sound signals are converted into spectrograms as features, using deep learning image recognition technology to obtain object emotion information. By using the fusion of object posture and emotional characteristics as the basis for pet emotion identification and analysis, the detected specific pet behaviour states will be actively notified to the owner for processing. Compared with traditional speech recognition, which uses mel-frequency cepstral coefficients for feature extraction, coupled with a Gaussian mixture model-hidden Markov model for voice recognition, the experimental method of this research paper effectively improves the accuracy by 70%. Prior work on the implementation of smart pet surveillance systems has used the pet's tail and mouth as features, and has combined these with sound features to analyse the pet's emotions. This research paper proposes a new method of sentiment analysis in pets, and our method is compared with previous related work. Experimental results show that our approach increases the accuracy rate by 70%.

Highlights

Pet sentiment analysis can be used to analyse whether a pet is suffering from anxiety, hypothetical disorders and other mental illnesses
When the one-dimensional array identified by Faster R-CNN uses the TOP-3 result as the basis for emotion voting, the analysis accuracy of angry, sad, and normal states is 80%, 60%, and 70% respectively
When the onedimensional array identified by Faster R-CNN uses the TOP-5 result as the basis for emotion voting, the analysis accuracy of angry, sad, and normal states are 80%, 90%, and 90% respectively

Summary

System Functions

If the framed image with the pet's head facing to the left meets the condition of formula (4), it is judged to be standing. The framed image with the pet's head facing to the right still uses formula (4) to determine the standing posture conditions. The success rate of generating the contour mask map with the training sample image set is 100% and the average cosine similarity accuracy is 96.78%. The similarity of the standing, sitting and lying postures of the framed image under 70% noise interference is 69.81%, 60.33% and 67.06% respectively

Posture analysis

State recognition

Findings

Conclusions