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Predicting the customer’s opinion on amazon products using selective memory architecture-based convolutional neural network

  • Trupthi MandhulaEmail author
  • Suresh Pabboju
  • Narsimha Gugulotu
Article
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Abstract

Opinion mining and sentiment analysis are useful to extract subjective information out of bulk text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. Though performing sentiment analysis is a challenging task for the researchers to identify the user’s sentiments from the large datasets, it is unstructured in nature, and also includes slangs, misspells, and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; they are data collection, pre-processing, keyword extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, pre-processing was carried out for enhancing the quality of collected data. The pre-processing phase comprises of three systems: lemmatization, review spam detection, and removal of stop words and URLs. Then, an effective topic modelling approach latent Dirichlet allocation along with modified possibilistic fuzzy C-Means was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative, and neutral) by applying an effective machine learning classifier: Selective memory architecture-based convolutional neural network. The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6–20% related to the existing systems.

Keywords

Convolutional neural network Latent Dirichlet allocation Lemmatization Modified possibilistic fuzzy c-means Adam optimization algorithm Sentiment analysis 

Notes

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Compliance with ethical standards

Conflict of interest

None.

References

  1. 1.
    Hassan MK, Shakthi SP, Sasikala R (2017) Sentimental analysis of Amazon reviews using naïve bayes on laptop products with MongoDB and R. IOP Conf Ser Mater Sci Eng IOP Publ 263(4):042090CrossRefGoogle Scholar
  2. 2.
    Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: A deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp 513–520Google Scholar
  3. 3.
    Deng ZH, Luo KH, Yu HL (2014) A study of supervised term weighting scheme for sentiment analysis. Expert Syst Appl 41(7):3506–3513CrossRefGoogle Scholar
  4. 4.
    Fang X, Zhan J (2015) Sentiment analysis using product review data. J Big Data 2(1):5CrossRefGoogle Scholar
  5. 5.
    Pandey AC, Rajpoot DS, Saraswat M (2017) Twitter sentiment analysis using hybrid cuckoo search method. Inf Process Manag 53(4):764–779CrossRefGoogle Scholar
  6. 6.
    Ghiassi M, Lee S (2018) A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach. Expert Syst Appl 106:197–216CrossRefGoogle Scholar
  7. 7.
    Alharbi ASM, de Doncker E (2019) Twitter sentiment analysis with a deep neural network: an enhanced approach using user behavioral information. Cogn Syst Res 54:50–61CrossRefGoogle Scholar
  8. 8.
    Daniel M, Neves RF, Horta N (2017) Company event popularity for financial markets using Twitter and sentiment analysis. Expert Syst Appl 71:111–124CrossRefGoogle Scholar
  9. 9.
    Abid F, Alam M, Yasir M, Li C (2019) Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter. Future Gener Comput Syst 95:292–308CrossRefGoogle Scholar
  10. 10.
    Öztürk N, Ayvaz S (2018) Sentiment analysis on Twitter: a text mining approach to the Syrian refugee crisis. Telemat Inform 35(1):136–147CrossRefGoogle Scholar
  11. 11.
    Singh T, Kumari M (2016) Role of text pre-processing in twitter sentiment analysis. Proc Comput Sci 89:549–554CrossRefGoogle Scholar
  12. 12.
    Philander K, Zhong Y (2016) Twitter sentiment analysis: capturing sentiment from integrated resort tweets. Int J Hosp Manag 55:16–24CrossRefGoogle Scholar
  13. 13.
    Schumaker RP, Jarmoszko AT, Labedz CS Jr (2016) Predicting wins and spread in the Premier League using a sentiment analysis of twitter. Decis Support Syst 88:76–84CrossRefGoogle Scholar
  14. 14.
    Da Silva NF, Hruschka ER, Hruschka ER Jr (2014) Tweet sentiment analysis with classifier ensembles. Decis Support Syst 66:170–179CrossRefGoogle Scholar
  15. 15.
    Wang Y, Sun L, Wang J, Zheng Y, Youn HY (2017) A novel feature-based text classification improving the accuracy of twitter sentiment analysis. Advances in computer science and ubiquitous computing. Springer, Singapore, pp 440–445Google Scholar
  16. 16.
    Le B, Nguyen H (2015) Twitter sentiment analysis using machine learning techniques. Advanced computational methods for knowledge engineering. Springer, Cham, pp 279–289Google Scholar
  17. 17.
    Jalaja G, Kavitha C (2019) Sentiment analysis for text extracted from Twitter. Integrated intelligent computing, communication and security. Springer, Singapore, pp 693–700Google Scholar
  18. 18.
    Yang M, Qu Q, Chen X, Guo C, Shen Y, Lei K (2018) Feature-enhanced attention network for target-dependent sentiment classification. Neurocomputing 307:91–97CrossRefGoogle Scholar
  19. 19.
    Araque O, Corcuera-Platas I, Sanchez-Rada JF, Iglesias CA (2017) Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst Appl 77:236–246CrossRefGoogle Scholar
  20. 20.
    Giatsoglou M, Vozalis MG, Diamantaras K, Vakali A, Sarigiannidis G, Chatzisavvas KC (2017) Sentiment analysis leveraging emotions and word embeddings. Expert Syst Appl 69:214–224CrossRefGoogle Scholar
  21. 21.
    Alsinet T, Argelich J, Béjar R, Fernández C, Mateu C, Planes J (2018) An argumentative approach for discovering relevant opinions in Twitter with probabilistic valued relationships. Pattern Recogn Lett 105:191–199CrossRefGoogle Scholar
  22. 22.
    Balahur A, Perea-Ortega JM (2015) Sentiment analysis system adaptation for multilingual processing: the case of tweets. Inf Process Manag 51(4):547–556CrossRefGoogle Scholar
  23. 23.
    Bouazizi M, Ohtsuki T (2017) A pattern-based approach for multi-class sentiment analysis in twitter. IEEE Access 5:20617–20639CrossRefGoogle Scholar
  24. 24.
    Yu D, Xu D, Wang D, Ni Z (2019) Hierarchical topic modeling of Twitter data for online analytical processing. IEEE Access 7:12373–12385CrossRefGoogle Scholar
  25. 25.
    Bharathi S, Geetha A, Sathiynarayanan R (2017) Sentiment analysis of Twitter and RSS news feeds and its impact on stock market prediction. Int J Intell Eng Syst 10(6):68–77Google Scholar
  26. 26.
    Saif H, He Y, Fernandez M, Alani H (2016) Contextual semantics for sentiment analysis of Twitter. Inf Process Manage 52(1):5–19CrossRefGoogle Scholar
  27. 27.
    Ren Y, Wang R, Ji D (2016) A topic-enhanced word embedding for Twitter sentiment classification. Inf Sci 369:188–198CrossRefGoogle Scholar
  28. 28.
    Preethi PG, Uma V (2015) Temporal sentiment analysis and causal rules extraction from tweets for event prediction. Proc Comput Sci 48:84–89CrossRefGoogle Scholar
  29. 29.
    Kumar KA, Rajasimha N, Reddy M, Rajanarayana A, Nadgir K (2015) Analysis of users’ sentiments from kannada web documents. Proc Comput Sci 54:247–256CrossRefGoogle Scholar
  30. 30.
    Amolik A, Jivane N, Bhandari M, Venkatesan M (2016) Twitter sentiment analysis of movie reviews using machine learning techniques. Int J Eng Technol 7(6):1–7Google Scholar
  31. 31.
    Cambria E, Poria S, Gelbukh A, Thelwall M (2017) Sentiment analysis is a big suitcase. IEEE Intell Syst 32(6):74–80CrossRefGoogle Scholar
  32. 32.
    Ghorbel H, Jacot D (2011) Sentiment analysis of French movie reviews. Advances in distributed agent-based retrieval tools. Springer, Berlin, pp 97–108Google Scholar
  33. 33.
    Boyd-Graber J, Resnik P (2010) Holistic sentiment analysis across languages: Multilingual supervised latent Dirichlet allocation. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp 45–55Google Scholar
  34. 34.
    Colace F, De Santo M, Greco L, Moscato V, Picariello A (2016) Probabilistic approaches for sentiment analysis: latent dirichlet allocation for ontology building and sentiment extraction. Sentiment analysis and ontology engineering. Springer, Cham, pp 75–91Google Scholar
  35. 35.
    Patel OP, Bharill N, Tiwari A (2015) A quantum-inspired fuzzy based evolutionary algorithm for data clustering. In: IEEE International Conference on FUZZY SYSTEMS (FUZZ-IEEE), pp 1–8Google Scholar
  36. 36.
    Chakhmakhchyan L, Cerf NJ, Garcia-Patron R (2017) Quantum-inspired algorithm for estimating the permanent of positive semi definite matrices. Phys Rev A 96(2):022329CrossRefGoogle Scholar
  37. 37.
    Trupthi M, Pabboju S, Narsimha G (2018) Possibilistic fuzzy c-means topic modelling for twitter sentiment analysis. Int J Intell Eng Syst 11(3):100–108Google Scholar
  38. 38.
    Alayba AM, Palade V, England M, Iqbal R (2018) A combined CNN and LSTM model for arabic sentiment analysis. In: International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Springer, Cham, pp 179–191Google Scholar
  39. 39.
    Han H, Zhang Y, Zhang J, Yang J, Zou X (2018) Improving the performance of lexicon-based review sentiment analysis method by reducing additional introduced sentiment bias. PLOS One 13(8):e0202523CrossRefGoogle Scholar
  40. 40.
    Liu B, Blasch E, Chen Y, Shen D, Chen G (2013) Scalable sentiment classification for big data analysis using naive bayes classifier. In 2013 IEEE International Conference on Big Data, pp 99–104Google Scholar
  41. 41.
    Rain C (2013) Sentiment analysis in amazon reviews using probabilistic machine learning, Swarthmore CollegeGoogle Scholar
  42. 42.
    Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans Knowl Data Eng 23(10):1498–1512CrossRefGoogle Scholar
  43. 43.
    Balles L, Hennig P (2017) Dissecting adam: the sign, magnitude and variance of stochastic gradients. arXiv preprint arXiv:1705.07774

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Trupthi Mandhula
    • 1
    Email author
  • Suresh Pabboju
    • 2
  • Narsimha Gugulotu
    • 3
  1. 1.Information TechnologyChaitanya Bharathi Institute of TechnologyHyderabadIndia
  2. 2.Deptartment of Information TechnologyCBITGandipet, HyderabadIndia
  3. 3.Computer Science Engineering, College of EngineeringJNTUHHyderabadIndia

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