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.
Similar content being viewed by others
References
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):042090
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 513–520
Deng ZH, Luo KH, Yu HL (2014) A study of supervised term weighting scheme for sentiment analysis. Expert Syst Appl 41(7):3506–3513
Fang X, Zhan J (2015) Sentiment analysis using product review data. J Big Data 2(1):5
Pandey AC, Rajpoot DS, Saraswat M (2017) Twitter sentiment analysis using hybrid cuckoo search method. Inf Process Manag 53(4):764–779
Ghiassi M, Lee S (2018) A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach. Expert Syst Appl 106:197–216
Alharbi ASM, de Doncker E (2019) Twitter sentiment analysis with a deep neural network: an enhanced approach using user behavioral information. Cogn Syst Res 54:50–61
Daniel M, Neves RF, Horta N (2017) Company event popularity for financial markets using Twitter and sentiment analysis. Expert Syst Appl 71:111–124
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 95:292–308
Öztürk N, Ayvaz S (2018) Sentiment analysis on Twitter: a text mining approach to the Syrian refugee crisis. Telemat Inform 35(1):136–147
Singh T, Kumari M (2016) Role of text pre-processing in twitter sentiment analysis. Proc Comput Sci 89:549–554
Philander K, Zhong Y (2016) Twitter sentiment analysis: capturing sentiment from integrated resort tweets. Int J Hosp Manag 55:16–24
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 88:76–84
Da Silva NF, Hruschka ER, Hruschka ER Jr (2014) Tweet sentiment analysis with classifier ensembles. Decis Support Syst 66:170–179
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 440–445
Le B, Nguyen H (2015) Twitter sentiment analysis using machine learning techniques. Advanced computational methods for knowledge engineering. Springer, Cham, pp 279–289
Jalaja G, Kavitha C (2019) Sentiment analysis for text extracted from Twitter. Integrated intelligent computing, communication and security. Springer, Singapore, pp 693–700
Yang M, Qu Q, Chen X, Guo C, Shen Y, Lei K (2018) Feature-enhanced attention network for target-dependent sentiment classification. Neurocomputing 307:91–97
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 77:236–246
Giatsoglou M, Vozalis MG, Diamantaras K, Vakali A, Sarigiannidis G, Chatzisavvas KC (2017) Sentiment analysis leveraging emotions and word embeddings. Expert Syst Appl 69:214–224
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 105:191–199
Balahur A, Perea-Ortega JM (2015) Sentiment analysis system adaptation for multilingual processing: the case of tweets. Inf Process Manag 51(4):547–556
Bouazizi M, Ohtsuki T (2017) A pattern-based approach for multi-class sentiment analysis in twitter. IEEE Access 5:20617–20639
Yu D, Xu D, Wang D, Ni Z (2019) Hierarchical topic modeling of Twitter data for online analytical processing. IEEE Access 7:12373–12385
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):68–77
Saif H, He Y, Fernandez M, Alani H (2016) Contextual semantics for sentiment analysis of Twitter. Inf Process Manage 52(1):5–19
Ren Y, Wang R, Ji D (2016) A topic-enhanced word embedding for Twitter sentiment classification. Inf Sci 369:188–198
Preethi PG, Uma V (2015) Temporal sentiment analysis and causal rules extraction from tweets for event prediction. Proc Comput Sci 48:84–89
Kumar KA, Rajasimha N, Reddy M, Rajanarayana A, Nadgir K (2015) Analysis of users’ sentiments from kannada web documents. Proc Comput Sci 54:247–256
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):1–7
Cambria E, Poria S, Gelbukh A, Thelwall M (2017) Sentiment analysis is a big suitcase. IEEE Intell Syst 32(6):74–80
Ghorbel H, Jacot D (2011) Sentiment analysis of French movie reviews. Advances in distributed agent-based retrieval tools. Springer, Berlin, pp 97–108
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 45–55
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 75–91
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 1–8
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):022329
Trupthi M, Pabboju S, Narsimha G (2018) Possibilistic fuzzy c-means topic modelling for twitter sentiment analysis. Int J Intell Eng Syst 11(3):100–108
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 179–191
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):e0202523
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 99–104
Rain C (2013) Sentiment analysis in amazon reviews using probabilistic machine learning, Swarthmore College
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):1498–1512
Balles L, Hennig P (2017) Dissecting adam: the sign, magnitude and variance of stochastic gradients. arXiv preprint arXiv:1705.07774
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
None.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mandhula, T., Pabboju, S. & Gugulotu, N. Predicting the customer’s opinion on amazon products using selective memory architecture-based convolutional neural network. J Supercomput 76, 5923–5947 (2020). https://doi.org/10.1007/s11227-019-03081-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-019-03081-4