## 5 Discussion and Conclusions  
This research was based on Kansei engineering and artificial neural networks and used web crawler and natural language processing techniques to mine consumers' Kansei words on the sound of electric shavers online and the cluster analysis to extract representative product samples and Kansei words. Through ArtemiS 13.6 analysis, the sound design elements were divided into five items and 13 categories. Lastly, the QTTI multiple linear regression, GRNN, BPNN, and GA-BPNN non-linear analysis were combined with the SD method to construct prediction models between sound design elements and users' emotional evaluation values and to compare and select the best. The results of this research are as follows:  
The web crawler and Word2 Vec neural network can collect a wide range of data on users' imagery and feelings when using the product, which can compensate for the time-consuming and subjective problem of collecting data in the early stages of traditional Kansei engineering, while providing complete and effective preliminary data support. The QTTI, GRNN, BPNN, and GA-BPNN methods were applied to the research of product sound imagery. Multiple linear regression and non-linear perceptual prediction models were constructed and finally proved to be reliable, with GA-BPNN being the best in terms of accuracy and the rest being QTTI, GRNN, and BPNN, in that order. GA-BPNN uses GA to optimize BPNN, which can help designers to more accurately grasp the users' emotional evaluation of sound, while QTTI multiple linear regression modelling can provide designers with clear design indicators and references. A linear correlation model between sound design elements and imagery vocabulary was constructed using QTTI. It was able to provide a ranking of the degree of influence of each design item and category on the imagery, and the influence of its items and categories on the imagery was as follows:  
1. In the ranking of the impact of each item, the items in the \"Annoyance-Comfortable\" word are ranked in order of loudness > sharpness > A-weighted sound pressure level > roughness > tonality; in \"Inexpensive-Premium\" the items are ranked in order of influence as sharpness > loudness > roughness > tonality > A-weighted sound pressure level; in \"Weak-Powerful\" the items are ranked in order of influence as loudness > sharpness > tonality > A-weighted sound pressure level > roughness. 2. In the ranking of the impact of each category, the 'loudness' of \"Annoyance-Comfortable\" is taken as an example. Its score points indicate that the lower the loudness, the more comfortable the imagery conveyed, and, conversely, the higher the loudness, the more annoying it is. The second ranked item is sharpness, and its category score points indicate that the lower the sharpness, the more comfortable the imagery is conveyed, while the opposite is more annoying. The effects of the other categories can be followed in this way, as shown in Table 4.  
In conclusion, this research adopted a systematic approach, applying Kansei engineering and artificial neural networks to the research of product sound imagery, and provides an objective and accurate understanding of the relationship between the sound of electric shavers and consumers' emotional evaluation, and provides designers with explicit sound design indicators and references for optimal design. Conceptualization, Z.-H.L. and J.-C.W. ; methodology, Z.-H.L. and J.-C.W. ; software, Z.-H.L. and [PERSON]-C.W. ; validation J.-C.W. and F.L. ; formal analysis, Y.-T.C. ; investigation, J.-C.W. ; resources, Z.-H.L. ; data curation, Z.-H.L. ; writing--original draft preparation, Z.-H.L. ; writing--review and editing, J.-C.W. and F.L. ; visualization, Y.-T.C. ; supervision, J.-C.W. ; project administration, J.-C.W. ; funding acquisition, J.-C.W. All authors have read and agreed to the published version of the manuscript.