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Advancing aspect-based sentiment analysis with a novel architecture combining deep learning models CNN and bi-RNN with the machine learning model SVM

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Abstract

Over the last decades, the aspect-based sentiment analysis (ABSA) task has been given great attention and has been deeply studied by the scientific community. It was first introduced in 2002 to extract the users’ fine-grained sentiments from textual data by focusing on aspect terms. In this paper, we propose a machine learning-based architecture called CBRS (CNN-Bi-RNN-SVM) to enhance the ABSA of smartphone reviews. This architecture combines two deep learning models [convolutional neural network (CNN) and bidirectional recurrent neural network (Bi-RNN)] with the classical machine learning model support vector machine (SVM). The CNN and the Bi-RNN models are used to capture both local features and contextual information. The SVM model is applied to classify the sentiments, expressed towards aspect terms, as positive or negative. To evaluate the performance of the developed architecture, 8,000 French smartphone reviews, extracted from the Amazon website, are annotated to create a dataset including 15,411 positive aspects and 14,627 negative aspects. The obtained findings corroborated the efficiency of the designed architecture by achieving an F-measure value of 94.05%, for the smartphone dataset, and 85.70% for the SemEval-2016 restaurant dataset. A comparative study demonstrates that the overall performance of our proposed architecture outperformed that of the existing ABSA models.

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References

  • Akhtar MS, Kohail S, Kumar A, Ekbal A, Biemann C (2017) Feature selection using multi-objective optimization for aspect based sentiment analysis. In: Natural language processing and information systems: 22nd international conference on applications of natural language to information systems, NLDB 2017, Liège, Belgium, June 21--23, 2017, Proceedings 22, pp. 15–27. Springer

  • Akhtar MS, Kumar A, Ekbal A, Biemann C, Bhattacharyya P (2019) Language-agnostic model for aspect-based sentiment analysis. In: Proceedings of the 13th international conference on computational semantics-long papers, pp 154–164

  • Al-Smadi M, Al-Ayyoub M, Jararweh Y, Qawasmeh O (2019) Enhancing aspect-based sentiment analysis of arabic hotels’ reviews using morphological, syntactic and semantic features. Inf Proces Manag 56(2):308–319

    Article  Google Scholar 

  • AlAjlan SA, Saudagar AKJ (2021) Machine learning approach for threat detection on social media posts containing arabic text. Evol Intell 14(2):811–822

    Article  Google Scholar 

  • Alharbi O (2021) A deep learning approach combining cnn and bi-lstm with svm classifier for arabic sentiment analysis. Int J Adv Comput Sci Appl 12(6)

  • Alqaryouti O, Siyam N, Abdel Monem A, Shaalan K (2020) Aspect-based sentiment analysis using smart government review data. Appl Comput Inf

  • Althobaiti M, Kruschwitz U, Poesio M Aranlp (2014) A java-based library for the processing of arabic text. In: Proceedings of the 9th international conference on language resources and evaluation, LREC 2014, pp 4134–4138. European Language Resources Association (ELRA)

  • Angiani G, Ferrari L, Fontanini T, Fornacciari P, Iotti E, Magliani F, Manicardi S (2016) A comparison between preprocessing techniques for sentiment analysis in twitter. In: KDWeb

  • Aubaid AM, Mishra A (2020) A rule-based approach to embedding techniques for text document classification. Appl Sci 10(11):4009

    Article  Google Scholar 

  • Ayetiran EF (2022) Attention-based aspect sentiment classification using enhanced learning through cnn-bilstm networks. Knowl Based Syst 252:109409

    Article  Google Scholar 

  • Banjar A, Ahmed Z, Daud A, Abbasi RA, Dawood H (2021) Aspect-based sentiment analysis for polarity estimation of customer reviews on twitter. Comput Mater Continua 67(2):2203–2225

    Article  Google Scholar 

  • Brauwers G, Frasincar F (2022) A survey on aspect-based sentiment classification. ACM Comput Surv 55(4):1–37

    Article  Google Scholar 

  • Brun C, Perez J, Roux C (2016) Xrce at semeval-2016 task 5: Feedbacked ensemble modeling on syntactico-semantic knowledge for aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 277–281

  • Brun C (2018) Transfert de ressources sémantiques pour l’analyse de sentiments au niveau des aspects. In: Actes de la Conférence Traitement Automatique de la Langue Naturelle, TALN 2018, p 547

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    Article  Google Scholar 

  • Deligiannidis S, Mesaritakis C, Bogris A (2021) Performance and complexity analysis of bi-directional recurrent neural network models versus volterra nonlinear equalizers in digital coherent systems. J Lightwave Technol 39(18):5791–5798

    Article  Google Scholar 

  • Ellouze M, Hadrich L (2022) A hybrid approach for the detection and monitoring of people having personality disorders on social networks. Social Netw Anal Min 12(1):67

    Article  Google Scholar 

  • Essebbar A, Kane B, Guinaudeau O, Chiesa V, Quénel I, Chau S (2021) Aspect based sentiment analysis using french pre-trained models. In: ICAART (1), pp 519–525

  • Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202

    Article  Google Scholar 

  • García-Pablos A, Cuadros M, Rigau G (2018) W2vlda: almost unsupervised system for aspect based sentiment analysis. Exp Syst Appl 91:127–137

    Article  Google Scholar 

  • Graves A (2013) Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850

  • Grewal D, Roggeveen A, Sisodia R, Nordfält J (2017) Enhancing customer engagement through consciousness. J Retail 93(1):55–64

    Article  Google Scholar 

  • Hamdan H, Bellot P, Bechet F Lsislif (2015) Crf and logistic regression for opinion target extraction and sentiment polarity analysis. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), pp 753–758

  • Hammi S, Hammami SM, Belguith LH (2022) Aspect term extraction improvement based on a hybrid method. In: International symposium on methodologies for intelligent systems, pp 85–94 Springer

  • Hammi S, Hammami SM, Belguith LH (2022) An improved hybrid method for sentiment analysis. In: 2022 International conference on innovations in intelligent systems and applications (INISTA), pp 1–6. IEEE

  • Hidaka A, Kurita T (2017) Consecutive dimensionality reduction by canonical correlation analysis for visualization of convolutional neural networks. In: Proceedings of the ISCIE international symposium on stochastic systems theory and its applications, vol 2017, pp 160–167

  • Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp 168–177

  • Jagtap V, Pawar K (2013) Analysis of different approaches to sentence-level sentiment classification. Int J Sci Eng Technol 2(3):164–170

    Google Scholar 

  • Jivani AG et al (2011) A comparative study of stemming algorithms. Int J Comp Tech Appl 2(6):1930–1938

    Google Scholar 

  • Kumar V, Sundaram S (2022) Offline text-independent writer identification based on word level data. arXiv preprint arXiv:2202.10207

  • Kumar A, Kohail S, Kumar A, Ekbal A, Biemann C (2016) Iit-tuda at semeval-2016 task 5: Beyond sentiment lexicon: combining domain dependency and distributional semantics features for aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 1129–1135

  • Liang B, Su H, Gui L, Cambria E, Xu R (2022) Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl Based Syst 235:107643

    Article  Google Scholar 

  • Liu C, Ta V, Le N, Tadesse DA, Shi C (2022) Deep neural network framework based on word embedding for protein glutarylation sites prediction. Life 12(8):1213

    Article  Google Scholar 

  • Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113

    Article  Google Scholar 

  • Moghaddam, S., Ester, M.: Opinion digger: an unsupervised opinion miner from unstructured product reviews. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1825–1828 (2010)

  • Mubarok MS, Adiwijaya A, Aldhi MD (2017) Aspect-based sentiment analysis to review products using naïve bayes. In: AIP conference proceedings, vol 1867

  • Nasr L, Burton J, Gruber T, Kitshoff J (2014) Exploring the impact of customer feedback on the well-being of service entities: a tsr perspective. J Serv Manage 25(4):531–555

    Article  Google Scholar 

  • Palomino MA, Aider F (2022) Evaluating the effectiveness of text pre-processing in sentiment analysis. Appl Sci 12(17):8765

    Article  Google Scholar 

  • Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? sentiment classification using machine learning techniques. arXiv preprint cs/0205070

  • Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

  • Pigneul N, Kooli E (2018) Analyse de sentiments à base d’aspects par combinaison de réseaux profonds: application à des avis en français

  • Piryani R, Gupta V, Singh VK, Ghose U (2017) A linguistic rule-based approach for aspect-level sentiment analysis of movie reviews. In: Advances in computer and computational sciences: proceedings of ICCCCS 2016, vol 1, pp 201–209

  • PontikiM, GalanisD, Papageorgiou H, Androutsopoulos I, Manandhar S, AL-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, Clercq OD et al. (2016) Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 19–30. Association for Computational Linguistics

  • Potisuk S (2010) Typed dependency relations for syntactic analysis of thai sentences. In: Proceedings of the 24th Pacific Asia conference on language, information and computation, pp 511–518

  • Ramaswamy SL, Chinnappan J (2022) Recognet-lstm+ cnn: a hybrid network with attention mechanism for aspect categorization and sentiment classification. J Intell Inf Syst 58(2):379–404

    Article  Google Scholar 

  • Ray P, Chakrabarti A (2022) A mixed approach of deep learning method and rule-based method to improve aspect level sentiment analysis. Appl Comput Inf 18(1/2):163–178

    Google Scholar 

  • Rizwan A, Iqbal N, Ahmad R, Kim D (2021) Wr-svm model based on the margin radius approach for solving the minimum enclosing ball problem in support vector machine classification. Appl Sci 11(10):4657

    Article  Google Scholar 

  • Ruder S, Ghaffari P, Breslin JG (2016) Insight-1 at semeval-2016 task 5: deep learning for multilingual aspect-based sentiment analysis. arXiv preprint arXiv:1609.02748

  • Ruder S, Ghaffari P, Breslin JG (2016) A hierarchical model of reviews for aspect-based sentiment analysis. arXiv preprint arXiv:1609.02745

  • Sarker IH (2021) Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci 2(6):420

    Article  Google Scholar 

  • Straka M, Straková J (2017) Tokenizing, pos tagging, lemmatizing and parsing ud 2.0 with udpipe. In: Proceedings of the CoNLL 2017 shared task: multilingual parsing from raw text to universal dependencies, pp 88–99

  • Taye MM (2023) Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers 12(5):91

    Article  Google Scholar 

  • Vanaja S, Belwal M (2018) Aspect-level sentiment analysis on e-commerce data. In: 2018 International conference on inventive research in computing applications (ICIRCA), pp 1275–1279

  • Villena RPCSL, Cristóbal J (2011) Hybrid approach combining machine learning and a rule-based expert system for text categorization. AAAI

  • Wang J, Li J, Li S, Kang Y, Zhang M, SiL, Zhou G (2018) Aspect sentiment classification with both word-level and clause-level attention networks. In: IJCAI, vol 2018, pp. 4439–4445

  • Yan Z, Xing M, zhang D (2015) Exprs: An extended pagerank method for product feature extraction from online consumer reviews. Inf Manage 52(7)

  • Yao L, Mao C, Luo Y (2019) Clinical text classification with rule-based features and knowledge-guided convolutional neural networks. BMC Med Inf Decis Making 19(3):31–39

    Google Scholar 

  • Zhao Y, Mamat M, Aysa A, Ubul K (2023) Multimodal sentiment system and method based on crnn-svm. Neural Comput App, pp 1–13

  • Zhao Y, Mamat M, Aysa A, Ubul K (2023) Multimodal sentiment system and method based on crnn-svm. Neural Comput Appl, pp 1–13

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  1. University of Sfax , Sfax, Tunisia

    Sarsabene Hammi, Souha Mezghani Hammami & Lamia Hadrich Belguith

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  1. Sarsabene Hammi
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  2. Souha Mezghani Hammami
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  3. Lamia Hadrich Belguith
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Contributions

SH conceived and designed the study, conducted the experiments, collected and analyzed the data. SMH contributed to data analysis and interpretation, drafted the manuscript. LHB provided critical revisions and contributed to the intellectual content. All authors contributed equally to the crystallization of this work.

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Correspondence to Sarsabene Hammi.

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Hammi, S., Hammami, S.M. & Belguith, L.H. 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, 117 (2023). https://doi.org/10.1007/s13278-023-01126-4

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