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International Journal of Sensors, Wireless Communications and Control

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ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

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Research Article

An Efficient Aspect-based Sentiment Classification with Hybrid Word Embeddings and CNN Framework

Author(s): Monika Agrawal and Nageswara Rao Moparthi*

Volume 14, Issue 1, 2024

Published on: 19 January, 2024

Page: [45 - 54] Pages: 10

DOI: 10.2174/0122103279275188231205094007

Price: $65

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Abstract

Background: As the e-commerce product reviews and social media posts are increasing enormously, the size of the database for polarity/ sentiment detection is a challenging task, and again, predicting polarities associated with respect to aspect terms end to end in a sentence is a havoc in real-time applications. Human behavior is influenced by the various opinions generated in society. Public opinion influences our decisions most often. Businesses and establishments always need to collect the opinion of the society, which they try to obtain using customer feedback forms and questionnaires or surveys, which help them to be aware of the shortcomings if any, and to use suggestions to improve quality. It works in the same way for customers as well and the opinions of other customers about a particular product can come in handy when deciding to buy a product.

Objectives: In this work, an efficient Aspect-based Sentiment Classification technique has been introduced with a hybrid, multiple-word embedding methods and implemented using the CNN framework on large databases.

Methods: Most of the traditional models have a limitation on the dependency for one or more similar types of aspect words for sentiment classification problem. However, these conventional models such as TF-ID, Word 2Vec and Glove method consumes much more time for word embedding process and Aspect terms generation and further process of aspect level sentiment classification. Further, these models are facing problems of high true negative rate and misclassification rate on large aspect databases in sentiment classification. In this article, we have introduced an efficient Proposed ensemble word embedding model in the CNN network and defined Hybrid Word2 Vec method, Hybrid Glove word embedding method and Hybrid Random Forest model for sentiment classification.

Results: Experiments on a widely used benchmark prove that the proposed word embedding method- based classification technique results in to higher true positive rate with minimal misclassifications and also supports better runtime and accuracy than the traditional word embedding-based aspect level classification approaches.

Conclusion: In this article, a hybrid ensemble feature ranking-based classification model is proposed on the large aspect databases. In this work, advanced multiple-word embedding methods are implemented to improve the essential feature extraction problem in the aspect level sentiment process. These multiple-word embedding methods are applied to the sentiment databases in the CNN framework.

Keywords: Sentiment classification, opinion, WordNet, machine learning, word embedding, CNN.

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[1]
Akhtar MS, Garg T, Ekbal A. Multi-task learning for aspect term extraction and aspect sentiment classification. Neurocomputing 2020; 398: 247-56.
[http://dx.doi.org/10.1016/j.neucom.2020.02.093]
[2]
Chen F, Yuan Z, Huang Y. Multi-source data fusion for aspect-level sentiment classification. Knowl Base Syst 2020; 187: 104831.
[http://dx.doi.org/10.1016/j.knosys.2019.07.002]
[3]
Du Y, He M, Wang L, Zhang H. Wasserstein based transfer network for cross-domain sentiment classification. Knowl Base Syst 2020; 204: 106162.
[http://dx.doi.org/10.1016/j.knosys.2020.106162]
[4]
Duan J, Luo B, Zeng J. Semi-supervised learning with generative model for sentiment classification of stock messages. Expert Syst Appl 2020; 158: 113540.
[http://dx.doi.org/10.1016/j.eswa.2020.113540]
[5]
Fu X, Wei Y, Xu F, et al. Semi-supervised aspect-level sentiment classification model based on variational autoencoder. Knowl Base Syst 2019; 171: 81-92.
[http://dx.doi.org/10.1016/j.knosys.2019.02.008]
[6]
García-Díaz JA, Cánovas-García M, Valencia-García R. Ontologydriven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America. Future Gener Comput Syst 2020; 112: 641-57.
[http://dx.doi.org/10.1016/j.future.2020.06.019] [PMID: 32572291]
[7]
Liu F, Zheng L, Zheng J. HieNN-DWE: A hierarchical neural network with dynamic word embeddings for document level sentiment classification. Neurocomputing 2020; 403: 21-32.
[http://dx.doi.org/10.1016/j.neucom.2020.04.084]
[8]
Liu S, Lee K, Lee I. Document-level multi-topic sentiment classification of Email data with BiLSTM and data augmentation. Knowl Base Syst 2020; 197: 105918.
[http://dx.doi.org/10.1016/j.knosys.2020.105918]
[9]
Ma R, Wang K, Qiu T, Sangaiah AK, Lin D, Liaqat HB. Featurebased compositing memory networks for aspect-based sentiment classification in social internet of things. Future Gener Comput Syst 2019; 92: 879-88.
[http://dx.doi.org/10.1016/j.future.2017.11.036]
[10]
Meškelė D, Frasincar F. ALDONAr: A hybrid solution for sentence-level aspect-based sentiment analysis using a lexicalized domain ontology and a regularized neural attention model. Inf Process Manage 2020; 57(3): 102211.
[http://dx.doi.org/10.1016/j.ipm.2020.102211]
[11]
Mowlaei ME, Saniee Abadeh M, Keshavarz H. Aspect-based sentiment analysis using adaptive aspect-based lexicons. Expert Syst Appl 2020; 148: 113234.
[http://dx.doi.org/10.1016/j.eswa.2020.113234]
[12]
Nguyen HT, Nguyen LM. ILWAANet: An interactive lexiconaware word-aspect attention network for aspect-level sentiment classification on social networking. Expert Syst Appl 2020; 146: 113065.
[http://dx.doi.org/10.1016/j.eswa.2019.113065]
[13]
Park H, Song M, Shin KS. Deep learning models and datasets for aspect term sentiment classification: Implementing holistic recurrent attention on target-dependent memories. Knowl Base Syst 2020; 187: 104825.
[http://dx.doi.org/10.1016/j.knosys.2019.06.033]
[14]
Pham DH, Le AC. Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis. Int J Approx Reason 2018; 103: 1-10.
[http://dx.doi.org/10.1016/j.ijar.2018.08.003]
[15]
Shuang K, Gu M, Li R, Loo J, Su S. Interactive POS-aware network for aspect-level sentiment classification. Neurocomputing 2020; (Sep):
[http://dx.doi.org/10.1016/j.neucom.2020.08.013]
[16]
Shuang K, Ren X, Yang Q, Li R, Loo J. AELA-DLSTMs: Attention-enabled and location-aware double LSTMs for aspect-level sentiment classification. Neurocomputing 2019; 334: 25-34.
[http://dx.doi.org/10.1016/j.neucom.2018.11.084]
[17]
Wen J, Zhang G, Zhang H, Yin W, Ma J. Speculative text mining for document-level sentiment classification. Neurocomputing 2020; 412: 52-62.
[http://dx.doi.org/10.1016/j.neucom.2020.06.024]
[18]
Xu Q, Zhu L, Dai T, Yan C. Aspect-based sentiment classification with multi-attention network. Neurocomputing 2020; 388: 135-43.
[http://dx.doi.org/10.1016/j.neucom.2020.01.024]
[19]
Yang M, Jiang Q, Shen Y, Wu Q, Zhao Z, Zhou W. Hierarchical human-like strategy for aspect-level sentiment classification with sentiment linguistic knowledge and reinforcement learning. Neural Netw 2019; 117: 240-8.
[http://dx.doi.org/10.1016/j.neunet.2019.05.021] [PMID: 31195206]
[20]
Ye X, Dai H, Dong L, Wang X. Multi-view ensemble learning method for microblog sentiment classification. Expert Syst Appl 2021; 166(Sep): 113987.
[http://dx.doi.org/10.1016/j.eswa.2020.113987]
[21]
Zhang J, Chen M, Sun H, Li D, Wang Z. Object semantics sentiment correlation analysis enhanced image sentiment classification. Knowl Base Syst 2020; 191: 105245.
[http://dx.doi.org/10.1016/j.knosys.2019.105245]
[22]
Zhang M, Palade V, Wang Y, Ji Z. Attention-based word embeddings using artificial bee colony algorithm for aspect-level sentiment classification. Inf Sci 2020; (Sep):
[http://dx.doi.org/10.1016/j.ins.2020.09.038]
[23]
Zhao P, Hou L, Wu O. Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification. Knowl Base Syst 2020; 193: 105443.
[http://dx.doi.org/10.1016/j.knosys.2019.105443]
[24]
Zhou J, Chen Q, Huang JX, Hu QV, He L. Position-aware hierarchical transfer model for aspect-level sentiment classification. Inf Sci 2020; 513: 1-16.
[http://dx.doi.org/10.1016/j.ins.2019.11.048]
[25]
Zhou J, Huang JX, Hu QV, He LSK-GCN. Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowl Base Syst 2020; 205: 106292.
[http://dx.doi.org/10.1016/j.knosys.2020.106292]
[26]
Agrawal M, Moparthi NR. An efficient multiple-word embeddingbased cross-domain feature extraction and aspect sentiment classification Meas: Sens 2023; 100851.
[http://dx.doi.org/10.1016/j.measen.2023.100851]
[27]
Wang X, Tang M, Yang T, Wang Z. A novel network with multiple attention mechanisms for aspect-level sentiment analysis. Knowl Base Syst 2021; 227: 107196.
[http://dx.doi.org/10.1016/j.knosys.2021.107196]
[28]
Wang X, Li F, Zhang Z, Xu G, Zhang J, Sun X. A unified position-aware convolutional neural network for aspect based sentiment analysis. Neurocomputing 2021; 450: 91-103.
[http://dx.doi.org/10.1016/j.neucom.2021.03.092]
[29]
Wei L, Hu D, Zhou W, et al. Hierarchical interaction networks with the rethinking mechanism for document-level sentiment analysis. Proceedings, Part III September 14–18, 2020;. 2021; pp. 633-49.
[http://dx.doi.org/10.1007/978-3-030-67664-3_38]
[30]
Agrawal M, Moparthi NR. A Comprehensive Survey on Aspect Based Word Embedding Models and Sentiment Analysis Classification Approaches. Recent Trends in Intensive Computing 2021.
[http://dx.doi.org/10.3233/APC210175]
[31]
Rodrigues AP, Chiplunkar NN. A new big data approach for topic classification and sentiment analysis of Twitter data. Evol Intell 2019; 1-11.
[32]
Patel D, Amin K. Multi-source domain adaptation in sentiment analysis using optimized neural network and cross-domain semantic libra. Int J Intell Eng Syst 2021; 14(5)
[http://dx.doi.org/10.22266/ijies2021.1031.47]
[33]
Senevirathne L, Demotte P, Karunanayake B, Munasinghe U, Ranathunga S. Sentiment analysis for sinhala language using deep learning techniques. arXiv:201107280 2020.
[34]
Poria S, Hazarika D, Majumder N, Mihalcea R. Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research. IEEE Trans Affect Comput 2020.
[35]
Li Z, Wei Y, Zhang Y, Zhang X, Li X. Exploiting coarse-to-fine task transfer for aspect-level sentiment classification. Proc Conf AAAI Artif Intell 2019; 33(1): 4253-60.
[http://dx.doi.org/10.1609/aaai.v33i01.33014253]
[36]
Li L, Zhu F, Sun H, Hu Y, Yang Y, Jin D. Multi-source information fusion and deep-learning-based characteristics measurement for exploring the effects of peer engagement on stock price synchronicity. Inf Fusion 2021; 69: 1-21.
[http://dx.doi.org/10.1016/j.inffus.2020.11.006]
[37]
Yin C, Zhang S, Zeng Q. Hybrid representation and decision fusion towards visual-textual sentiment. ACM Trans Intell Syst Technol 2023; 14(3): 1-17.
[http://dx.doi.org/10.1145/3583076]
[38]
Song W, Wen Z, Xiao Z, Park SC. Semantics perception and refinement network for aspect-based sentiment analysis. Knowl Base Syst 2021; 214: 106755.
[http://dx.doi.org/10.1016/j.knosys.2021.106755]
[39]
Liu M, Zhou F, Chen K, Zhao Y. Co-attention networks based on aspect and context for aspect-level sentiment analysis. Knowl Base Syst 2021; 217: 106810.
[http://dx.doi.org/10.1016/j.knosys.2021.106810]
[40]
Rajput GK, Kundu S, Kumar A. The impact of feature extraction on multi-source sentiment analysis. 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART) 2021; 79: 510-5.
[http://dx.doi.org/10.1109/SMART52563.2021.9676201]
[41]
Afyouni I, Aghbari ZA, Razack RA. Multi-feature, multi-modal, and multi-source social event detection: A comprehensive survey. Inf Fusion 2022; 79: 279-308.
[http://dx.doi.org/10.1016/j.inffus.2021.10.013]
[42]
Jiang L, Liu L, Yao J, Shi L. A hybrid recommendation model in social media based on deep emotion analysis and multi-source view fusion. J Cloud Comput 2020; 9: 1-16.
[43]
Zhu L, Zhu Z, Zhang C, Xu Y, Kong X. Multimodal sentiment analysis based on fusion methods: A survey. Inf Fusion 2023; 95: 306-25.
[http://dx.doi.org/10.1016/j.inffus.2023.02.028]
[44]
Sarker A, Canto AC, Mozaffari Kermani M, Azarderakhsh R. Error detection architectures for hardware/software co-design approaches of number-theoretic transform. IEEE Trans Comput Aided Des Integrated Circ Syst 2023; 42(7): 2418-22.
[http://dx.doi.org/10.1109/TCAD.2022.3218614]
[45]
Mehran MKR. Integrating emerging cryptographic engineering research and security education. ASEE Annual Conference & Exposition. 1-26.
[46]
Ausmita Sarker, Alvaro Cintas Canto, Mehran Mozaffari Kermani, Reza Azarderakhsh,. “Error Detection Architectures for Hardware/ Software Co-design Approaches of Number-Theoretic Transform”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol 42, Issue 7, July 2023.
[47]
Sarker A, Kermani MM, Azarderakhsh R. Efficient error detection architectures for postquantum signature Falcon’s sampler and KEM SABER. IEEE Trans Very Large Scale Integr VLSI Syst 2022; 30(6): 794-802.
[http://dx.doi.org/10.1109/TVLSI.2022.3156479]
[48]
Canto AC, Kaur J, Kermani MM, Azarderakhsh R. Algorithmic security is insufficient: A comprehensive survey on implementation attacks haunting post-quantum security. arXiv:230513544 2023.
[49]
Kaur J, Canto AC, Kermani MM, Azarderakhsh R. A comprehensive survey on the implementations, attacks, and countermeasures of the current NIST lightweight cryptography standard. arXiv:230406222 2023.
[50]
Elkhatib R, Koziel B, Azarderakhsh R, Kermani MM. Accelerated RISC-V for SIKE. 2021 IEEE 28th Symposium on Computer Arithmetic (ARITH) 14-16 June 2021; Lyngby, Denmark 2021.
[http://dx.doi.org/10.1109/ARITH51176.2021.00035]
[51]
Canto AC, Kaur J, Kermani MM, Azarderakhsh R. ChatGPT vs. Lightweight security: First work implementing the NIST cryptographic standard ASCON. arXiv:230608178 2023.
[52]
Chauhan GS, Meena YK. An unsupervised multiple word-embedding method with attention model for cross domain aspect term extraction. 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE2020).
[http://dx.doi.org/10.1109/ICETCE48199.2020.9091738]
[53]
Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: A survey. Ain Shams Eng J 2014; 5(4): 1093-113.
[http://dx.doi.org/10.1016/j.asej.2014.04.011]
[54]
Toqir AR, Cheah YN. Aspect extraction in sentiment analysis: comparative analysis and survey. Springer 2016.
[http://dx.doi.org/10.1007/s10462-016-9472-z]
[55]
Abirami AM, Gayathri V. A survey on sentiment analysis methods and approach. 2016 Eighth International Conference on Advanced Computing (ICoAC). 19-21 January 2017; Chennai, India. 2016.
[56]
Araque O, Zhu G, Garcia-Amado M, Iglesias CA. Mining the opinionated web: Classification and detection of aspect contexts for aspect based sentiment analysis. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). 12-15 December 2016; Barcelona, Spain. 2016.
[http://dx.doi.org/10.1109/ICDMW.2016.0132]
[57]
Liu R, Shi Y, Ji C, Jia M. A survey of sentiment analysis based on transfer learning. IEEE Access 2019; 7: 85401-12.
[http://dx.doi.org/10.1109/ACCESS.2019.2925059]
[58]
Poria S. Aspect extraction for opinion mining with a deep convolutional neural network. KBS 2016; 108: 42-9.
[http://dx.doi.org/10.1016/j.knosys.2016.06.009]
[59]
Zhou Z, Liu F. Filter gate network based on multi-head attention for aspect-level sentiment classification. Neurocomputing 2021; 441: 214-25.
[http://dx.doi.org/10.1016/j.neucom.2021.02.041]
[60]
Zhou T, Law K, Creighton D. A weakly-supervised graph-based joint sentiment topic model for multi-topic sentiment analysis. Inf Sci 2022; 609: 1030-51.
[http://dx.doi.org/10.1016/j.ins.2022.07.126]
[61]
Zhao Z, Tang M, Tang W, Wang C, Chen X. Graph convolutional network with multiple weight mechanisms for aspect-based sentiment analysis. Neurocomputing 2022; 500: 124-34.
[http://dx.doi.org/10.1016/j.neucom.2022.05.045]
[62]
Zhao Q, Niu J, Liu X. ALS-MRS: Incorporating aspect-level sentiment for abstractive multi-review summarization. Knowl Base Syst 2022; 258: 109942.
[http://dx.doi.org/10.1016/j.knosys.2022.109942]
[63]
Zhang H, Chen Z, Chen B, et al. Complete quadruple extraction using a two-stage neural model for aspect-based sentiment analysis. Neurocomputing 2022; 492: 452-63.
[http://dx.doi.org/10.1016/j.neucom.2022.04.027]
[64]
Yang L, Na JC, Yu J. Cross-modal multitask transformer for end-to-end multimodal aspect-based sentiment analysis. Inf Process Manage 2022; 59(5): 103038.
[http://dx.doi.org/10.1016/j.ipm.2022.103038]
[65]
Xu L, Pang X, Wu J, Cai M, Peng J. Learn from structural scope: Improving aspect-level sentiment analysis with hybrid graph convolutional networks. Neurocomputing 2023; 518: 373-83.
[http://dx.doi.org/10.1016/j.neucom.2022.10.071]
[66]
Xianghua F, Guo L, Yanyan G, Zhiqiang W. Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowl Base Syst 2013; 37: 186-95.
[http://dx.doi.org/10.1016/j.knosys.2012.08.003]
[67]
Wu H, Huang C, Deng S. Improving aspect-based sentiment analysis with Knowledge-aware Dependency Graph Network. Inf Fusion 2023; 92: 289-99.
[http://dx.doi.org/10.1016/j.inffus.2022.12.004]
[68]
Wang X, Xu G, Zhang Z, Jin L, Sun X. End-to-end aspect-based sentiment analysis with hierarchical multi-task learning. Neurocomputing 2021; 455: 178-88.
[http://dx.doi.org/10.1016/j.neucom.2021.03.100]
[69]
Shaik T, Tao X, Dann C, Xie H, Li Y, Galligan L. Sentiment analysis and opinion mining on educational data: A survey. J Nat Lang Process 2023; 2: 100003.
[http://dx.doi.org/10.1016/j.nlp.2022.100003]
[70]
Rahmani S, Hosseini S, Zall R, Kangavari MR, Kamran S, Hua W. Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects. Knowl Base Syst 2023; 261: 110219.
[http://dx.doi.org/10.1016/j.knosys.2022.110219]
[71]
Phan HT, Nguyen NT, Hwang D. Aspect-level sentiment analysis: A survey of graph convolutional network methods. Inf Fusion 2023; 91: 149-72.
[http://dx.doi.org/10.1016/j.inffus.2022.10.004]
[72]
Peng H, Ma Y, Li Y, Cambria E. Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowl Base Syst 2018; 148: 167-76.
[http://dx.doi.org/10.1016/j.knosys.2018.02.034]
[73]
Ma X, Zeng J, Peng L, Fortino G, Zhang Y. Modeling multi-aspects within one opinionated sentence simultaneously for aspect-level sentiment analysis. Future Gener Comput Syst 2019; 93: 304-11.
[http://dx.doi.org/10.1016/j.future.2018.10.041]

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