# The Influence of Brand Visual Communication on Consumer Psychology Based on Deep Learning

Authors: Hui Wang
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In order to solve the problem that there are few methods of users’ consumption psychology, the author proposes a research on the influence of brand visual communication on consumer psychology based on deep learning. First, establish the mapping relationship between experience level-product features-aspect words and then use aspect word extraction technology, mining users’ attention to different experience levels from user comments and dividing users into three types: instinctive preference, behavioral preference, and reflective preference; finally, the deep learning-based aspect sentiment analysis technology is used to calculate the user’s preference for the product and further analyze the characteristics of different types of users. Experimental results show that based on the application analysis of more than 900,000 JD.com mobile phone review data, three types of consumer preference user groups were obtained, of which instinctive preference users accounted for 41.6%; it is higher than behavioral preference users (33.01%) and reflection preference users (25.39%), and the consumption characteristics of the three types of users are analyzed from the aspects of mobile phone brand and price. It is proved that the author’s user portrait method can better express the consumption preferences of different types of users.


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## Body

## 1. Introduction

In the process of developing the socialist market economy, it has become a major strategic decision and deployment to implement the brand strategy, pay full attention to the cultivation and protection of independent brands, and enhance the core competitiveness of enterprises. Since then, concepts such as “city brand,” “national brand,” and “cultural brand” have entered people’s thinking field, and developing cultural industries and building cultural brands have become an important topic in China’s medium and long-term cultural strategic planning [1]. In the process of development, the development of China’s cultural industry has also encountered a series of problems; first, the total amount of cultural consumption is too low; there are three main reasons for this. First, the cultural consumption capacity of China’s basic consumer groups is seriously insufficient, and the quality of cultural consumption subjects is not high, thus inhibiting the rapid growth of cultural consumption demand. Second, there is insufficient effective demand for cultural brands, and there is a lack of high-quality, truly marketable cultural brands. The third is based on the drawbacks of the “one-size-fits-all” vacation system, which seriously restricts the maximization of the benefits of recreational cultural consumption resources. The second is that the cultural brand “has a license but no market,” and the market adaptability is not strong. The third is that the market segmentation is not enough, and the homogenization competition of regional cultural brands is serious [2]. The fourth is the lack of strong policy support for the development of cultural brands. Finally, the theoretical research on cultural brand lags behind the practice. Therefore, in the modern business world where the types and numbers of cultural brands are increasing and the market competition is increasingly fierce, if you want to win consumers for your own cultural brand, you must be in the process of shaping the image of the cultural brand, fully consider the needs of consumers, especially the psychological needs of consumers, and provide consumers with more choices. Only in this way can cultural brands fully play the role of satisfying consumers’ psychological emotions. The author starts from this situation; it is proposed to analyze the related issues of cultural brand image building from the perspective of consumer psychology, in order to provide theoretical support for enterprises to build brand image, promote the effective building of Chinese cultural brands, and then promote the great development and prosperity of China’s cultural industry [3].


## 2. Literature Review

Through the research on the related principles of the consumer behavior conditioning theory, it is proposed to strengthen the brand loyalty from the perspective of the classical conditioning principle. From the principle of operant conditioning, the behavioral loyalty to the brand is strengthened, and then, the brand loyalty of consumers is formed [4]. Lototskyy et al. conducted an in-depth discussion on the mechanism of brand extension and further expounded consumers’ trust and loyalty to the brand’s original products from the aspects of learning theory, ratchet effect theory, risk theory, and demand hierarchy theory; through the low-cost transfer of brand extension to extended products, this almost recognized but lacking detailed analysis point of view has been analyzed, which has laid a theoretical foundation for the related research on brand extension [5]. Zhou et al. deeply analyzed the characteristics of consumer behavior, as shown in Figure 1, and studied the interaction and role between consumer behavior and brand image building. In addition, the research on brand image building based on consumer behavior provides new ideas for enterprises to carry out brand work from the perspective of consumer behavior, which is of strategic and tactical significance to enhance brand value [6]. The important point they made was that, in consumer perception of brands, build a long-term competitive brand by establishing effective mental differentiation and bringing consumers a pleasant effect [7]. Hu et al. segmented brand-sensitive consumers based on consumer psychology, analyzed the brand preferences of different types of consumers, and put forward marketing suggestions accordingly [8]. Lee and Ryu believe that the consumer brand relationship is a psychological contractual relationship built on the basis of mutual trust and mutual benefit between enterprises and consumers. The violation of consumers’ psychological contract will lead to the change or even rupture of the brand relationship, which will eventually lead to the generation of brand crisis [9]. Sun et al. introduce knowledge from cognitive psychology, in-depth investigation, and analysis of consumers’ internal cognition of brand choice. The author takes the construction of the consumer brand choice cognitive model as the main line, and the consumer’s cognitive structure and cognitive process as the two branches, an in-depth investigation and analysis, was carried out [10].Figure 1
The impact of brand visual communication on consumer psychology.Guided by the three-level experience theory, combined with aspect word extraction and sentiment analysis technology, the author proposes a user consumption mental portrait method based on aspect words.


## 3. Research Methods

### 3.1. Method Overview

The method can be divided into three stages:(1) Consumer psychology (relationship analysis of aspect words): first of all, we must establish the relationship between consumer psychology and the words used in comments. According to the three-level experience theory, discuss with product experts and divide a number of product features into the instinct layer, behavior layer, and reflection layer, respectively; then, aspect words are extracted from reviews, and common aspect words are associated with product feature descriptions in combination with semantics, and a mapping table of experience level, product features, and aspect words is established [11].(2) Attention analysis: on the basis of the above mapping vocabulary, the attention of product features and the attention of the experience level are obtained based on the frequency of the aspect words in the user comments being mentioned, and the users are divided into instinctive groups according to the user’s attention to the experience level; there are three types of preference, behavioral preference, and reflective preference.(3) Analysis of favorability: perform fine-grained aspect-level sentiment analysis on comments to obtain the user’s emotional inclination for the aspect, and synthesize the emotional attitude of the aspect at different experience levels to obtain the user’s favorability, and use the results of sentiment analysis to characterize different users and group’s consumer psychology portrait [12].


### 3.2. Analysis of Consumer Psychology: Aspect Word Relationship

According to the three-level experience theory, the author firstly divides the product features into instinct level, behavior level, and reflection level.Taking mobile phones as an example, the author analyzes the characteristics of products included in each level as follows.The instinctive layer represents the initial impression of the product, which refers to the information and feelings that the user can obtain immediately before or when using the mobile phone, such as appearance, price, and parameters directly visible on the online shopping platform. The behavioral layer refers to the experience of using most complex functions of the mobile phone, such as performance, camera, and battery. The reflective layer refers to the user’s perception of self-image and product image, such as brand, service, group using the phone, and abstract concepts such as domestic and technology.Refer to the domain knowledge of mobile phone products, and map the three-level experience theory to specific mobile phone product characteristics:(1)
Instinct layer: appearance, screen, sound, price, and parameters(2)
Behavior layer: performance, battery, camera, communication, and auxiliary functions(3)
Reflection layer: brand, service, user, overall feeling, and accessoriesAmong them, “parameters” refer to parameters directly visible on the online shopping platform, “auxiliary functions” refer to functions such as fingerprints and Bluetooth, “user” refers to whether the mobile phone is suitable for a certain group of people, that is, the degree of matching between the user’s self-image and the product, and “overall feeling” refers to the experience of abstract concepts such as technology, humanization, and quality. In the comments related to “accessories,” most users do not pay attention to the quality of accessories, but only pay attention to whether the merchants have given away mobile phone accessories, so this is a concept similar to “service” and should be classified into the reflection layer.Then, map the product features to its typical descriptive aspects. Considering that the current mainstream aspect word extraction algorithms are highly dependent on manual annotation, corpora, rule templates, or other forms of manual intervention and the problem of low portability of algorithm models in different fields, the author adopts the method of combining word segmentation and rules to obtain aspect words that describe product characteristics. Among them, aspect words are divided into explicit aspect words and implicit aspect words and explicit aspect words are mainly nouns, while implicit aspect words are extracted through the correspondence between adjectives and nouns.


### 3.3. Analysis of Attention

In the comments, the higher the frequency of a certain type of aspect word, the higher the user’s attention to the aspect. Therefore, attention is defined based on the frequency of appearance of aspect words.Inspecting the setP=pk|k=1,2,…K of K products, a total of J comment statements about product pk are collected; then, the comment statement set of product pk is Rk=rjk|j=1,2,…J, for a comment sentence rjk, which contains Ljk aspect words; then, the sequence formed by all the aspect words in the comment sentence rjk is Wjk=wjkl|l=1,2,…Ljk.For an aspect wordwjkl, the instinct-level attention iijkl, the behavior-level attention ibjkl, and the reflection-level attention irjkl of the aspect word can be given, as shown in the following formulas (1)–(3):(1)iijkl=1wjkl∈instinct layer0wjkl∉instinct layer,(2)ibjkl=1wjkl∈behavior layer0wjkl∉behavior layer,(3)irjkl=1wjkl∈reflection layer0wjkl∉reflection layer.From this, the attention degreeIi of the user who gives the comment rjk to the instinct layer design is obtained, as shown in the following formula:(4)Ii=1Ljk∑l=1Ljkiijkl.In the same way, the users who give commentrjk attention to the behavior layer and the design of the reflection layer Ib and Ir can be obtained, as shown in the following formulas:(5)Ib=1Ljk∑l=1Ljkibjkl,(6)Ir=1Ljk∑l=1Ljkirjkl.According to the above calculation results, when the value ofIi is higher than the other two, it is considered that the user pays more attention to the design of the product instinct layer and belongs to the user with instinct preference; in the same way, when Ib or Ir is higher than the other two, it is considered that users pay more attention to the design of the product behavior layer or reflection layer, which belongs to users with behavior preference or reflection preference [13].


### 3.4. Likelihood Analysis

The user’s preference for a specific product is constituted by his emotional attitude towards various aspects of the product, so the user’s preference for the product evaluated by the user is calculated by using the user’s emotion for different aspects. In the sentiment analysis task of aspect words, the author adopts the DFAOA-BERT model [14]. The model combines the AOA (attention-over-attention) mechanism with the BERT pre-training model, comprehensively considers the correlation between aspect word information and context information, and combines global semantic features and local semantic features; judgmental aspect vocabulary expresses the sentiment in the comment sentence.For the aspect word sequenceWjk in a comment statement rjk of product pk, the meaning of the context information r¯jkl corresponding to any aspect word wjkl is the remaining content of the comment sentence rjk after removing the aspect word wjkl [15, 16]. Input the aspect word wjkl and the context information r¯jkl into the network at the same time to obtain the emotional tendency sjkl of the aspect word in the comment sentence rjk; the values of sjkl are 0, 1, and 2, which represent negative, neutral, and positive emotions, respectively.Then, user j’s preferenceEjk for product pk is expressed as the following formula:(7)Ejk=1Ljk∑l=1LjkSjkl.Usingcjk to represent the user who gave the comment rjk, from all the comments of the product pk, the preference Eik for the product pk from the instinctual preference user group Cik who purchased the product pk can be comprehensively extracted, as shown in the following formula:(8)Eik=1Cik∑j=1CikEjkcjk∈Cik.Similarly, the favorabilityEbk of the behavioral preference user group Cbk for the product pk, and the favorability Erk of the reflection preference user group Crk for the product pk can be obtained, as shown in the following formulas:(9)Ebk=1Cbk∑j=1CbkEjkcjk∈Cbk,(10)Erk=1Crk∑j=1CrkEjkcjk∈Crk.


## 3.1. Method Overview

The method can be divided into three stages:(1) Consumer psychology (relationship analysis of aspect words): first of all, we must establish the relationship between consumer psychology and the words used in comments. According to the three-level experience theory, discuss with product experts and divide a number of product features into the instinct layer, behavior layer, and reflection layer, respectively; then, aspect words are extracted from reviews, and common aspect words are associated with product feature descriptions in combination with semantics, and a mapping table of experience level, product features, and aspect words is established [11].(2) Attention analysis: on the basis of the above mapping vocabulary, the attention of product features and the attention of the experience level are obtained based on the frequency of the aspect words in the user comments being mentioned, and the users are divided into instinctive groups according to the user’s attention to the experience level; there are three types of preference, behavioral preference, and reflective preference.(3) Analysis of favorability: perform fine-grained aspect-level sentiment analysis on comments to obtain the user’s emotional inclination for the aspect, and synthesize the emotional attitude of the aspect at different experience levels to obtain the user’s favorability, and use the results of sentiment analysis to characterize different users and group’s consumer psychology portrait [12].


## 3.2. Analysis of Consumer Psychology: Aspect Word Relationship

According to the three-level experience theory, the author firstly divides the product features into instinct level, behavior level, and reflection level.Taking mobile phones as an example, the author analyzes the characteristics of products included in each level as follows.The instinctive layer represents the initial impression of the product, which refers to the information and feelings that the user can obtain immediately before or when using the mobile phone, such as appearance, price, and parameters directly visible on the online shopping platform. The behavioral layer refers to the experience of using most complex functions of the mobile phone, such as performance, camera, and battery. The reflective layer refers to the user’s perception of self-image and product image, such as brand, service, group using the phone, and abstract concepts such as domestic and technology.Refer to the domain knowledge of mobile phone products, and map the three-level experience theory to specific mobile phone product characteristics:(1)
Instinct layer: appearance, screen, sound, price, and parameters(2)
Behavior layer: performance, battery, camera, communication, and auxiliary functions(3)
Reflection layer: brand, service, user, overall feeling, and accessoriesAmong them, “parameters” refer to parameters directly visible on the online shopping platform, “auxiliary functions” refer to functions such as fingerprints and Bluetooth, “user” refers to whether the mobile phone is suitable for a certain group of people, that is, the degree of matching between the user’s self-image and the product, and “overall feeling” refers to the experience of abstract concepts such as technology, humanization, and quality. In the comments related to “accessories,” most users do not pay attention to the quality of accessories, but only pay attention to whether the merchants have given away mobile phone accessories, so this is a concept similar to “service” and should be classified into the reflection layer.Then, map the product features to its typical descriptive aspects. Considering that the current mainstream aspect word extraction algorithms are highly dependent on manual annotation, corpora, rule templates, or other forms of manual intervention and the problem of low portability of algorithm models in different fields, the author adopts the method of combining word segmentation and rules to obtain aspect words that describe product characteristics. Among them, aspect words are divided into explicit aspect words and implicit aspect words and explicit aspect words are mainly nouns, while implicit aspect words are extracted through the correspondence between adjectives and nouns.


## 3.3. Analysis of Attention

In the comments, the higher the frequency of a certain type of aspect word, the higher the user’s attention to the aspect. Therefore, attention is defined based on the frequency of appearance of aspect words.Inspecting the setP=pk|k=1,2,…K of K products, a total of J comment statements about product pk are collected; then, the comment statement set of product pk is Rk=rjk|j=1,2,…J, for a comment sentence rjk, which contains Ljk aspect words; then, the sequence formed by all the aspect words in the comment sentence rjk is Wjk=wjkl|l=1,2,…Ljk.For an aspect wordwjkl, the instinct-level attention iijkl, the behavior-level attention ibjkl, and the reflection-level attention irjkl of the aspect word can be given, as shown in the following formulas (1)–(3):(1)iijkl=1wjkl∈instinct layer0wjkl∉instinct layer,(2)ibjkl=1wjkl∈behavior layer0wjkl∉behavior layer,(3)irjkl=1wjkl∈reflection layer0wjkl∉reflection layer.From this, the attention degreeIi of the user who gives the comment rjk to the instinct layer design is obtained, as shown in the following formula:(4)Ii=1Ljk∑l=1Ljkiijkl.In the same way, the users who give commentrjk attention to the behavior layer and the design of the reflection layer Ib and Ir can be obtained, as shown in the following formulas:(5)Ib=1Ljk∑l=1Ljkibjkl,(6)Ir=1Ljk∑l=1Ljkirjkl.According to the above calculation results, when the value ofIi is higher than the other two, it is considered that the user pays more attention to the design of the product instinct layer and belongs to the user with instinct preference; in the same way, when Ib or Ir is higher than the other two, it is considered that users pay more attention to the design of the product behavior layer or reflection layer, which belongs to users with behavior preference or reflection preference [13].


## 3.4. Likelihood Analysis

The user’s preference for a specific product is constituted by his emotional attitude towards various aspects of the product, so the user’s preference for the product evaluated by the user is calculated by using the user’s emotion for different aspects. In the sentiment analysis task of aspect words, the author adopts the DFAOA-BERT model [14]. The model combines the AOA (attention-over-attention) mechanism with the BERT pre-training model, comprehensively considers the correlation between aspect word information and context information, and combines global semantic features and local semantic features; judgmental aspect vocabulary expresses the sentiment in the comment sentence.For the aspect word sequenceWjk in a comment statement rjk of product pk, the meaning of the context information r¯jkl corresponding to any aspect word wjkl is the remaining content of the comment sentence rjk after removing the aspect word wjkl [15, 16]. Input the aspect word wjkl and the context information r¯jkl into the network at the same time to obtain the emotional tendency sjkl of the aspect word in the comment sentence rjk; the values of sjkl are 0, 1, and 2, which represent negative, neutral, and positive emotions, respectively.Then, user j’s preferenceEjk for product pk is expressed as the following formula:(7)Ejk=1Ljk∑l=1LjkSjkl.Usingcjk to represent the user who gave the comment rjk, from all the comments of the product pk, the preference Eik for the product pk from the instinctual preference user group Cik who purchased the product pk can be comprehensively extracted, as shown in the following formula:(8)Eik=1Cik∑j=1CikEjkcjk∈Cik.Similarly, the favorabilityEbk of the behavioral preference user group Cbk for the product pk, and the favorability Erk of the reflection preference user group Crk for the product pk can be obtained, as shown in the following formulas:(9)Ebk=1Cbk∑j=1CbkEjkcjk∈Cbk,(10)Erk=1Crk∑j=1CrkEjkcjk∈Crk.


## 4. Analysis of Results

### 4.1. Data Collection and Mapping Table Construction

The author conducts experimental analysis based onJD.com mobile phone review data. Data crawlers are used to crawl relevant parameters and user reviews of mobile phones on sale from the Jingdong website, screen more than 3,000 mobile phones, and exclude products with unclear basic information such as product names or store names, reservations, and second-hand products, and each product is required to contain at least 5 positive reviews, 5 positive reviews, and 5 negative reviews and finally got 904,232 reviews for a total of 1,171 mobile phone products; among them, 677,266 were positive, 73,443 were moderate, and 153,523 were negative. Since the website limits each mobile phone to display at most 1000 positive comments, 1000 moderate comments, and 1000 negative comments, the number of comments obtained for each mobile phone is between 32 and 3000 [17].Use the stuttering word segmentation tool to segment the review text, and select the aspect words related to the product features from the words with a word frequency greater than 5/10,000, correct or complete words with inaccurate segmentation results, (for example, change “comprehensive” to “full screen”), add some words related to existing attribute words according to common sense (for example, add “double” to “price.” 11), and then identify explicit and implicit aspect words respectively [18]. The meaning of the aspect words established on the JD mobile phone dataset is shown in Table 1.Table 1
Experience level, product features, andaspect word mapping table established according to the three-level experience theory (example).
Three-level experience theoryInterpretation in the context of mobile phone designProduct featuresAspect word (example)Number of explicit wordsNumber of implicit wordsInstinct layerInformation and feelings that can be obtained before using a mobile phone or when using it lightlyExteriorColor, border, and surface5327ScreenDisplay, quality, and clarity293SoundSound quality, volume, and noise190PricePrice2114ParameterGB, memory, and chip220Behavioral layerThe experience of using most functions of the mobile phonePerformanceBlack screen, game, and speed269BatteryBattery, power, and battery life200PhotographPixel, night scene, and beauty260CommunicationInternet speed, wireless, and signal310AccessibilityFace recognition, fingerprint, and Bluetooth413Reflection layerUser perception of self-image and product imageBrandHuawei, Xiaomi, and Apple300ServeStore, logistics, and after-sales511UserElderly, children, and girls340Overall feelingTechnology, domestic production, and humanization2631AccessoriesHeadphones, data cables, and bracelets180


### 4.2. Analysis of Consumer Psychology Preference Based on Attention

Based on the attention of comments, analyze the consumer psychology preferences of users. According to formulas (4)–(6), the instinct level attention, behavior level attention, and reflective level attention of each comment are calculated, respectively, and users are classified into three categories: instinctive preference, behavioral preference, or reflective preference [19].Taking the overall mobile phone review data collected by theJD.com dataset as an example, the distribution of three types of users in the mobile phone purchase market can be obtained as shown in Figure 2.Figure 2
Distribution of three-tier user groups in the mobile phone market.It can be seen that, in the mobile phone purchase market, most users are instinctive preference users; that is, they pay attention to intuitive features such as the appearance and price of mobile phones. Behavioral preference users, that is, the number of users who pay attention to the experience of using various practical functions of mobile phones, are slightly smaller. The least number are reflective preference users.The top nine most frequently mentioned aspect words in the process of extracting each type of user comments are shown in Table2; from top to bottom, the mentioned frequency of aspect words decreases in turn, among the high-frequency aspect words mentioned by instinctual preference users; aspect words that belong to the instinctive layer are marked in black font; the same is true for the other two types of users.Table 2
Three-level user groups comment on high-frequency words.
Instinct preference userBehavioral preference userReflect on preference usersScreenSpeedJD.comAppearancePhotographElderSpeedStandby timeCustomer servicePhotographScreenLogisticsSound effectAppearanceQualityStandby timeSound effectHuaweiExteriorCardSpeedSoundChargeExpress deliveryFeelBatteryEarphoneJudging from the five specific features at each level, the instinctive preference users focus more on aspects that are more closely related to the actual hand experience, such as the appearance, screen, and sound of the instinctual layer, for features such as price and parameters that have little impact on the user experience, the attention is not as much as appearance, screen, and sound. It is worth noting that the instinctive preference users pay a high degree of attention to the words “speed,” “photographing,” and “standby time” in the behavioral layer.Behavioral preference users focus more on performance, battery, and photography; for more traditional communication functions and more attention to auxiliary functions, it is not as good as performance, battery, photography, and other three aspects. For the three instinctive aspects of “screen,” “appearance,” and “sound effects,” behavior preference users pay a high degree of attention.Reflection preference: the user’s concerns comprehensively cover all five characteristics of the reflection layer.


### 4.3. User Analysis Based on Likeness

The DFAOA-BERT model is used to analyze the favorability of the aspect words in the comment sentences, so as to obtain the favorability of different types of users for their mobile phone purchases, and based on this, we analyze the consumption preferences of different types of users on brands and prices [20].Taking eight mobile phone brands with a large number of users, such as Apple and Xiaomi, as examples, the user preference results of different preferences are shown in Table3.Table 3
The three-level user groups’ ratings of their preference for different brands of mobile phones.
BrandFavoriteInstinct preference userBehavioral preference userReflect on preference usersApple1.5703631.3146071.326808Millet1.6142271.3172041.276268Red rice1.6150181.2840131.325327Huawei1.6678171.4930531.420238Glory1.6195611.4247371.35648Oppo1.7565831.4911.544588Vivo1.7115091.4889051.413475Philips1.5569181.1986611.596631It can be obtained from Table3; among the top seven types of mobile phone brands, instinctive preference users have higher preference for each brand of mobile phone than the other two types of users, while the other two types of users have little difference in their preference for the same brand.Instinct preference users have the highest preference for Oppo, and behavior preference users have the highest preference for Huawei. Therefore, we can think that, among these eight brands, Oppo and Huawei are the most suitable for user needs in the design of the instinct layer and the behavior layer, respectively. Reflection preference users have the highest preference for Philips because most users believe that Philips’ design more accurately meets the needs of the target user group of the elderly, so its reflection layer design is optimal [21].Then, according to the price distribution characteristics of mobile phones we obtained, the mobile phones are divided into the following five price levels, and the preference of each type of users for these five price mobile phones is compared; the results are shown in Table4.Table 4
Ratings of three-level user groups’ preference for mobile phones at different price points.
PriceFavoriteInstinctive preferenceUser behavior preferencesUser reflection preferenceBelow 1k1.6251371.2128331.5453951-2k1.6676691.4272431.4104212-3k1.6424091.4310431.3461383–5k1.6037841.3876161.304926Above 5k1.6144961.4041151.323678It can be seen from the table that users who prefer the instinctive and behavioral layers prefer midpriced phones, so we believe that the instinct-layer and behavior-layer designs of midpriced mobile phones are more able to meet users’ expectations based on their price [22]. However, overall, instinct preference users are basically similar in their liking for each price point; while behavioral preference users have significantly lower evaluations of mobile phones below 1,000 yuan than other price points, which shows that the functions of mobile phones below 1,000 yuan are less and weaker; it cannot meet the needs of these users. In retrospect, the most popular mobile phone is the mobile phone below 1,000 yuan, which is related to the generally lower price of the old phone. At the same time, this score is significantly different from that of this type of users for higher-priced phones; it also shows that other price-point phones need to improve their own reflection layer design [23].Judging from the preference of different types of users for mobile phones of the same price, at all price points, the instinctive preference users’ preference for mobile phones at this price is higher than that of the other two types of users. At the four price points of more than 1,000 yuan, behavior preference users are more fond of mobile phones at this price than reflection preference users. Only in the mobile phones below 1,000 yuan, the preference of users with reflection preference is higher than that of users with behavior preference. Therefore, we believe that most mobile phones will pay more attention to the design of the instinct layer under the constraints of their pricing, followed by the design of the behavior layer; considering that users with instinct preferences account for the highest proportion in the market, the design bias is indeed more in line with the market status [24].Furthermore, taking Xiaomi Mi 10 (the product id on theJD.com shopping website is 100011351676, 1095 instinctively prefer users, 1311 behavioral preference users, and 490 reflection preference users) as an example, the 15 features of the mobile phone are analyzed for different types of users, as shown in Table 5.Table 5
Ratings of the three-level user groups’ favorability of various features of Xiaomi Mi 10.
FeatureFavoriteInstinctive preferenceUser behavior preferencesUser reflection preferencesExterior1.3124351.165321.041837Screen1.2976341.165611.005272Sound1.4109631.1986911.065646Price0.9558671.0076280.972449Parameter1.1566251.0480551.018367Performance1.3387060.9000321.029082Battery1.1824511.0150841.011395Photograph1.2493891.0305031.012517Communication0.986910.8137090.959184Accessibility1.0633180.9539140.99966Overall feeling1.1125571.045080.923129Brand1.1687741.0486950.960002Serve0.9419930.9286490.584262Accessories0.9627310.9797230.709796User1.0191781.0022880.986735It can be seen from Table5 that the three types of users have the highest preference for the feature of the phone’s sound, so we believe that the phone’s sound design is the best among the fifteen features.For users with instinctive preference and users with reflective preference, the lowest preference is service, while for behavioral preference users, the lowest preference is communication. We believe that if the Xiaomi Mi 10 mobile phone wants to attract the most instinctive preference users in the market or the reflective preference users who buy this mobile phone the least, it should improve its service level, including customer service level, store and platform cooperation, delivery logistics, and after-sales service quality. If you want to consolidate the base of the number of users who currently buy the most behavioral preference for this mobile phone, you should improve your communication quality, including mobile phone communication and network signals [25].


## 4.1. Data Collection and Mapping Table Construction

The author conducts experimental analysis based onJD.com mobile phone review data. Data crawlers are used to crawl relevant parameters and user reviews of mobile phones on sale from the Jingdong website, screen more than 3,000 mobile phones, and exclude products with unclear basic information such as product names or store names, reservations, and second-hand products, and each product is required to contain at least 5 positive reviews, 5 positive reviews, and 5 negative reviews and finally got 904,232 reviews for a total of 1,171 mobile phone products; among them, 677,266 were positive, 73,443 were moderate, and 153,523 were negative. Since the website limits each mobile phone to display at most 1000 positive comments, 1000 moderate comments, and 1000 negative comments, the number of comments obtained for each mobile phone is between 32 and 3000 [17].Use the stuttering word segmentation tool to segment the review text, and select the aspect words related to the product features from the words with a word frequency greater than 5/10,000, correct or complete words with inaccurate segmentation results, (for example, change “comprehensive” to “full screen”), add some words related to existing attribute words according to common sense (for example, add “double” to “price.” 11), and then identify explicit and implicit aspect words respectively [18]. The meaning of the aspect words established on the JD mobile phone dataset is shown in Table 1.Table 1
Experience level, product features, andaspect word mapping table established according to the three-level experience theory (example).
Three-level experience theoryInterpretation in the context of mobile phone designProduct featuresAspect word (example)Number of explicit wordsNumber of implicit wordsInstinct layerInformation and feelings that can be obtained before using a mobile phone or when using it lightlyExteriorColor, border, and surface5327ScreenDisplay, quality, and clarity293SoundSound quality, volume, and noise190PricePrice2114ParameterGB, memory, and chip220Behavioral layerThe experience of using most functions of the mobile phonePerformanceBlack screen, game, and speed269BatteryBattery, power, and battery life200PhotographPixel, night scene, and beauty260CommunicationInternet speed, wireless, and signal310AccessibilityFace recognition, fingerprint, and Bluetooth413Reflection layerUser perception of self-image and product imageBrandHuawei, Xiaomi, and Apple300ServeStore, logistics, and after-sales511UserElderly, children, and girls340Overall feelingTechnology, domestic production, and humanization2631AccessoriesHeadphones, data cables, and bracelets180


## 4.2. Analysis of Consumer Psychology Preference Based on Attention

Based on the attention of comments, analyze the consumer psychology preferences of users. According to formulas (4)–(6), the instinct level attention, behavior level attention, and reflective level attention of each comment are calculated, respectively, and users are classified into three categories: instinctive preference, behavioral preference, or reflective preference [19].Taking the overall mobile phone review data collected by theJD.com dataset as an example, the distribution of three types of users in the mobile phone purchase market can be obtained as shown in Figure 2.Figure 2
Distribution of three-tier user groups in the mobile phone market.It can be seen that, in the mobile phone purchase market, most users are instinctive preference users; that is, they pay attention to intuitive features such as the appearance and price of mobile phones. Behavioral preference users, that is, the number of users who pay attention to the experience of using various practical functions of mobile phones, are slightly smaller. The least number are reflective preference users.The top nine most frequently mentioned aspect words in the process of extracting each type of user comments are shown in Table2; from top to bottom, the mentioned frequency of aspect words decreases in turn, among the high-frequency aspect words mentioned by instinctual preference users; aspect words that belong to the instinctive layer are marked in black font; the same is true for the other two types of users.Table 2
Three-level user groups comment on high-frequency words.
Instinct preference userBehavioral preference userReflect on preference usersScreenSpeedJD.comAppearancePhotographElderSpeedStandby timeCustomer servicePhotographScreenLogisticsSound effectAppearanceQualityStandby timeSound effectHuaweiExteriorCardSpeedSoundChargeExpress deliveryFeelBatteryEarphoneJudging from the five specific features at each level, the instinctive preference users focus more on aspects that are more closely related to the actual hand experience, such as the appearance, screen, and sound of the instinctual layer, for features such as price and parameters that have little impact on the user experience, the attention is not as much as appearance, screen, and sound. It is worth noting that the instinctive preference users pay a high degree of attention to the words “speed,” “photographing,” and “standby time” in the behavioral layer.Behavioral preference users focus more on performance, battery, and photography; for more traditional communication functions and more attention to auxiliary functions, it is not as good as performance, battery, photography, and other three aspects. For the three instinctive aspects of “screen,” “appearance,” and “sound effects,” behavior preference users pay a high degree of attention.Reflection preference: the user’s concerns comprehensively cover all five characteristics of the reflection layer.


## 4.3. User Analysis Based on Likeness

The DFAOA-BERT model is used to analyze the favorability of the aspect words in the comment sentences, so as to obtain the favorability of different types of users for their mobile phone purchases, and based on this, we analyze the consumption preferences of different types of users on brands and prices [20].Taking eight mobile phone brands with a large number of users, such as Apple and Xiaomi, as examples, the user preference results of different preferences are shown in Table3.Table 3
The three-level user groups’ ratings of their preference for different brands of mobile phones.
BrandFavoriteInstinct preference userBehavioral preference userReflect on preference usersApple1.5703631.3146071.326808Millet1.6142271.3172041.276268Red rice1.6150181.2840131.325327Huawei1.6678171.4930531.420238Glory1.6195611.4247371.35648Oppo1.7565831.4911.544588Vivo1.7115091.4889051.413475Philips1.5569181.1986611.596631It can be obtained from Table3; among the top seven types of mobile phone brands, instinctive preference users have higher preference for each brand of mobile phone than the other two types of users, while the other two types of users have little difference in their preference for the same brand.Instinct preference users have the highest preference for Oppo, and behavior preference users have the highest preference for Huawei. Therefore, we can think that, among these eight brands, Oppo and Huawei are the most suitable for user needs in the design of the instinct layer and the behavior layer, respectively. Reflection preference users have the highest preference for Philips because most users believe that Philips’ design more accurately meets the needs of the target user group of the elderly, so its reflection layer design is optimal [21].Then, according to the price distribution characteristics of mobile phones we obtained, the mobile phones are divided into the following five price levels, and the preference of each type of users for these five price mobile phones is compared; the results are shown in Table4.Table 4
Ratings of three-level user groups’ preference for mobile phones at different price points.
PriceFavoriteInstinctive preferenceUser behavior preferencesUser reflection preferenceBelow 1k1.6251371.2128331.5453951-2k1.6676691.4272431.4104212-3k1.6424091.4310431.3461383–5k1.6037841.3876161.304926Above 5k1.6144961.4041151.323678It can be seen from the table that users who prefer the instinctive and behavioral layers prefer midpriced phones, so we believe that the instinct-layer and behavior-layer designs of midpriced mobile phones are more able to meet users’ expectations based on their price [22]. However, overall, instinct preference users are basically similar in their liking for each price point; while behavioral preference users have significantly lower evaluations of mobile phones below 1,000 yuan than other price points, which shows that the functions of mobile phones below 1,000 yuan are less and weaker; it cannot meet the needs of these users. In retrospect, the most popular mobile phone is the mobile phone below 1,000 yuan, which is related to the generally lower price of the old phone. At the same time, this score is significantly different from that of this type of users for higher-priced phones; it also shows that other price-point phones need to improve their own reflection layer design [23].Judging from the preference of different types of users for mobile phones of the same price, at all price points, the instinctive preference users’ preference for mobile phones at this price is higher than that of the other two types of users. At the four price points of more than 1,000 yuan, behavior preference users are more fond of mobile phones at this price than reflection preference users. Only in the mobile phones below 1,000 yuan, the preference of users with reflection preference is higher than that of users with behavior preference. Therefore, we believe that most mobile phones will pay more attention to the design of the instinct layer under the constraints of their pricing, followed by the design of the behavior layer; considering that users with instinct preferences account for the highest proportion in the market, the design bias is indeed more in line with the market status [24].Furthermore, taking Xiaomi Mi 10 (the product id on theJD.com shopping website is 100011351676, 1095 instinctively prefer users, 1311 behavioral preference users, and 490 reflection preference users) as an example, the 15 features of the mobile phone are analyzed for different types of users, as shown in Table 5.Table 5
Ratings of the three-level user groups’ favorability of various features of Xiaomi Mi 10.
FeatureFavoriteInstinctive preferenceUser behavior preferencesUser reflection preferencesExterior1.3124351.165321.041837Screen1.2976341.165611.005272Sound1.4109631.1986911.065646Price0.9558671.0076280.972449Parameter1.1566251.0480551.018367Performance1.3387060.9000321.029082Battery1.1824511.0150841.011395Photograph1.2493891.0305031.012517Communication0.986910.8137090.959184Accessibility1.0633180.9539140.99966Overall feeling1.1125571.045080.923129Brand1.1687741.0486950.960002Serve0.9419930.9286490.584262Accessories0.9627310.9797230.709796User1.0191781.0022880.986735It can be seen from Table5 that the three types of users have the highest preference for the feature of the phone’s sound, so we believe that the phone’s sound design is the best among the fifteen features.For users with instinctive preference and users with reflective preference, the lowest preference is service, while for behavioral preference users, the lowest preference is communication. We believe that if the Xiaomi Mi 10 mobile phone wants to attract the most instinctive preference users in the market or the reflective preference users who buy this mobile phone the least, it should improve its service level, including customer service level, store and platform cooperation, delivery logistics, and after-sales service quality. If you want to consolidate the base of the number of users who currently buy the most behavioral preference for this mobile phone, you should improve your communication quality, including mobile phone communication and network signals [25].


## 5. Conclusion

According to the three-level experience theory, the author analyzes the consumer psychology of users and proposes a new user portrait analysis and calculation method. First, we constructed the mapping relationship between the experience level and the aspect words in the comment statement, corresponding the aspect words in the product reviews to the product features and then corresponding the fifteen product features to the three levels, thus establishing the experience level-product feature: aspect word mapping table. Based on this vocabulary, we can use aspect word extraction technology and deep learning-based aspect word sentiment analysis technology, obtain the attention and love of various types of users on different aspects and experience levels of the product, divide users into three types, instinctive preference, behavioral preference, and reflective preference, and further obtain the consumption characteristics of users with different preferences. From the merchant’s point of view, when you understand the distribution characteristics and reasons of your product’s user group, as well as various users’ preferences for the product, you can combine the merchant’s development goals to judge the direction of product improvement. Although the study only takes the mobile phone as an example for application analysis, as long as the product information and review content are combined, the product features are reasonably classified into three levels, and the method of the study is also applicable to the consumer psychology analysis of other product users. Mobile phones are search-type commodities in Nelson’s product category; in the future, other commodities, such as experience-type products, can be selected for user portraits to further verify the universality and effectiveness of the author’s method. In addition, there is still room for improvement in the construction of the experience level-product feature: aspect word mapping table. At present, we only take the mobile phone review dataset as an example. In the future, we can build a vocabulary across multiproduct and multiplatform review datasets, in order to conduct a more comprehensive analysis of user consumption characteristics. The current vocabulary is generated based on manual rules. In the future, a machine learning method for automatically generating vocabulary can be developed based on this, so as to improve the versatility and ease of use of user portraits based on the three-level experience theory in different fields.



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*Source: 9599943-2022-09-29.xml*