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An innovative word embedded and optimization based hybrid artificial intelligence approach for aspect-based sentiment analysis of app and cellphone reviews

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Abstract

A one-grained problem in Natural Language Processing (NLP), "Aspect-Based Sentiment Analysis (ABSA)" seeks to predict the sentiment polarity of several features in a sentence or document. The majority of the present research concentrates on the relationship between a given context and an aspect sentiment score. Inadequate attention has been paid to the significant deep relationships between the global context and aspect sentiment polarity. In this article, a novel word-embedded and optimization-based hybrid artificial intelligence (AI) method is proposed for ABSA of different customer review datasets. The review dataset was gathered in the initial stage using a web scraping algorithm. Here, the analysis is validated with the help of Flipkart Cell Phone Reviews and the ABSA Warehouse of App Reviews (AWARE) dataset. Using a pre-processing strategy, the raw data is improved as informative data. Additionally, the Convolutional Neural Attentive Bag-of-Entities (CNABE) of pre-trained word embedding is proposed, which provides the most efficient feature engineering and effectively preprocesses the words/characters for enhanced representation. Then, the Remora Optimization Based Extreme Action Selection Gradient Boosting (RO-EASGB) algorithm is proposed for sentiment analysis classification using the benchmark datasets. The implementation of this research is done using Python software. The performance of the proposed method is compared with the existing methods in terms of accuracy, recall, precision, F1-measure, and so on. Based on the experimental outcomes, the research shows that the proposed approaches outperform the existing state of the art methods.

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References

  1. Yang L et al (2020) Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access 8:23522–23530

    Article  Google Scholar 

  2. Xu G et al (2019) Sentiment analysis of comment texts based on BiLSTM. IEEE Access 7:51522–51532

    Article  Google Scholar 

  3. Jiang Q, Chen L, Xu R, Ao X, Yang M (2019) A challenge dataset and effective models for aspect-based sentiment analysis. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) (pp. 6280–6285) Hong Kong, China. Association for Computational Linguistics

    Google Scholar 

  4. D’Aniello G, Gaeta M, La Rocca I (2022) KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis. Artif Intell Rev 55:5543–5574

    Article  Google Scholar 

  5. Tian Y, Chen G, Song Y (2021) Aspect-based sentiment analysis with type-aware graph convolutional networks and layer ensemble. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies. Association for Computational Linguistics, pp 2910–2922

    Google Scholar 

  6. Li N, Chow C-Y, Zhang J-D (2020) SEML: A semi-supervised multi-task learning framework for aspect-based sentiment analysis. IEEE Access 8:189287–189297

    Article  Google Scholar 

  7. D’Aniello G, Gaeta M, La Rocca I (2022) KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis. Artif Intell Rev:1–32

  8. Nikolić N, Grljević O, Kovačević A (2020) Aspect-based sentiment analysis of reviews in the domain of higher education. The Electronic Library 38(1):44–64. https://doi.org/10.1108/EL-06-2019-0140

    Article  Google Scholar 

  9. Sun C, Huang L, Qiu X (2019) Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. arXiv preprint arXiv:1903.09588. https://doi.org/10.48550/arXiv.1903.09588

  10. Veyseh APB, Nour N, Dernoncourt F, Tran QH, Dou D, Nguyen TH (2020) Improving aspect-based sentiment analysis with gated graph convolutional networks and syntax-based regulation. arXiv preprint arXiv:2010.13389

  11. Karimi A, Rossi L, Prati A (2021) Adversarial training for aspect-based sentiment analysis with BERT. In: 2020 25th international conference on pattern recognition (ICPR). IEEE

    Google Scholar 

  12. Al-Ghuribi SM, Noah SAM, Tiun S (2020) Unsupervised semantic approach of aspect-based sentiment analysis for large-scale user reviews. IEEE Access 8:218592–218613

    Article  Google Scholar 

  13. Manik LP, Mustika HF, Akbar Z, Kartika YA, Saleh DR, Setiawan FA, Satya IA (2020) Aspect-based sentiment analysis on candidate character traits in Indonesian presidential election. In: 2020 international conference on radar, antenna, microwave, electronics, and telecommunications (ICRAMET), vol 845. IEEE, pp 224–228

  14. Ashi MM, Siddiqui MA, Nadeem F (2019) Pre-trained word embeddings for Arabic aspect-based sentiment analysis of airline tweets. In: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics (AISC, volume 845) Aug 2018 4, pp. 241-251. Springer International Publishing

    Google Scholar 

  15. Karimi A, Rossi L, Prati A (2020) Improving bert performance for aspect-based sentiment analysis. arXiv preprint arXiv:2010.11731

  16. Liu N et al (2019) Attention-based Sentiment Reasoner for aspect-based sentiment analysis. Hum-centric Comput Inf Sci 9(1):1–17

    Article  Google Scholar 

  17. Jang H, Rempel E, Roth D, Carenini G, Janjua NZ (2021) Tracking COVID-19 discourse on twitter in North America: Infodemiology study using topic modeling and aspect-based sentiment analysis. J Med Internet Res 23(2):e25431

    Article  PubMed  PubMed Central  Google Scholar 

  18. Liu J, Zhong Q, Ding L, Jin H, Bo D, Tao D (2023) Unified instance and knowledge alignment pretraining for aspect-based sentiment analysis. IEEE/ACM transactions on audio, speech, and language processing:2629–2642

  19. Wang J, Bingxin X, Yujie Z (2021) Deep learning for aspect-based sentiment analysis. In: 2021 international conference on machine learning and intelligent systems engineering (MLISE). IEEE, pp 267–271

    Chapter  Google Scholar 

  20. Cahyadi A, Khodra ML (2018) Aspect-based sentiment analysis using convolutional neural network and bidirectional long short-term memory. In: 2018 5th international conference on advanced informatics: concept theory and applications (ICAICTA). IEEE

    Google Scholar 

  21. Bie Y, Yang Y (2021) A multitask multiview neural network for end-to-end aspect-based sentiment analysis. Big Data Min Anal 4(3):195–207

    Article  Google Scholar 

  22. Liu N, Shen B (2020) ReMemNN: A novel memory neural network for powerful interaction in aspect-based sentiment analysis. Neurocomputing 395:66–77

    Article  Google Scholar 

  23. Chakraborty A (2022) Aspect based sentiment analysis using spectral temporal graph neural network. arXiv preprint arXiv:2202.06776

  24. Truşcǎ MM, Wassenberg D, Frasincar F, Dekker R (2020) A hybrid approach for aspect-based sentiment analysis using deep contextual word embeddings and hierarchical attention. In: Web Engineering: 20th International Conference, ICWE 2020, Helsinki, Finland, June 9–12, 2020, Proceedings 20, pp. 365-380 (LNISA, volume 12128). Springer International Publishing

    Google Scholar 

  25. Sai Aparna T, Simran K, Premjith B, Soman KP (2021) Aspect-based sentiment analysis in Hindi: comparison of machine/deep learning algorithms. In: Inventive computation and information technologies: Proceedings of ICICIT 2020. Springer, Singapore, pp 81–91

    Chapter  Google Scholar 

  26. He K et al (2022) Meta-based self-training and re-weighting for aspect-based sentiment analysis. IEEE Trans Affect Comput

  27. Abdelgwad MM, Soliman THA, Taloba AI, Farghaly MF (2022) Arabic aspect based sentiment analysis using bidirectional GRU based models. J King Saud Univ - Comput Inf Sci 34(9):6652–6662

    Google Scholar 

  28. Gräßer F et al (2018) Aspect-based sentiment analysis of drug reviews applying cross-domain and cross-data learning. In: Proceedings of the 2018 international conference on digital health

    Google Scholar 

  29. Kumar R, Pannu HS, Malhi AK (2020) Aspect-based sentiment analysis using deep networks and stochastic optimization. Neural Comput Appl 32(8):3221–3235

    Article  Google Scholar 

  30. Al-Smadi M, Qawasmeh O, Al-Ayyoub M, Jararweh Y, Gupta B (2018) Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. J Comput Sci 27:386–393

    Article  Google Scholar 

  31. Alagarsamy S, James V, Raj RSP (2022) An experimental analysis of optimal hybrid word embedding methods for text classification using a movie review dataset. Braz Arch Biol Techn 65

  32. Janjua SH et al (2021) Multi-level aspect based sentiment classification of Twitter data: using hybrid approach in deep learning. PeerJ Comput Sci 7:e433

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zhao N et al (2021) Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis. IEEE Access 9:15561–15569

    Article  Google Scholar 

  34. Datta S, Chakrabarti S (2021) Aspect based sentiment analysis for demonetization tweets by optimized recurrent neural network using fire fly-oriented multi-verse optimizer. Sādhanā 46(79)

  35. Liu N, Shen B (2020) ReMemNN: A novel memory neural network for powerful interaction in aspect-based sentiment analysis. Neurocomputing 395:66–77

    Article  Google Scholar 

  36. Ghosal S, Jain A (2022) Weighted aspect based sentiment analysis using extended OWA operators and Word2Vec for tourism. Multimed Tools Appl:1–28

  37. Alotaibi FS, Gupta V (2022) Sentiment analysis system using hybrid word embeddings with convolutional recurrent neural network. Int Arab J Inf Technol 19(3):330–335

    Google Scholar 

  38. Ishaq A, Asghar S, Gillani SA (2020) Aspect-based sentiment analysis using a hybridized approach based on CNN and GA. IEEE Access 8:135499–135512

    Article  Google Scholar 

  39. Kaur G, Sharma A (2023) A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis. J Big Data 10(1):5

    Article  PubMed  PubMed Central  Google Scholar 

  40. Truşcǎ MM, Frasincar F (2023) Survey on aspect detection for aspect-based sentiment analysis. Artif Intell Rev 56(5):3797–3846

    Article  Google Scholar 

  41. Chauhan GS, Nahta R, Meena YK, Gopalani D (2023) Aspect based sentiment analysis using deep learning approaches: A survey. Comput Sci Rev 49:100576

    Article  MathSciNet  Google Scholar 

  42. Mowlaei ME, Abadeh MS, Keshavarz H (2020) Aspect-based sentiment analysis using adaptive aspect-based lexicons. Expert Syst Appl 148:113234

    Article  Google Scholar 

  43. D’Aniello G, Gaeta M, La Rocca I (2022) KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis. Artif Intell Rev 55(7):5543–5574

    Article  Google Scholar 

  44. Hammi S, Hammami SM, Belguith LH (2023) Advancing aspect-based sentiment analysis with a novel architecture combining deep learning models CNN and bi-RNN with the machine learning model SVM. Soc Netw Anal Min 13(1):117

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering, GMR Institute of Technology, Razam, Andhra Pradesh, 532127, India

    N. Lakshmi Devi & Srividya Kotagiri

  2. Electronics and Communication Engineering, GMR Institute of Technology, Razam, Andhra Pradesh, 532127, India

    B. Anilkumar

  3. Department of CS&SE, Andhra University College of Engineering (A), Visakhapatnam, Andhra Pradesh, 530003, India

    A. Mary Sowjanya

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  1. N. Lakshmi Devi
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  2. B. Anilkumar
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  3. A. Mary Sowjanya
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  4. Srividya Kotagiri
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Correspondence to N. Lakshmi Devi.

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Devi, N.L., Anilkumar, B., Sowjanya, A.M. et al. An innovative word embedded and optimization based hybrid artificial intelligence approach for aspect-based sentiment analysis of app and cellphone reviews. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18510-7

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  • DOI: https://doi.org/10.1007/s11042-024-18510-7

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