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Bi-GRU Model with Stacked Embedding for Sentiment Analysis: A Case Study

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Internet of Things, Artificial Intelligence and Blockchain Technology

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Abstract

It is very important to understand the opinion and demand of customers in different market places, particularly when it comes to e-market places. As a result of the pandemic, more customers rely on e-commerce platforms for their purchase, understand their sentiments, and provide them with good service which has been a core challenge for e-commerce applications. However, the magnanimity of the data makes it very difficult for human beings to solve this problem without the help of computers. One solution to this problem is to make use of sentiment analysis for consumer feedback. In the last one and a half decades, scientific communities, academies, and public and business sectors have been trying hard on sentiment analysis, also known as opinion mining. In this regard, this paper presents a full picture of sentiment analysis techniques such as polarity-based process, long short-term memory (LSTM), and gated recurrent unit (GRU) with convolution-based neural networks (CNN) models. At the last, case study is provided on Amazon product feedback dataset using stacked embedding with bidirectional GRU model.

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References

  • Abid, F., Alam, M., Yasir, M., & Li, C. (2014, June). Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter. Future Generation Computer Systems, 95, 292–308.

    Google Scholar 

  • Agarwal, B., Poria, S., Mittal, N., Gelbukh, A., & Hussain, A. (2015). Concept-level sentiment analysis with dependency-based semantic parsing: A novel approach. Cognitive Computation, 7(4), 487–499.

    Article  Google Scholar 

  • Ashhar, Z.Q., Khan, A., Ahmad, S., Qasim, M., & Khan, I. A. (2017). Lexiconenhanced sentiment analysis framework using rule-based classification scheme. PLoS One, 12(2), Art.no. e0171649.

    Article  Google Scholar 

  • Bandhakavi, A., Wiratunga, N., Padmanabhan, D., & Massie, S. (2017, July). Lexicon based feature extraction for emotion text classification. Pattern Recognition Letters, 93, 133–142.

    Google Scholar 

  • Chen, X., Xue, Y., Zhao, H. Y., Lu, X., Hu, X. H., & Ma, Z. H. (2018a). A novel feature extraction methodology for sentiment analysis of product reviews. Neural Computing and Applications. https://doi.org/10.1007/s00521-00018-03477-00522

  • Chen, H., Li, S., Wu, P., Yi, N., Li, S., & Huang, X. (2018b). Fine-grained sentiment analysis of Chinese reviews using LSTM network. Journal of Engineering Science and Technology Review, 11(1), 174–179.

    Article  Google Scholar 

  • Chitkara, P., Modi, A., Avvaru, P., Janghorbani, S., & Kapadia, M. (2019, April). Topic spotting using hierarchical networks with self attention. arXiv

    Google Scholar 

  • Dhaoui, C., Webster, C. M., & Tan, L. P. (2017, September). Social media sentiment analysis: Lexicon versus machine learning. Journal of Consumer Marketing, 34(6), 480–488.

    Google Scholar 

  • Graham, J. w. (2009). Missing data analysis: making it work in the real world. Annual Review of Psychology, 60(2009): 549–576.

    Google Scholar 

  • Feng, S., Song, K., Wang, D., & Yu, G. (2015, July). A word-emoticon mutual reinforcement ranking model for building sentiment lexicon from massive collection of microblogs. World Wide Web, 18(4), 949–967.

    Google Scholar 

  • Gao, B., Hu, N., & Bose, I. (2017). Follow the herd or be myself? An analysis of consistency in behavior of reviewers and helpfulness of their reviews. Decision Support Systems, 95, 1–11.

    Article  Google Scholar 

  • Gavilan, D., Avello, M., & Martinez-Navarro, G. (2018). The influence of online ratings and reviews on hotel booking consideration. Tourism Management, 66, 53–61.

    Article  Google Scholar 

  • Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent Dirichlet allocation. Tourism Management, 59, 467–483.

    Article  Google Scholar 

  • Hagenau, M., Liebmann, M., & Neumann, D. (2013). Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems.

    Google Scholar 

  • Hai, Z., Cong, G., Chang, K., Cheng, P., & Miao, C. (2017, June). Analyzing sentiments in one go: A supervised joint topic modeling approach. IEEE Transactions on Knowledge and Data Engineering, 29(6), 1172–1185.

    Google Scholar 

  • Hochreiter, S., & Schmidhuber, J. (2017). Long short-term memory. Neural Computation, 9(8), 1735–1780, 3497.

    Article  Google Scholar 

  • Hu, F., Li, L., Zhang, Z.-L., Wang, J.-Y., & Xu, X.-F. (2017, July). Emphasizing essential words for sentiment classification based on recurrent neural networks. Journal of Computer Science and Technology, 32(4), 785–795.

    Google Scholar 

  • Huq, M. R., Ali, A., & Rahman, A. (2017). Sentiment analysis on Twitter data using KNN and SVM. International Journal of Advanced Computer Science and Applications, 8(6), 34–25.

    Google Scholar 

  • Jianqiang, Z., Xiaolin, G., & Xuejun, Z. (2018). Deep convolution neural networks for twitter sentiment analysis. IEEE Access, 6, 23253–23260.

    Article  Google Scholar 

  • Jurek, A., Mulvenna, M. D., & Bi, Y. (2015). Improved lexicon-based sentiment analysis for social media analytics. Security Informatics, 4(1), 9.

    Article  Google Scholar 

  • Keshavarz, H., & Abadeh, M. S. (2017, April). ALGA: Adaptive lexicon learning using genetic algorithm for sentiment analysis of micro blogs. Knowledge-Based Systems, 122, 1–16.

    Google Scholar 

  • Khoo, C. S., & Johnkhan, S. B. (2018, August). Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons. Journal of Information Science, 44(4), 491–511

    Google Scholar 

  • Kim, K., Aminanto, M. E., & Tanuwidjaja, H. C. (2018). Deep Learning (pp. 27–34). Springer.

    Google Scholar 

  • La’ercio Dias. (2018). Using text analysis to quantify the similarity and evolution of scientific disciplines. Royal Society.

    Google Scholar 

  • Liang, R., & Wang, J. Q. (2019, April). Alinguistic intuitionistic cloud decision support model with sentiment analysis for product selection in E-commerce. International Journal of Fuzzy Systems, 21(3), 963–977.

    Google Scholar 

  • Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5.1 (2012): 1-167.

    Google Scholar 

  • Liu, Y., Bi, J. W., & Fan, Z. P. (2017). Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Information Fusion, 36, 149–161.

    Article  Google Scholar 

  • Long, W., Tang, Y.-R., & Tian, Y.-J. (2018, July). Investor sentiment identification based on the Universum SVM. Neural Computing and Applications, 30(2), 661–670

    Google Scholar 

  • Loughran, T., & Mcdonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-ks. The Journal of Finance, 66(1), 35–65.

    Article  Google Scholar 

  • Ma, Y., Peng, H., Khan, T., Cambria, E., & Hussain, A. (2018, August). Sentic LSTM: A hybrid network for targeted aspect-based sentiment analysis. Cognitive Computation, 10(4), 639–650.

    Google Scholar 

  • Manek, A. S., Shenoy, P. D., Mohan, M. C., & Venugopal, K. (2017, March). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 20(2), 135–154.

    Google Scholar 

  • Ramasamy, L. K., Kadry, S., & Lim, S. (2021a). Selection of optimal hyper-parameter values of support vector machine for sentiment analysis tasks using nature-inspired optimization methods. Bulletin of Electrical Engineering and Informatics, 10(1), 290–298.

    Article  Google Scholar 

  • Ramasamy, L. K., et al. (2021b). Performance analysis of sentiments in twitter dataset using SVM models. International Journal of Electrical & Computer Engineering, 11(3), 2088–8708.

    Google Scholar 

  • Sarawgi, Kajal, and Vandana Pathak. “Opinion mining: aspect level sentiment analysis using SentiWordNet and Amazon web services.” Int. J. Comput. Appl 158.6 (2017): 0975-8887”.

    Google Scholar 

  • Singh, J., Singh, G., & Singh, R. (2017). Optimization of sentiment analysis using machine learning classifiers. Human-Centric Computing and Information Sciences, 7(1), 32.

    Article  Google Scholar 

  • Xia, Y., Cambria, E., Hussain, A., & Zhao, H. (2014). Word polarity disambiguation using bayesian model and opinion-level features. Cognitive Computation, 7(3), 369–380.

    Article  Google Scholar 

  • Xing, F., Cambria, E., & Welsch, R. (2018). Natural language based financial forecasting: A survey. Artificial Intelligence Review. https://doi.org/10.1007/s10462-017-9588-9

  • Xu, T., Qinke, P., & Cheng, Y. (2012). Identifying the semantic orientation of terms using S-HAL for sentiment analysis. Knowledge-Based Systems, 35, 279–289.

    Article  Google Scholar 

  • Yang, L., Wang, J., Tang, Z., & Xiong, N. N. (2019). Using conditional random fields to optimize a self-adaptive Bell-LaPadula model in control systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems. to be published.

    Google Scholar 

  • Yu Liang-Chih, Wu Jheng-Long, Chang Pei-Chann, & Chu Hsuan- Shou. (2013). Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news. Knowledge-Based Systems, 41, 89–97.

    Article  Google Scholar 

  • Zeng, D., Dai, Y., Li, F., Sherratt, R., & Wang, J. (2018). Adversarial learning for distant supervised relation extraction. Computers, Materials & Continua, 55(1), 121–136.

    Google Scholar 

  • Zhang, B. J., Ye, Q., & Li, Y. J. (2010). Literature review on sentiment analysis of online product reviews. Journal of Management Sciences in China, 13(6), 84–96.

    Google Scholar 

  • Zhang, S., Wei, Z., Wang, Y., & Liao, T. (2018, April). Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary. Future Generation Computer Systems, 81, 395–403.

    Google Scholar 

  • Zhou, F., Jiao, J. R., Yang, X. J., & Lei, B. (2017). Augmenting feature model through customer preference mining by hybrid sentiment analysis. Expert Systems with Applications, 89, 306–317.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Angadi Institute of Technology and Management (AITM), Belagavi, Karnataka, India

    Sanjana Kavatagi

  2. Dean R&D, Angadi Institute of Technology and Management (AITM), Belagavi, Karnataka, India

    Vinayak Adimule

Authors
  1. Sanjana Kavatagi
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  2. Vinayak Adimule
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Editor information

Editors and Affiliations

  1. Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India

    R.Lakshmana Kumar

  2. Sheffield University Management School, The University of Sheffield, Sheffield, UK

    Yichuan Wang

  3. School of Computing Science and Engineering, Galgotias University, Delhi-NCR, India

    T. Poongodi

  4. University of Lagos, Lagos, Nigeria

    Agbotiname Lucky Imoize

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Kavatagi, S., Adimule, V. (2021). Bi-GRU Model with Stacked Embedding for Sentiment Analysis: A Case Study. In: Kumar, R., Wang, Y., Poongodi, T., Imoize, A.L. (eds) Internet of Things, Artificial Intelligence and Blockchain Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-74150-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-74150-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-74149-5

  • Online ISBN: 978-3-030-74150-1

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