Product Quality and Safety Incident Information Tracking Based on Web

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1 Product Qualty and Safety Incdent Informaton Trackng Based on Web News 1 Yuexang Yang, 2 Correspondng Author Yyang Wang, 2 Shan Yu, 2 Jng Q, 1 Hual Ca 1 Chna Natonal Insttute of Standardzaton, Beng , Chna *2 School of Economcs and Management, Behang Unversty, Beng , Chna alexgothe@gmal.com Abstract In recent years, product qualty and safety events happened frequently, whch poses a threat to the health and property safety of consumers. Wth the development of the Internet, Web news has become one of the mportant channels for nformaton release regardng product qualty and safety ssues. Therefore, dentfyng and trackng these ncdents nformaton plays an mportant role n provdng early warnng for product qualty and safety problems. In ths paper, through the analyss of the characterstcs of Web news regardng product qualty and safety ncdents, an event-based multvector text representaton model has been establshed, and a correspondng smlarty calculaton method has been developed. Based on K-Nearest Neghbor classfcaton algorthm, a topc-trackng model has been set up, and fnally the effectveness of the model has been verfed va the desgn of experments. Keywords: Web News; Product Qualty and Safety Incdent; Mult-Dmensonal VSM; Smlarty Calculaton; KNN 1. Introducton Product qualty and safety s drectly related to the health and safety of consumers. Therefore, close montorng of product qualty and safety n a tmely manner could avod those ssues whch may cause sgnfcant problems. At the begnnng of product qualty and safety event, there s sporadc nformaton n web meda. Then wth a rapd ncrease of related nformaton on the Internet, t s very lkely to evolve nto nfluental publc events. In addton, when the product qualty and safety event occurs, government agences often release announcement, and news meda wll follow-up n a tmely manner. Ths web nformaton provdes useful data for trackng related ssues of product qualty and safety, and for buldng product qualty and safety rsk montorng and early warnng system. However, ths nformaton does not get fully utlzed currently, and the study on product qualty and safety nformaton trackng s very scarce. Therefore, ths paper wll focus on trackng web news about product qualty and safety events, by usng the theory and technology of Web text mnng for topc trackng, and by settng up a mult-vector space text model based on the characterstcs of Web news and product qualty and safety event nformaton. By trackng the product qualty and safety event nformaton, to fnd out the characterstcs of ts development and propagaton, t wll be helpful to provde certan gudance for the development of relevant polces. 2. Related research 2.1. Topc detecton and trackng (TDT) on Web nformaton Topc detecton and trackng (TDT) s a research area closely related to Web nformaton flow analyss. TDT study began n 1996 as an nformaton processng method to dentfy unknown topc and track meda nformaton flow. TDT study nvolves three basc concepts: (1) topc whch ncludes a core event or actvty, plus other events and actvtes drectly related to [1] ; (2) event whch occurs at a specfc tme and place, nvolvng certan partcpants, and accompaned by some of the results of other events; (3) story whch s closely related to a topc, contanng news clps of the event statement. Journal of Convergence Informaton Technology(JCIT) Volume 8, Number 2, Jan 2013 do : /ct.vol8.ssue2.23

2 Topc trackng s desgned to montor the nformaton flow of news reports, to fnd out the news reports related to the known topc, whch s equvalent of a specal bnary classfcaton task. There are three dfferent ways to acheve topc trackng, whch are based on query tracker, probablstc model and supervsed classfcaton algorthms respectvely Text mnng Text Mnng, also known as text data mnng or text knowledge dscovery, s a process to obtan nterestng or useful modes for users from unstructured text nformaton [2] Text representaton model: Text representaton ndcates whch text feature to choose and how to mathematcally descrbe text features to characterze a text. Ths s an mportant task n text mnng. Currently man frame text representaton methods nclude vector space model, Boolean model, and probablty model et al. 1) Boolean model Boolean model s the frst proposed model, whch s a smple model based on set theory and Boolean algebra, a specal case of the vector space model, followng the bnary decson crteron,.e., only 0 and 1 are used to represent vector weghts. In tradtonal Boolean model, a text s generally converted nto an nverted text dentfed as a keyword or a topc word, and then an approprate match s made. The advantage of Boolean model s smple and structural wth semantc sklls. However, there are also drawbacks. Because Boolean model's strategy s based on the bnary decson crteron, two documents only have two states,.e., relevant and rrelevant. Wthout the concept of document classfcaton (rank), t s mpossble to reflect the weght of the feature; and dffcult to control lterature screenng. Sometmes there are too many lteratures for user to select for more pertnent one, whle sometmes there s nothng passng screenng. Salton et al. proposed vector space model based on Boolean model. Decson trees and assocaton rules are all based on Boolean model. 2) Vector space model Vector space model (VSM) [3] s one of the most smple and effcent text representaton models. The basc dea of the vector space model s to convert a gven text (artcles, queres, or a secton of artcles, etc.) nto a hgh dmenson vector. Its unque feature s easy to calculate the degree of smlarty between any of the two vectors,.e., the smlarty between two correspondng texts. VSM has advantage of knowledge representaton, as the result of smple concept and easy to operate, whch s currently the most popular text representaton model, and has dfferent applcatons n many systems. In vector space model, a document s treated as a seres of unordered collecton of entres, wth a correspondng weght value for each entry, and a feature vector for the document: V( d) ( t ( d), w ( d),, t ( d), w ( d) ) n n n Wheren t s the entry tem, w( d) s the weghtng value of t n text d. w( d) s generally defned as a functon of appearance frequency of t n d,.e. w( d) ( tf ( d)), whch s calculated usng TF-IDF formula: tf ( t, d ) log( N 0.01) nt W(, t d) 2 dtf [ ( td, ) log( N 0.01)] n (1) Wheren tf (, td) s the appearance frequency of entry t n text d, N s the total number of tranng text, and n s the number of texts wth entry t n tranng text set. t After pretreatment and word frequency statstcs, a text ultmately becomes a statstc varable as descrbed above. However, the classc vector space model also has problems. For example, t takes too long for the calculaton of smlarty to fnsh, and t s too large of dmensons. It s also too smple for the treatment of the word n ttle and n man body, gnorng the mportance of the word poston, so as to affect the classfcaton effectveness. It t

3 s assumng that text features are mutually ndependent, as the result, the nformaton easly gets lost regardng the context of the text semantcs and the underlyng concept structure. So that the model has a lmt for the amount of nformaton t could handle. 3) Probablty model Bayesan probablty model s based on probablty theory to acheve learnng and reasonng wth probablstc rules, assumng egenvectors are ndependent, whch s characterzed by the use of probablty to represent all forms of uncertanty. The results are shown as the probablty dstrbuton of the random varable, whch can be understood as the level of trust of dfferent possbltes. Nave Bayes algorthm and mproved algorthm are based on probablty model Text classfcaton method: Text classfcaton s a supervsed learnng process. Based on a marked collecton of tranng texts, t can be used to fnd the relatonshp between text features and text category, whch can then be used to udge the new text category. In general, text classfcaton has fve steps [4] as shown n Fgure 1: Fgure1. Basc steps of text classfcaton For the choce of classfcaton algorthm, there are a varety of text classfcaton algorthms avalable, such as vector space, Nave Bayes method, K-nearest neghbor algorthm, Neural Network and SVM methods. 3. Web news about product qualty and safety event 3.1. WEB news News s manly related to mportant events happened daly. News reports cover a wde range of topcs, wth specfcatons regardng the content and format of wrtng. As an nformaton carrer, news s a knd of sem-structured data [5]. News reports have the characterstcs of open, thematc, normatve and clarty. News often covers around a central event, generally reported n several news clps, targeted at related events wth rch nformaton to descrbe the causes and consequences of the ncdent. Some tmes news s released by authorty or agences wth strong credblty. The structure of a typcal news reports generally nclude sx (5W1H) elements,.e., when, where, who, what, why and how. Based on these sx elements, news reports descrbe comprehensvely the topc event. Compared wth tradtonal news, Web news s an nformaton carrer based on network, whch not only nhert the characterstcs of the tradtonal news, but also has new features such as real-tme, spread more rapdly, mult-faceted content, and mult-channel dssemnaton et al.

4 Therefore, news reports have the ntegrty and credblty on the topc descrbed, also have textual specfcaton whch facltates mnng. Ths paper wll focus on the analyss of product qualty and safety event n news reports Product qualty and safety event n WEB news reports Product qualty and safety ndcates there should be no harm to people or property durng ts lfe cycle, when n use and reasonably foreseeable msuse. Products n use are lkely to cause some harm to the user, e.g., the sharp edge of consumer goods may lead to cuts; consumer products wth chemcals may cause skn rrtaton or burns. When products cause harm due to qualty problems, t means that the event of product qualty and safety occurs. The event of damage caused by the products due to qualty problems often does not happen as an ndvdual case. In general, the same batch or the same type of products may have smlar degree of potental danger. Through WEB news coverage of these events, consumers can obtan relevant nformaton and avod nury. Because of the characterstcs of WEB news, consumers can usually get event nformaton ncludng products, nures and causes more comprehensvely. Currently WEB news s one of the man channels for consumers to obtan such nformaton. Product qualty and safety event news typcally contans product nformaton (product name, product type, etc.), event nformaton (tme, place, etc.), nury (name, causng reasons), and measures to deal wth. Informaton can be a comprehensve overvew of the overall event. Through topc dentfcaton and trackng of Web news, the product qualty and safety factors can be dentfed, provdng early warnng for product safety. 4. Topc trackng on product qualty and safety ncdents There are three steps to create a mult-vector model for a text: (1) feature selecton, classfcaton based on the characterstcs of dfferent tasks, and representaton of the text wth mult-vector by usng classfed features; (2) vector smlarty calculaton; (3) selecton of combnatons of modes, and parameters optmzaton Vector text representaton model For news reportng vector, the model constructon should start from the event. Feature selecton should also be based on product qualty and safety ncdents. Usually a descrpton of the event wll relate to the tme, place, names of persons and nsttuton groups. Other related content s flled nto event framework [6]. Therefore, n accordance wth the characterstcs of news coverage of the product qualty and safety event, ths study presents a more targeted representaton model based on mult-vector product qualty and safety event. For the text representaton of mult-vector model, Xaoyan Zhang [7] suggested that assumng all the other varables under the same condton, as the nformaton content of the model s reduced, system effectveness would reduce accordngly. As for Web news structure, the authors beleve that the news coverage has ts unque "content", "structure" and "tmng", whch are dfferent from the ordnary text features. In news reports, the ttle, the frst sentence, and the second sentence make sgnfcant contrbuton for news topc descrpton. Therefore, the authors beleve that ths nformaton can be extracted to construct the news text summary. Based on the characterstcs of product qualty and safety events and the structural features and style of the wrtng n news reports, pretreated feature words are placed n fve sub-vectors,.e., products, safety factor nformaton (Factor), tme, event summary (Profle) and event body (Content). The characterstcs of the frst two sub-vectors can be selected based on rule extracton, whle the screenng of the last two sub-vectors s depends on the poston n the report. If the canddate feature has appeared n the report ttle or n the frst sentence of the body, ths feature wll be put nto the event summary sub-vector. If t has appeared n the remanng of the body, t wll be placed n the event descrpton sub-vector. If the canddate

5 feature has appeared n two locatons, ths feature wll be contaned n both event summary and events descrpton sub-vectors. In ths paper, the feature set (F) s determned and dvded nto fve categores, so that a product qualty and safety Web news can be expressed as: ( F, F, F, F, F ) ( F, F, F, F, F ) product factor tme profle content Wheren ( Fproduct, Ffactor, Ftme, Fprofle, Fcontent ) are representng product, safety factor, tme, event summary and event body. Accordng to the mult-vector model, text d can be expressed as d ( d 1, d2, d3, d4, d 5) where d (( TW 1, 1),( TW 2, 2), ( TW 3, 3)) s a sub-vector. T s the entry tem, W s the correspondng weght of the entry, whch s used to ndcate the weght of the entry n the subvector. Vector feature weght calculaton s an mportant part of the text vector model. Currently feature selecton methods nclude document frequency (DF), nformaton gan, expect cross entropy, and mutual nformaton method. Although smple, there s good performance of DF method n classfcaton [8]. In ths paper, the TF * IDF method s selected, and the weght of feature words n a document s calculated as follows: 4.2. Smlarty calculaton [9-12] wt (, d) tf( t, d) df( t) (2) The degree of smlarty between vectors can be calculated by usng cosne smlarty calculaton method. In the mult-vector model, d ( d 1 11, d12, d13, d14, d15), and d2 ( d 21, d 22, d 23, d 24, d 25), assumng there s k knd of smlarty calculaton methods ( M1, M 2,,M k), the degree of smlarty of Web news text regardng product qualty and safety events can be calculated Sm ( d, d ) M ( d, d )(0 m 5;0 k) m m m m (3) Tme smlarty calculaton: The tmng of Web news has followng components,.e., Web news release tme, meda reports tme and the tme when event happened. Ths study s focused on the trackng of news topcs, so Web news release tme s chosen as the parameter,.e., when the document s created or modfed. Web crawler n the collecton of web pages wll set the tme as the tme orgn. In ths paper, tme dstance between document and the latest document n the tranng set s defned as ds (, ) mn{ tme tme } (4) tme Other smlarty calculaton: For two n-dmensonal vectors D1 and D2, the smlarty can be calculated by usng the cosne values of the angle between these two vectors. For example, abstract nformaton features component smlarty can be calculated as shown n equaton 5: Sm( t, t ) Sm ( d, d ) d d n d ( w w ) m, m, m1 n n d 2 2 w m, w m, m1 m1 (5)

6 Text smlarty calculaton: For 5 sub-vectors, smlarty can be calculated by combnaton, whch results n the smlarty between two texts. The combnaton method s usually chosen between lnear combnaton and classfer. In ths paper, the lnear combnaton method s selected. Accordng to the prevously constructed vector text representaton model, the dstance between the documents s calculated va Equaton 6: Sm( t1, t2) 1sm( d11, d21) 2sm( d12, d22) (6) sm( d13, d23) sm( d14, d24) sm( d15, d25) Wheren s text feature sub-vector weghtng factor, whch can be determned by tranng or genetc algorthm Topc trackng algorthm selecton [13-15] In ths paper, K-nearest neghbor method (KNN) s selected as the topc trackng algorthm, whch s one of the most commonly used automatc text classfcaton method. For KNN, when a test document s gven, system starts to fnd the nearest k neghbors n the tranng set, and a classfcaton score s gven to the canddate document based on the classfcaton of these neghbors. The smlarty of neghbors and testng document s taken as the documentaton category weghts of neghbors. If part of the document n the k-neghbors belongs to the same classfcaton, the summaton of each neghbor's weghts n the classfcaton wll be taken as the smlarty of the classfcaton and the test document. 5. Experments and results Currently, there s no publc evaluaton data set for the classfcaton of Web News [16-17]. In ths paper, 2189 product qualty and safety related news pages are taken from the Internet by Web crawler accordng to the topcs of nterests. The topc of news and the numbers of correspondng tranng set and test set are summarzed n the followng table. Table1. Product safety and qualty events Web news date set Theme Total number of webpage Tranng set Test set Kumho Tre SanluMlk Poson Capsule The obtaned texts are pretreated for segmentaton by usng modfed ICTCLAS software [18-19]. Extracted nformaton regardng products, factors, tme, summary, and content are stored n the correspondng database. An expermental text vector space model s set up based on the mult-vector text representaton model. The text smlarty s calculated, and the Web documents are classfed by usng the KNN algorthm Evaluaton crteron Precson, Recall and F value are used to evaluate the experments, whch are defned as followng. TP s the number of text that s correctly classfed, FP the number of msclassfed text, FN the number of wrongly dscarded text. Therefore: TP precson (7) TP FP recall TP TP FN (8)

7 2 precson recall F precson recall (9) 5.2. Results It has been demonstrated that only nouns and verbs are needed as entres to reflect the actual meanng of the web content, and that pronouns, adectves, quantfers, prepostons and adverbs can be elmnated to avod trackng nose. So n ths study, only nouns and verbs are selected for the egenvectors canddate set. In order to determne the value of the weghtng factor of the model, the followng experment s carred out,.e., the top 30% of feature words selected by the TF-IDF method are tested for the mpact on the classfcaton wth dfferent weght values. Snce news topc trackng s the man focus n ths study, the characterstcs of summary and content nformaton wll have sgnfcant mpact on the fnal result; whle the mpact of product and factor nformaton wll be less sgnfcant. As shown n Fgure 2, n the case of the weght rato of summary to content at 1:1, F value of 92.8% s obtaned. Wth ncrease n the summary weght, F values are sgnfcantly mproved. At weght rato of 9:1, F reaches the maxmum value close to 97%. Therefore, summary to content weght rato at 9 s selected % 97.00% 96.00% 95.00% 94.00% 93.00% 92.00% 91.00% 90.00% Dfferent abstract text weght rato on the trackng effect F Measure Fgure.2 The mpact of dfferent summary to content weght rato on trackng results Text data sets are constructed wth conventonal vector space model verses mult-vector model. Fgure 3 shows that better trackng s obtaned wth event-based mult-vector text representaton model compared wth conventonal vector space model, regardng product qualty and safety news The tradtonal vector space model Based on products qualty and safety ncdents vector text representaton model Fgure.3 Trackng result comparson between models Based on the above experments, self adaptve KNN algorthm s used for trackng three product qualty and safety events as shown n Table 1, wth the selected parameters,.e., the summary to content weght rato of 9:1, by usng the top 30% of verbs and nouns as feature words. Results are summarzed n table 2, whch demonstrates good values of recall, precson and F for all three topcs, and valdates all the models used n ths study.

8 Table2. Trackng result summary Theme Recall Precson F Kumho Tre events Sanlu Mlk Poson Capsule Conclusons Ths study s focusng on Web news product qualty and safety ncdents trackng. An event-based mult-vector text representaton model s establshed, and a correspondng smlarty calculaton method s developed. Based on K-Nearest Neghbor classfcaton algorthm, a topc-trackng model s set up. The model s verfed va the desgn of experments. 7. Acknowledgements Ths work was funded by the Natonal Natural Scence Foundaton of Chna under Grant Nos , Nos and Nos , Natonal Socal Scence Foundaton of Chna under contract number 11AZD096, and Qualty Inspecton for Nonproft Industry Specal Program under contract number The authors gratefully acknowledge the support of Key Laboratory of Complex System Analyss, Management and Decson (Behang Unversty), Mnstry of Educaton, and Beng Key Laboratory of Emergency Support Smulaton Technologes for Cty Operatons. The authors would lke to thank revewers for provdng much valuable comments and advce. 8. References [1] Charles L. Wayne. Multlngual Topc Detecton and Trackng: Successful Research Enabled by Corpora and Evaluaton, Language Resources and Evaluaton Conference (LREC), pp , [2] Quanfeng Lu, Be Lu, Xaohua Wang, Text Mnng Clusterng Algorthm Comparson Research, Computer Era, [3] Shaohu Lu, Kamng Dong, Haun Zhang, et al, An Approach of Mult-herarchy Text Classfcaton Based on Vector Space Model, Journal of Chnese Informaton Processng,vol.16, no.3, pp.8-14, [4] Maureen Taylor, Danelle C. Perry, Dffuson of tradtonal and new meda tactcs n crss communcaton, Publc Relatons Revew, vol. 31, pp , [5] Zhen Le, Event-based Analyzng Technology n News Report: Introductory Revew, Natonal Unversty of Defense Technology, [6] Xa Zhang. Research on Chnese Personal Emal Management Based on Topc Detecton, Beng Unversty of Posts and Telecommuncatons, [7] Xaoyan Zhang. Research on the Representaton Model and Technologes of Lnk Detecton and Trackng on News Topc, Natonal Unversty of Defense Technology, [8] Songwe Shan, Shcong Feng, Xaomng L, A Comparatve Study on Several Typcal Feature Selecton Methods for Chnese Web Page Categorzaton, Computer Engneerng and Applcatons, vol. 39, no.22, pp , [9] Wehua Luo, Manquan Yu, Hongbo Xu, The Study of Topc Detecton Based on Algorthm of Dvson and Mult-level Clusterng wth Mult-strategy Optmzaton, Journal of Chnese Informaton Processng, vol.20, no.1, pp.29-36, [10] Yongku Zhang, Honguan L, Text Classfcaton of Accdent News Based on Category Keyword, Journal of Computer Applcatons, vol.6, no.3, pp , [11] Zhmng Lu, Lu Lu, Publc Emoton Montorng and Survellance n Emergences, Systems Engneerng, vol.7, no.15, pp [12] Yu Hong, Yu Zhang, Tng Lu, et al., Topc Detecton and Trackng Revew, Journal of

9 Chnese Informaton Processng, vol.11, no.13, pp.71-84, [13] Yule Zhang, Yan Dang, et al., Automatc Onlne News Montorng and Classfcaton for Syndromc Survellance, Decson Support Systems, vol.47, no.18, pp , [14] Manabu Tor, et al., An exploratory Study of a Text Classfcaton Framework for Internet-Based Survellance of Emergng Epdemcs, Internatonal ournal of medcal nformaton, vol.80, no.3, pp.56-66, [15] Hual Ca, Lu Lu, L Wang, Automated Multple Herarchcal Classfcaton of Web News of Unexpected Events, Journal of Beng Unversty of Technology, vol.6, no.2, pp , [16] Yn Bao, E.M. Bakker, Content Based Web Samplng, Internatonal Journal of Dgtal Content Technology and ts Applcatons, vol.4, no.1, pp.43-68, 2010 [17] Dongl Han, Xn Song, Kazuk Maed, Topc Presentaton for a Free Conversaton System Based on the Web Texts, Internatonal Journal of Dgtal Content Technology and ts Applcatons, vol.4, no.3, pp.7-14, 2010 [18] Yngcheng Xu, L Wang,Zhsong Chen,Sqng Shan,Guopng Xa, Optmzaton and adustment polcy of two-echelon reservor nventory management wth forecast updates, Computers & Industral Engneerng, vol.63, no.4, pp , 2012 [19] Hubn Sh, Hong L, Lu Lu, L Wang, A synthetc evaluaton model for consumer products qualty safety based on the VIKOR method, to be publshed. [20] Sqng Shan, L Wang, Tenglong Xn and Zhumng B, Developng a rapd response producton system for arcraft manufacturng, Internatonal Journal of Producton Economcs, n press [21] Shan Sqng, Zhao Quhong, Hua Fan, Impact of qualty management practces on the knowledge creaton process: the Chnese avaton frm perspectve, Computers & Industral Engneerng, vol. 64, no.1, pp , 2013 [22] Sqng Shan, L Wang, Lng L. Modelng of emergency response decson-makng process usng stochastc Petr net: an e-servce perspectve, Informaton Technology and Management, vol. 13, no. 4, pp , 2012 [23] Sqng Shan, L Wang, Jng Wang, Y Hao, and Fan Hua, Research on e-government Evaluaton Model based on the Prncpal Component Analyss, Informaton Technology and Management, vol.12, no. 2, pp , 2011 [24] Sqng Shan, L Wang, Lng L, Yong Chen. An emergency response decson support system framework for applcaton n egovernment. Informaton Technology and Management, vol.13, no. 4, pp , 2012

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