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Sentiment Analysis of Specific Product’s Features Using Product Tree for Application in New Product Development

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 8))

Abstract

New Product Development (NPD) is a multi-step process by which novel products are introduced in the market. Sentiment analysis, which ascertains the popularity of each new feature added to the product, is one of the key steps in this process. In this paper we present an approach by which product designers analyze users’ reviews from social media platforms to determine the popularity of a specific product’s feature in order to make a decision about adding it to the product’s next generation. Our proposed approach utilizes a product tree generated from a product specification document to facilitate forming an efficient link between features mentioned in the users’ reviews and those of the product designer’s interest. Furthermore, it captures the links/interactions between a feature of interest and its other related features in a product to ascertain its polarity.

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References

  1. Tuarob, S., Tucker, C.S.: A product feature inference model for mining implicit customer preferences within large scale social media networks. In: ASME 2015 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Boston, Massachusetts (2015)

    Google Scholar 

  2. Tucker, C., Kim, H.: predicting emerging product design trend by mining publicly available customer review data. In: 18th International Conference on Engineering Design Technical University of Denmark (2011). https://www.designsociety.org/publication/30612/predicting_emerging_product_design_trend_by_mining_publicly_available_customer_review_data

  3. Li, S., Nahar, K., Fung, B.C.M.: Product customization of tablet computers based on the information of online reviews by customers. J. Intell. Manuf. 26, 97–110 (2015). doi:10.1007/s10845-013-0765-7

    Article  Google Scholar 

  4. Li, Y.M., Chen, H.M., Liou, J.H., Lin, L.F.: Creating social intelligence for product portfolio design. Decis. Support Syst. 66, 123–134 (2014). doi:10.1016/j.dss.2014.06.013

    Article  Google Scholar 

  5. Wang, H., Wang, W.: Product weakness finder: an opinion-aware system through sentiment analysis. In: Industrial Management & Data Systems, vol. 114, pp. 1301–1320, (2014). http://www.emeraldinsight.com/doi/pdfplus/10.1108/IMDS-02-2014-0069

  6. Mirtalaie, M.A., Hussain, O.K., Chang, E.: FEATURE : new product development using feature- drift based framework for unique aspect recommendation. In: IEEE International Conference on e-Business Engineering, Macau, China (2016). doi:10.1109/ICEBE.2016.43

  7. Mirtalaie, M.A., Hussain, O.K., Chang, E., Hussain, F.K.: A decision support framework for identifying novel ideas. In: New Product Development from Cross-Domain Analysis, Information Systems, vol. 69, pp. 59–80, (2017). doi:https://doi.org/10.1016/j.is.2017.04.003

  8. Wei, W., Gulla, J.A.: Sentiment learning on product reviews via sentiment ontology tree. In: ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics. Proceedings of the Conference, pp. 404–413 (2010). http://www.scopus.com/inward/record.url?eid=2-s2.0-84859962524&partnerID=tZOtx3y1

  9. Mukherjee, S., Joshi, S.: Sentiment aggregation using ConceptNet ontology. In: Proceedings of Sixth International Joint Conference on Natural Language Processing, pp. 570–578 (2013) http://www.aclweb.org/anthology/I13-1065

  10. Agarwal, B., Mittal, N., Bansal, P., Garg, S.: Sentiment analysis using common-sense and context information. In: Computational Intelligence and Neuroscience, (2015). doi:10.1155/2015/715730

  11. Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28, 813–830 (2016). doi:10.1109/TKDE.2015.2485209

    Article  Google Scholar 

  12. Liu, Q., Gao, Z., Liu, B., Zhang, Y.: Automated rule selection for aspect extraction in opinion mining. In: Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 1291–1297 (2015)

    Google Scholar 

  13. Gu, X., Kim, S.: What parts of your apps are loved by users? In: 2015 30th IEEE/ACM International Conference Automation Software Engineering IEEE Computer Society, Washington, pp. 760–770 (2015) doi:10.1109/ASE.2015.57

  14. Bagheri, A., Saraee, M., De Jong, F.: Care more about customers: unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based Syst. 52, 201–213 (2013). doi:10.1016/j.knosys.2013.08.011

    Article  Google Scholar 

  15. Jeyapriya, A., Selvi, C.S.K.: Extracting aspects and mining opinions in product reviews using supervised learning algorithm. In: 2nd International Conference on Communication and Electronics Systems, ICECS, pp. 548–552. IEEE, Coimbatore (2015). doi:10.1109/ECS.2015.7124967

  16. Chen, Y., Perozzi, B., Skiena, S.: Vector-based similarity measurements for historical figures. In: Lecture Notes in Computer Science (Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 179–190 (2015). doi:10.1007/978-3-319-25087-8_17

  17. Manek, A.S., Shenoy, P.D., Mohan, M.C., Venugopal, K.R.: Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier, World Wide Web (2016). doi:10.1007/s11280-015-0381-x.

  18. Alghunaim, A., Mohtarami, M., Cyphers, S., Glass, J.: A Vector Space Approach for Aspect Based Sentiment Analysis, pp. 116–122 (2015)

    Google Scholar 

  19. Samha, A.K., Li, Y., Zhang, J.: Aspect - based opinion mining from product reviews using conditional random fields. In: AusDM 2015: the 13th Australasian Data Mining Conference, University of Technology, Sydney, Australia (2015)

    Google Scholar 

  20. Bagheri, A., Saraee, M., de Jong, F.: ADM-LDA: an aspect detection model based on topic modelling using the structure of review sentences. J. Inf. Sci. 40, 621–636 (2014). doi:10.1177/0165551514538744

    Article  Google Scholar 

  21. Bhattacharjee, S., Das, A., Bhattacharya, U., Parui, S.K., Roy, S.: Sentiment analysis using cosine similarity measure. In: IEEE 2nd International Conference on Recent Trends in Information System, pp. 27–32. doi:10.1109/ReTIS.2015.7232847

  22. Huang, J., Etzioni, O., Zettlemoyer, L., Clark, K., Lee, C.: Revminer: an extractive interface for navigating reviews on a smartphone. In: Proceedings of 25th Annual ACM Symposium on User Interface Software and Technology, pp. 3–12 (2012). doi:10.1145/2380116.2380120

  23. Zhai, Z., Liu, B., Xu, H.: Constrained LDA for grouping product features in opinion mining. Adv. Knowl. Discov. Data Min. 6634, 448–459 (2011). doi:10.1007/978-3-642-20841-6_37

    Google Scholar 

  24. Suleman, K., Vechtomova, O.: Discovering aspects of online consumer reviews. J. Inf. Sci. 42, 492–506 (2015). doi:10.1177/0165551515595742

    Article  Google Scholar 

  25. Ye, K., Li, L., Guo, M., Qian, Y., Yuan, H.B.: Summarizing product aspects from massive online review with word representation. Knowl. Sci. Eng. Manag. 9403, 318–323 (2015). doi:10.1007/978-3-319-25159-2

  26. Zhou, L., Chaovalit, P.: Ontology-supported polarity mining. J. Am. Soc. Inf. Sci. Technol. 59, 98–110 (2008). doi:10.1002/asi

    Article  Google Scholar 

  27. Lau, R.Y.K., Li, C., Liao, S.S.Y.: Social analytics: learning fuzzy product ontologies for aspect-oriented sentiment analysis. Decis. Support Syst. 65, 80–94 (2014). doi:10.1016/j.dss.2014.05.005

    Article  Google Scholar 

  28. Cambria, E., Poria, S., Bajpai, R., Schuller, B.: SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: Proceedings of COLING 2016, 26th International Conference Computational Linguistics Technical Papers, Osaka, Japan, pp. 2666–2677 (2016). http://sentic.net/computing/

  29. Deeply Moving, (n.d.). https://nlp.stanford.edu/sentiment/ Accessed 8 May 2017

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Correspondence to Monireh Alsadat Mirtalaie .

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Mirtalaie, M.A., Hussain, O.K., Chang, E., Hussain, F.K. (2018). Sentiment Analysis of Specific Product’s Features Using Product Tree for Application in New Product Development. In: Barolli, L., Woungang, I., Hussain, O. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-65636-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-65636-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65635-9

  • Online ISBN: 978-3-319-65636-6

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