Opinion-based co-occurrence network for identifying the most influential product features
Nowadays, social networking sites such as Facebook, Twitter, LinkedIn, YouTube, and other e-commerce websites produce a large number of text reviews. These text reviews mostly describe the product features and their opinions, which are the most important to the product developers, launchers, or buyers for business development and decision-making processes. Therefore, we present an opinion-based co-occurrence network for product reviews. The main aim of this research is to identify the popularity of product features or popular terms, the number of connections of a term, the strong relationship between terms, grouping the product terms, and the sentiment polarity links between terms in both positive sentiment and negative sentiment. Also, we employed the Harel-Koren fast multi-scale layout algorithm and CNM (Clauset-Newman-Moore) algorithm for visualizing and grouping the network. We then measured the overall graph metrics and vertex metrics to characterize the network. Additionally, the experimental result shows the ranked product features and their social strength between product features and sentiments.
Abdelsadek, Y., Chelghoum, K., Herrmann, F., Kacem, I., & Otjacques, B. (2018). Community extraction and visualization in social networks applied to Twitter. Information Sciences, 424, 204-223.
Alam, M. H., Ryu, W. J., & Lee, S. (2016). Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences, 339, 206-223.
Amplayo, R. K., Hong, S., & Song, M. (2018). Network-based approach to detect novelty of scholarly literature. Information Sciences, 422, 542-557.
Arif, T. (2015). The mathematics of social network analysis: metrics for academic social networks. International Journal of Computer Applications Technology and Research, 4(12), 889-93.
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45-59.
Chang, V. (2018). A proposed social network analysis platform for big data analytics. Technological Forecasting and Social Change, 130, 57-68.
Chen, J., Liu, Y., Yang, G., & Zou, M. (2018). Inferring tag co-occurrence relationship across heterogeneous social networks. Applied Soft Computing, 66, 512-524.
Cheng, K., Li, J., Tang, J., & Liu, H. (2017, February). Unsupervised sentiment analysis with signed social networks. In Thirty-First AAAI Conference on Artificial Intelligence.
Claros, I., Cobos, R., & Collazos, C. A. (2015). An approach based on social network analysis applied to a collaborative learning experience. IEEE Transactions on Learning Technologies, 9(2), 190-195.
Colladon, A. F., & Remondi, E. (2017). Using social network analysis to prevent money laundering. Expert Systems with Applications, 67, 49-58.
Cristian Zanoci & Jim Andress (2016). The Times They Are a Changin’: Evolving Communities in a Musician Network. Standford Network Analysis Project. http://snap.stanford.edu/class/cs224w-2016/projects/cs224w-70-final.pdf.
De Brún, A., & McAuliffe, E. (2018). Social Network Analysis as a methodological approach to explore health systems: A case study exploring support among senior managers/executives in a hospital network. International journal of environmental research and public health, 15(3), 511.
De Marneffe, M. C., & Manning, C. D. (2008). Stanford typed dependencies manual (pp. 338-345). Technical report, Stanford University.
Farasat, A., Gross, G., Nagi, R., & Nikolaev, A. G. (2016). Social network analysis with data fusion. IEEE Transactions on Computational Social Systems, 3(2), 88-99.
Forss, T., & Sarlin, P. (2016, December). From news to company networks: Co-occurrence, sentiment, and information centrality. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-8). IEEE.
Garg, M., & Kumar, M. (2018). Identifying influential segments from word co-occurrence networks using ahp. Cognitive Systems Research, 47, 28-41.
Hayat, T. Z., Lesser, O., & Samuel-Azran, T. (2017). Gendered discourse patterns on online social networks: A social network analysis perspective. Computers in Human Behavior, 77, 132-139.
Hirsch, L., & Andrews, S. (2016, July). Visualising text co-occurrence networks. In CEUR Workshop Proceedings (Vol. 1637, pp. 19-27). Tilburg University.
Hu, J., & Zhang, Y. (2015). Research patterns and trends of Recommendation System in China using co-word analysis. Information processing & management, 51(4), 329-339.
Hughes, C. E., Bright, D. A., & Chalmers, J. (2017). Social network analysis of Australian poly-drug trafficking networks: How do drug traffickers manage multiple illicit drugs?. Social Networks, 51, 135-147.
Ingo Feinerer (2017). Introduction to the tm Package Text Mining in R. https://cran.r-project.org/web/packages/tm/vignettes/tm.pdf.
Kauer, A. U., & Moreira, V. P. (2016). Using information retrieval for sentiment polarity prediction. Expert Systems with Applications, 61, 282-289.
Khasseh, A. A., Soheili, F., Moghaddam, H. S., & Chelak, A. M. (2017). Intellectual structure of knowledge in iMetrics: A co-word analysis. Information processing & management, 53(3), 705-720.
Kim, J., & Hastak, M. (2018). Social network analysis: Characteristics of online social networks after a disaster. International Journal of Information Management, 38(1), 86-96.
Koren, D. H. Y. (2002). A fast multi-scale method for drawing large graphs. Journal of graph algorithms and applications, 6(3), 179-202.
Kulig, A., Kwapień, J., Stanisz, T., & Drożdż, S. (2017). In narrative texts punctuation marks obey the same statistics as words. Information Sciences, 375, 98-113.
Li, H., An, H., Wang, Y., Huang, J., & Gao, X. (2016). Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network. Physica A: Statistical Mechanics and its Applications, 450, 657-669.
Li, L., Zhang, Q., Tian, J., & Wang, H. (2018). Characterizing information propagation patterns in emergencies: A case study with Yiliang Earthquake. International Journal of Information Management, 38(1), 34-41.
Li, Y., Tu, Y., & Li, X. (2018). Study on Enterprises’ Internet Public Opinion Area Hotspots Based on Social Network Analysis.
Li, Y., Zhang, D., Luo, P., & Jiang, J. (2017). Interpreting the formation of co-author networks via utility analysis. Information Processing & Management, 53(3), 624-639.
MeaningCloudTM (http://www.meaningcloud.com) have been used for Text Analytics purposes in the development/testing/validation of this research/prototype/software.
Mishra, N., Schreiber, R., Stanton, I., & Tarjan, R. E. (2007, December). Clustering social networks. In International Workshop on Algorithms and Models for the Web-Graph (pp. 56-67). Springer, Berlin, Heidelberg.
Mukherjee, S., & Bhattacharyya, P. (2012, March). Feature specific sentiment analysis for product reviews. In International Conference on Intelligent Text Processing and Computational Linguistics (pp. 475-487). Springer, Berlin, Heidelberg.
Ongkowijoyo, C. S., & Doloi, H. (2018). Understanding of Impact and Propagation of Risk based on Social Network Analysis. Procedia engineering, 212, 1123-1130.
Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., & Jia, W. (2018). Influence analysis in social networks: a survey. Journal of Network and Computer Applications, 106, 17-32.
Qiu, J., Liu, C., Li, Y., & Lin, Z. (2018). Leveraging sentiment analysis at the aspects level to predict ratings of reviews. Information Sciences, 451, 295-309.
Qiu, Z., & Shen, H. (2017). User clustering in a dynamic social network topic model for short text streams. Information Sciences, 414, 102-116.
Radhakrishnan, S., Erbis, S., Isaacs, J. A., & Kamarthi, S. (2017). Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PloS one, 12(3), e0172778.
Razghandi, M., & Golpaygani, S. A. H. (2017, November). A context-aware and user behavior-based recommender system with regarding social network analysis. In 2017 IEEE 14th International Conference on e-Business Engineering (ICEBE)(pp. 208-213). IEEE.
Richard G. Brereton (2017). Basic matrix algebra. Journal of Chemometrics, 31, 1-4. DOI: 10.1002/cem.2833.
Roy, S., Dey, P., & Kundu, D. (2017). Social Network Analysis of Cricket Community Using a Composite Distributed Framework: From Implementation Viewpoint. IEEE Transactions on Computational Social Systems, 5(1), 64-81.
Scott, J., Tallia, A., Crosson, J. C., Orzano, A. J., Stroebel, C., DiCicco-Bloom, B., ... & Crabtree, B. (2005). Social network analysis as an analytic tool for interaction patterns in primary care practices. The Annals of Family Medicine, 3(5), 443-448.
Shin, K. Y., & Lee, J. H. (2017, December). A job applicants' résumé verification method using a social network analysis Using Facebook like as Linkedin for a recruiting. In 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) (pp. 1-5). IEEE.
Smith, M. A., Shneiderman, B., Milic-Frayling, N., Mendes Rodrigues, E., Barash, V., Dunne, C., ... & Gleave, E. (2009, June). Analyzing (social media) networks with NodeXL. In Proceedings of the fourth international conference on Communities and technologies (pp. 255-264). ACM.
Tang, J., Qu, M., & Mei, Q. (2015, August). Pte: Predictive text embedding through large-scale heterogeneous text networks. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1165-1174). ACM.
Tubishat, M., Idris, N., & Abushariah, M. A. (2018). Implicit aspect extraction in sentiment analysis: Review, taxonomy, oppportunities, and open challenges. Information Processing & Management, 54(4), 545-563.
Varlamis, I., Eirinaki, M., & Louta, M. (2010, August). A study on social network metrics and their application in trust networks. In 2010 International Conference on Advances in Social Networks Analysis and Mining (pp. 168-175). IEEE.
Wakita, K., & Tsurumi, T. (2007, May). Finding community structure in mega-scale social networks. In Proceedings of the 16th international conference on World Wide Web (pp. 1275-1276). ACM.
Wissink, M., & Mazzucato, V. (2018). In transit: Changing social networks of sub-Saharan African migrants in Turkey and Greece. Social Networks, 53, 30-41.
Yang, B., Liu, Y., Liang, Y., & Tang, M. (2019). Exploiting user experience from online customer reviews for product design. International Journal of Information Management, 46, 173-186.
Yang, H. L., & Lin, Q. F. (2018). Opinion mining for multiple types of emotion-embedded products/services through evolutionary strategy. Expert Systems with Applications, 99, 44-55.
Yang, S., Han, R., Wolfram, D., & Zhao, Y. (2016). Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis. Journal of Informetrics, 10(1), 132-150.
Zarrinkalam, F., Kahani, M., & Bagheri, E. (2018). Mining user interests over active topics on social networks. Information Processing & Management, 54(2), 339-357.
Zhang, Z., Gu, Q., Yue, T., & Su, S. (2017). Identifying the same person across two similar social networks in a unified way: Globally and locally. Information Sciences, 394, 53-67.
Zhao, W., Mao, J., & Lu, K. (2018). Ranking themes on co-word networks: Exploring the relationships among different metrics. Information Processing & Management, 54(2), 203-218.
Zhou, F., Qu, Q., & Toivonen, H. (2017). Summarisation of weighted networks. Journal of Experimental & Theoretical Artificial Intelligence, 29(5), 1023-1052.