Product Approximate Reasoning of Online Reviews Applying to Consumer Affective and Psychological Motives Research

Transcription

1 Appled Mathematcs & Informaton Scences An Internatonal Journal 2011 NSP 5 (2) (2011), 45S-51S Product Approxmate Reasonng of Onlne Revews Applyng to Consumer Affectve and Psychologcal Motves Research Narsa Zhao 1 and Yng Lu System Engneerng Insttute, Dalan Unversty of Technology, Dalan , Chna Emal Address: 1 nmgnrs@dlut.edu.cn; 2 luyngdlut@yahoo.cn Receved June 18, 2010; Revsed January 18, 2011 Consumer psychology and sentment analyss become more and more flourshng. Ths paper presents fuzzy nference rules wth comprehensve evaluaton and emoton as antecedents and accomplshes reasonng recommendaton to consumers wth four dfferent motvatons. A fuzzy model for the evaluaton and emoton of onlne revew texts s proposed usng the theory of consumpton motvaton type. It has an nbult fuzzy corpus of consumer evaluaton and emoton and has gven a calculatonal method of comprehensve evaluaton and emoton combned wth the consumer s preference for product attrbutes. 2 Keywords: Consumer behavor, Consumpton motvaton, Atttude mnng, Approxmate reasonng. 1 Introducton Many consumers prefer to use free form of text to express ther opnons, atttudes and emotons n revew forums, dscusson groups and vrtual communty logs n the work of [1]. There s growng evdence that such forums could nfluence consumers purchase decsons accordng to [2]. Effectvely collectng and analyzng ths nformaton can be valuable to e-busness managers and analysts. Mnng and analyzng these onlne revews, especally ther sentment, can greatly help better to understand the users consumng habts and publc opnons whch play an mportant role n decson-makng for enterprses and the government. For Englsh texts the work of [3] provded standard classfcaton of corpus such as Reuters and statstcal evaluaton methods. The work of [4] llustrated a sentment analyss approach to extract sentments assocated wth polartes of postve or negatve for specfc subjects from a document. Ths paper focuses on the subsecton of polarty ntensty wth fuzzy method. Instead of numercal ntenstes we use a contnuous functon to represent the strength of the level of the effect. We propose a fuzzy model for onlne revew texts. Furthermore, usng the theory of consumpton motvaton, ths paper presents approxmate reasonng rules wth

2 Narsa Zhao and Yng Lu 46 comprehensve evaluaton and emoton as antecedents. Consequently t accomplshes reasonng recommendaton to consumers wth four dfferent motvatons. 2 Problem Statement and Prelmnares Most exstng systems of recommendng use collaboratve flterng methods, contentbased methods or hybrd flterng methods that combne both technques. However, the wdely used free forms of texts whch are especally onlne revews of products are great resources of consumer opnon and sentment. More detal can be seen n [5-6]. The applcaton of Fuzzy Mathematcs s able not only to solve the problem of language uncertanty but also to mprove the method of quantfcaton by usng a contnuous functon to represent the meanng and emoton ntensty of words. It s mperatve to defne the varables, fuzzy membershp functon and fuzzy sets Semantc Fuzzfcaton Methodology Tradtonally, regardng to the quantfcaton of word meanng, t comes to the common pont that all researches consder the word meanng as accurate (Ma Mou-chao, 1994). For usng a fuzzy method to process evaluaton and emoton, the measurement of polarty words can be dvded nto four ranks separately on postve and negatve category: small (S), mddle (M), large (L) and very large (VL). Each rank corresponds to a fuzzy membershp functon, namely -VL,-L,-M,-S, Z, +S, +M, +L, +VL whch we together call basc evaluaton fuzzy set. Evaluaton of apprase s denoted as G (Good), B (Bad) and the degree of emoton s represented as H (Hgh), L (Low). For the sake of smplcty these basc evaluaton fuzzy sets may generally be regarded as convex fuzzy sets. We choose the Gaussan functon as a template to defne fuzzy membershp functons for 9 semantc ranks n doman [-4, 4]. Eq.1 s the template of the Fuzzy Membershp Functon. 2 ( x c ) k y = gaussmfk( x, σ k, ck) = exp 2 (1) 2σ k where k { VL, L, M, S, Z, + S, + M, + L, + VL}, σ k, ck are parameters of the Gaussan membershp functon correspondng to the sentmental rankk. When x=c k, y=1, namely, the value of membershp functon s 1 at the functon center pont, thenσ k = 0.4. From V to Z, x [ 4, 0], C -VL =-4, C -L =-3, C -M =-2, C -S =-1, C Z =0. Conversely a negatve sgn s dfference.

3 Product Approxmate Reasonng of Onlne Revews Establshment of Consumer Psychologcal Fuzzy Corpus Bases. Table 1. Evaluaton and Emoton Fuzzy Corpus of Onlne Revews Ontology Evaluaton Emoton words degree words degree Montor, chasss, unque VLG happy LH cable, touch screen, deal LG enjoyable MH processor, appearance, comfortable MG fortunately SH hard dsk, prce, good SG hestate Z weght, after sale, fan, ordnary Z fant SL repar rate expensve SB gloomy ML On the above bass a semantc fuzzy corpus s establshed. Products and ther attrbutes are objectve ontology upon whch consumers comment. We take a notebook computer as an example to gve the base of ontology. We buld a fuzzy corpus of evaluaton and emoton accordng to the establshed methods of the membershp functon ntroduced n Secton 1. The fuzzy corpus s then stored by a two-dmensonal table, for the case of fuzzy evaluaton, the words are n the frst column and the bass evaluaton fuzzy set are stored n the second ones, as shown n Table 1. 3 Fuzzy Calculaton of Product Evaluaton and Emoton The emoton value s obtaned through the comprehensve calculaton of massvely emotonal words. Also we take customer preferences of certan features nto account to calculate evaluaton of products wth mult-attrbutes. Thus the fuzzy calculaton steps of evaluaton and emoton go as follows: Step 1. Frstly conduct part-of-speech taggng and syntactc analyss and then get n evaluaton words, EF ( = 1,..., n), m emotonal words, EM ( = 1,..., m), and ther correspondng ntensve or negatve modfers. Step 2. Regardng evaluaton and emoton words, EF ( = 1,..., n), EM ( = 1,..., m), query the correspondng basc evaluaton fuzzy set µ ( EF )( = 1,..., n) and µ ( EM )( = 1,..., m) from the evaluaton and emoton fuzzy corpus. Step 3. Accordng to the ntensve and negatve processng approach accomplsh shft the wdth and center of ts membershp functon, and get modfed evaluaton fuzzy set, µ ( EF )( = 1,..., n), and modfed emoton fuzzy set, µ ( EM )( = 1,..., m). Step 4. Execute the and operaton to µ ( EF )( = 1,..., n) and µ ( EM )( = 1,..., m), respectvely, and then get the evaluaton of the certan attrbute and the total emoton fuzzy set EF, EM. Step 5. Defuzzfy EF, EM separately by means of centrod, afterwards obtan the attrbute evaluaton value and the overall emoton value of the product.

4 Narsa Zhao and Yng Lu 48 Step 6. A fnal score of the product based on the valuaton of each feature s Overall Assessment that s calculated as the weghted sum of several attrbutes. OA = EF ImportanceIndex (2) We compled a program n Matlab n accordance wth the rules above, whereby we can calculate the evaluaton and emoton degree of a product. 4 Recommendaton Methodology In order to acheve the product recommendaton, based on the theory of consumpton motvaton, we calculate and reason by way of product evaluaton and emoton values and then we generate the extent of recommendaton effect of product revewsrecommendaton degrees. That requres settng up reasonng rule bases representng knowledge Fuzzy reasonng rule bases In varous methods of Knowledge Representaton the commonest way s the form of natural language rules: IF premse (antecedent), THEN concluson (consequent). We own evaluaton (EF) and emoton (EM) as antecedent and recommendaton (R) as consequent to establsh fuzzy reasonng system (FIS). The form s as follows: IF antecedent 1 (Customer Type) and antecedent (EF) and antecedent 3(EM), THEN consequent (R)(5). There are several ways to classfy users such as lfestyle theory, demographc flterng, shoppng motve and so on. Nevertheless the lfestyle theory s dffcult to explan that the values nfluence on a user s purchase behavor n [7]. Demographc flterng does not provde any ndvdual adaptaton partcularly as the user s nterests tend to change over tme referrng to [8] and reference n [9] provded that the shoppng motve can be defned as the drver of behavor that brngs consumers to the marketplace to satsfy ther nternal needs. In the work of [7] the shoppng motve was classfed nto four shoppng types as shown n Fg 4.1. Utltaran Motve hgh utlty/low pleasure (man-centrc utltarans) low utlty/low pleasure (woman-centrc begnnng netzens) hgh utlty/hgh pleasure (profssonalst-centrc profcent netzens) hgh utlty/low pleasure (collegan-centr hedonsts) Hedonc Motve Fg 4.1: User Classfcaton by Shoppng Motve

5 Product Approxmate Reasonng of Onlne Revews 49 The low-utlty/low-pleasure type ncludes woman-centrc begnnng netzens for whom the utlty s quantzed by the emoton value and the degree of pleasure need s characterzed as emoton value. The basc prncple of constructng the nference rules: the consequent ncreases as the antecedent strengthens; When antecedent 2(EF) and antecedent 3(EM) are both large negatve values, the recommendaton degree s ntense opposton (VLO); On the premse of the theory of Bounded Ratonalty, although one of the antecedents s a negatve value, the other s very hgh and ths antecedent s demanded by the consumer, then the purchase ntenton s the consumer and then the purchase ntenton s strong. Therefore the extent of recommendaton degree should advance along wth the growth of the hgh antecedent. Four types of rules of the consumer s analyss go as follows: Regardng the type of hgh utlty/hgh pleasure consumers, the hgher the utlty of the product as well as the better the emoton satsfes the consumer the more wllng to buy. Namely the recommendaton ntensty should be bgger. For the type of low utlty/low pleasure the product utlty and the emoton satsfacton have lttle mpact on purchase ntenton. If t can meet the consumer s basc need, namely evaluaton and emoton s nonnegatve, the consumers are wllng to buy. So when antecedents 2(EF) and antecedents 3(EM) rse to zero, consequent (R) jumps rapdly to an ntense degree of recommendaton. Hgh utlty/low pleasure consumers tend to buy hgh-utlty product the onlne revews of whch are good, but they are less nfluenced by the emoton of the comments, namely, the purchase ntenton rses wth the utlty evaluaton rather than the pleasure. The low utlty/hgh pleasure s on the contrary. Mamdan reasonng method s adopted and the Centrod method s employed for defuzzfcaton Experment desgn and analyss From the related posts of Badu Post Bar ( usng the page collecton tool, bget_share, downloaded more than 1200 revews of whch there are 437 sentences commented on X brand of notebook computer and 328 on Y. After beng screened 160 revews are dentfed as the fnal corpus respectvely. For each one of these 160 revews the comprehensve evaluaton and the emoton degree were calculated. By the above nference method the recommendaton degree s obtaned for four types of consumers. The abscssa n the graph represents the number of the comments. In order to facltate the observaton of recommendaton laws we sort the evaluaton value of X from low to hgh whle the emoton value of Y s sorted the same way. The effects of the two experments are shown n Fgg :

6 Narsa Zhao and Yng Lu 50 Fg 4.2: Recommendaton effect of product X to Fg 4.3: Recommendaton effect of product Y to four consumer types four consumer types From Fg 4.2 the recommendaton degree of the consumers wth strong need of product utlty (black and blue) s enhanced wth the ncreasng of evaluaton value. From 120 comments the recommendaton degree of hgh utlty/hgh pleasure (blue) consumers slows gradually. Gven that both are at the same emotonal level and the low pleasure (black) types do not ask so much pleasure, the recommendaton degree s not lmted by the antecedents. The evaluaton value has mnmal mpact on the recommendaton degree of the consumers wthout strong utlty demandng (red and green). Thus ther values reman at a hgh level. As can be seen n the comments between the 80 and 100, when the evaluaton value accelerate to a certan extent, the recommendaton degree of low utlty/low pleasure (green) consumers shoots up and remans at a hgh level. From Fg 4.3 hgh utlty/hgh pleasure and low utlty/low pleasure consumers adopt the same recommendaton law as the analyss of Fg 4.2. However, the recommendaton degree of low utlty/hgh-pleasure s gradually enhanced wth the growth of emoton value and the one of hgh utlty/low-pleasure tends to be horzontal. The experments verfy the valdty of reasonng rules. 5 Concluson Ths paper consders consumers psychologcal factors and the nherent fuzzy propertes of the natural language. Based on the fuzzy model for the evaluaton and emoton of onlne revew texts, fuzzy corpus are establshed usng evaluaton and emoton words. Combned wth consumer preferences of product attrbutes, the comprehensve evaluaton and emotonal value s calculated. Regardng them as nference antecedents towards dfferent consumer types wth bounded ratonalty we acheve the recommendaton degree. Experments of a large number of comments on X and Y notebook products are gven as well as a detaled analyss of recommended levels and trends. The valdty of the method s proved. In the future t s necessary to enrch and verfy the corpus. Meanwhle experments on dfferent product types and mprovement of nference rules are to be contnued.

7 Product Approxmate Reasonng of Onlne Revews 51 Acknowledgments Ths work s partally supported by a research grant from the Program of the Natonal Natural Scence Foundaton of Chna (No ). References [1] C. Dellarocas, The dgtzaton of Word-of-Mouth: Promse and challenges of onlne feedback mechansms. Management Scence, 49(10), (2003) [2] J. Chevaler and D. Mayzln, The effect of word of mouth on sales: Onlne book revews, Journal of Marketng Research, 43(3), (2006) [3] S.Senecal and J. Nantel, The nfluence of onlne product recommendatons on consumers onlne choces, Journal of Relatng, 80(2), (2004) [4] B. Lu and M. Hu, Opnon observer: analyzng and comparng opnons on the web, Proceedngs of the 14th Internatonal Conference on World Wde Web, Chba, Japan, 2005, [5] G. Adomavcus and A. Tuzhln, Toward the next generaton of recommender systems: A survey of the State-of-the-Art and possble extensons, IEEE Transactons on Knowledge and Data Engneerng, 17(6), (2005) [6] S. Acar, D. Zhang, S. Smoff and J. Debenham, Recommender system based on consumer product revews, Proceedngs of the 2006 IEEE Conference on Web Intellgence, Australa, (2006) [7] P. Cheol, A comparson between cyber shoppers and non-cyber shoppers n Korea, Proceedngs of the 5th Internatonal Conference on Pervasve Servces, Sorrento, Italy, (2008) [8] M. Lenar and J. Sobeck, Usng recommendaton to mprove negotatons n agent-based systems. Journal of Unversal Computer Scence. 13(2), (2007) [9] Y. C. Choe, D. R. H wang, M. Km and J. Moon, Product heterogenety: Moderatng effect on onlne consumer behavor, System Scences 40th Annual Hawa Internatonal Conference, Bg Island, Hawa, (2007) Narsa Zhao receved the MS degree n Operatonal Research and Cybernetcs n 1995 from Dalan Unversty of Technology (DUT) and the PhD degree n Systems Engneerng n 1998 from DUT. He s currently an Assocate Professor n System Engneerng Insttute n DUT. Hs research nterests are Text Sentment Computng, System Analyss and Model Constructon.

8 Appled Mathematcs & Informaton Scences An Internatonal 5 (2) (2011), 33S-37S