Competitive Relationship Quantitative Model by Integrating Word-of-Mouth and Geographic Location
It is an important problem of identifying and quantifying the competition in similar services or products in the research area of competitive relationship mining. A scientific and reasonable evaluation metric of competitive relationship is proposed, and a comprehensive evaluation system of entity co...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2020-05-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/CN/abstract/abstract2195.shtml |
| _version_ | 1818649230879752192 |
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| author | LI Aixian, QIAO Shaojie, HAN Nan, YUAN Chang'an, HUANG Ping, PENG Jing, ZHOU Kai |
| author_facet | LI Aixian, QIAO Shaojie, HAN Nan, YUAN Chang'an, HUANG Ping, PENG Jing, ZHOU Kai |
| author_sort | LI Aixian, QIAO Shaojie, HAN Nan, YUAN Chang'an, HUANG Ping, PENG Jing, ZHOU Kai |
| collection | DOAJ |
| description | It is an important problem of identifying and quantifying the competition in similar services or products in the research area of competitive relationship mining. A scientific and reasonable evaluation metric of competitive relationship is proposed, and a comprehensive evaluation system of entity competitive relationship is constructed. Dimension reduction and theme extraction on users reviews are achieved by using the latent Dirichlet allocation (LDA) model, the similarity function of comments is constructed, and the similarity degree of entity users comments is quantified. Based on the geographic location information of entities, the spatial distance of entities is calculated, the adjacent relation of entities is constructed, the distance of entities with adjacent relationship is regarded as the cluster center, and the entities are clustered by using the K-nearest neighbor (KNN) algorithm. The location & topical model (LTM) is proposed by integrating user's reviews, entity's geographical attributes, and quantifying the com-petitive relationship between entities. Conducted on a large number of real social network data, the experiments results show that the proposed method has great advantages in quantitative metric formulation, practicability and time performance. |
| first_indexed | 2024-12-17T01:31:02Z |
| format | Article |
| id | doaj.art-27d67f9f6b7f417c84047d53e0f1c13c |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-17T01:31:02Z |
| publishDate | 2020-05-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-27d67f9f6b7f417c84047d53e0f1c13c2022-12-21T22:08:34ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182020-05-0114582583210.3778/j.issn.1673-9418.1905094Competitive Relationship Quantitative Model by Integrating Word-of-Mouth and Geographic LocationLI Aixian, QIAO Shaojie, HAN Nan, YUAN Chang'an, HUANG Ping, PENG Jing, ZHOU Kai01. School of Cybersecurity, Chengdu University of Information Technology, Chengdu 610225, China 2. School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China 3. Software Automatic Generation and Intelligent Service Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225, China 4. School of Management, Chengdu University of Information Technology, Chengdu 610103, China 5. Nanning Normal University, Nanning 530001, China 6. Sichuan Provincial Department of Public Security, Chengdu 610014, ChinaIt is an important problem of identifying and quantifying the competition in similar services or products in the research area of competitive relationship mining. A scientific and reasonable evaluation metric of competitive relationship is proposed, and a comprehensive evaluation system of entity competitive relationship is constructed. Dimension reduction and theme extraction on users reviews are achieved by using the latent Dirichlet allocation (LDA) model, the similarity function of comments is constructed, and the similarity degree of entity users comments is quantified. Based on the geographic location information of entities, the spatial distance of entities is calculated, the adjacent relation of entities is constructed, the distance of entities with adjacent relationship is regarded as the cluster center, and the entities are clustered by using the K-nearest neighbor (KNN) algorithm. The location & topical model (LTM) is proposed by integrating user's reviews, entity's geographical attributes, and quantifying the com-petitive relationship between entities. Conducted on a large number of real social network data, the experiments results show that the proposed method has great advantages in quantitative metric formulation, practicability and time performance.http://fcst.ceaj.org/CN/abstract/abstract2195.shtmlcompetitive relationshiprelationship quantificationword-of-mouthgeographical location |
| spellingShingle | LI Aixian, QIAO Shaojie, HAN Nan, YUAN Chang'an, HUANG Ping, PENG Jing, ZHOU Kai Competitive Relationship Quantitative Model by Integrating Word-of-Mouth and Geographic Location Jisuanji kexue yu tansuo competitive relationship relationship quantification word-of-mouth geographical location |
| title | Competitive Relationship Quantitative Model by Integrating Word-of-Mouth and Geographic Location |
| title_full | Competitive Relationship Quantitative Model by Integrating Word-of-Mouth and Geographic Location |
| title_fullStr | Competitive Relationship Quantitative Model by Integrating Word-of-Mouth and Geographic Location |
| title_full_unstemmed | Competitive Relationship Quantitative Model by Integrating Word-of-Mouth and Geographic Location |
| title_short | Competitive Relationship Quantitative Model by Integrating Word-of-Mouth and Geographic Location |
| title_sort | competitive relationship quantitative model by integrating word of mouth and geographic location |
| topic | competitive relationship relationship quantification word-of-mouth geographical location |
| url | http://fcst.ceaj.org/CN/abstract/abstract2195.shtml |
| work_keys_str_mv | AT liaixianqiaoshaojiehannanyuanchanganhuangpingpengjingzhoukai competitiverelationshipquantitativemodelbyintegratingwordofmouthandgeographiclocation |