Aspect-Based Sentiment Analysis for Hotel Reviews Using a Data Augmentation Approach
Keywords:
Feature Extraction, PAM, Aspect-based Sentiment Analysis, Topic ModellingAbstract
The amount of user-generated textual material created by social networks, blogs, forums, and e-commerce websites is increasing at an astronomical pace. When it comes to determining the success of a product or service, the opinions of customers are critical. Due to this, interest in FE exams and assessment mining has surged. Angle`s put-together feeling examination depends on extracting item qualities from client assessments utilizing subject demonstrating and Latent Dirichlet Allocation (LDA). Because of information sparsity and the non-appearance of co-event designs in short texts, LDA won`t be quickly applied to client audits and other short texts. Various methods have been distributed for adapting the latest models like LDA for short. A Pachinko Allocation Model (PAM) is proposed in this paper as a one-of-a-kind methodology for opinion examination because of perspectives. The Pachinko Allocation Model is a new PAM adaptation that extracts product aspects. Data augmentation increases the text data set size for the text classification task. After that, features are extracted using TF-IDF-IC-SDF and TF-IGM methods, and the fine sentiment is extracted utilizing the opinion lexicon. According to the findings of the experiments, PAM is a competitive method for extracting aspects. The outcomes of the trial show that the novel sentiment classification approach is competitive in terms of product extraction. A statistical test has also been conducted.
References
J. Singh, G. Singh, & R. Singh, “Optimization of sentiment analysis using machine learning classifiers,” Human-centric Computing and information Sciences, Vol.7, Issue.1, pp.1-12, 2017.
M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, & B. Gupta, “Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews,” Journal of computational science, Vol.27, pp.386-393, 2018.
P. Kalarani, & S. Selva Brunda, “Sentiment analysis by POS and joint sentiment topic features using SVM and ANN,” Soft Computing, Vol.23, Issue.16, pp.7067-7079, 2019.
L. Haghnegahdar, & Y. Wang, “A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection,” Neural computing and applications, Vol.32, Issue.13, pp.9427-9441, 2020.
W. Maharani, D. Widyantoro, H. Khodra, “Aspect extraction in customer reviews using syntactic pattern,” Procedia Computer Science, Vol.59, pp.244-253, 2015.
Q. Liu, Z. Gao, B. Liu, & Y. Zhang, “Automated rule selection for opinion target extraction,” Knowledge-Based Systems, Vol.104, pp.74-88, 2016.
C. Liao, C. Feng, S. Yang, & H. Huang, “Topic-related Chinese message sentiment analysis,” Neurocomputing, Vol.210, pp.237-246, 2016.
S. M. Rezaeinia, R. Rahmani, A. Ghodsi, &H. Veisi, “Sentiment analysis based on improved pre-trained word embeddings,” Expert Systems with Applications, Vol.117, pp.139-147, 2019.
M. E. Mowlaei, M. S. Abadeh, & H. Keshavarz, “Aspect-based sentiment analysis using adaptive aspect-based lexicons,” Expert Systems with Applications, Vol.148, pp.113-234, 2020.
M. Hu,& B. Liu, “Mining and summarizing customer reviews,” In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.168-177, 2004.
Danial Haider, Tehreem Saboor, Aqsa Rais, "Internet of Things (IoT) and their Intrusion: Solution and Potential Challenges," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.4, pp.32-47, 2024.
P. Jansson, & S. Liu, “Topic modelling enriched LSTM models for the detection of novel and emerging named entities from social media,” In 2017 IEEE International Conference on Big Data (Big Data), pp.4329-4336, 2017.
Y. Jo, L. Lee, &S. Palaskar, “Combining LSTM and latent topic modeling for mortality prediction,” arXiv preprint arXiv:1709.02842,2017.
G. Pergola, L., Gui, & Y. He, “TDAM: A topic-dependent attention model for sentiment analysis, “Information Processing & Management, Vol.56, Issue.6, 2017.
Y. Ma, H. Peng, & E. Cambria, “Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM,” In Proceedings of the AAAI conference on artificial intelligence, Vol.32, No.1, 2018.
A. B Dieng, F. J., Ruiz, & D. M. Blei, “Topic modeling in embedding spaces, “Transactions of the Association for Computational Linguistics, Vol.8, pp.439-453, 2020.
R. Bhargava, S. Arora, & Y. Sharma, “Neural network-based architecture for sentiment analysis in Indian languages,” Journal of Intelligent Systems, Vol.28, Issue.3, pp.361-375, 2019.
S. Xiong, K. Wang, D. Ji, & B. Wang, “A short text sentiment-topic model for product reviews,” Neurocomputing, Vol.297, pp.94-102, 2018.
A. García-Pablos, M. Cuadros, &G. Rigau, “W2VLDA: almost unsupervised system for aspect based sentiment analysis,” Expert Systems with Applications, Vol.91, pp.127-137, 2018.
Yojitha Chilukuri, Ulligaddala Srinivasarao, "Generative Adversarial Networks Using Name Entity Recognition Model Using Clinical Health Records," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.4, pp.1-7, 2024.
W. Li, & A. McCallum, “Pachinko allocation: DAG-structured mixture models of topic correlations,” In Proceedings of the 23rd international conference on Machine learning, pp.577-584, 2006.
W. Li, & A. McCallum, “Pachinko allocation: Scalable mixture models of topic correlations,” J. of Machine Learning Research. Submitted, 2008.
H. Niemi, “Stochastic Approximations in the, “In Proceedings of the Third Finnish-Soviet Symposium on Probability Theory and Mathematical Statistics, Turku, Finland, August, pp.13–16, 2020.
S. Baccianella, A. Esuli, & F. Sebastiani, “Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining,” In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC`10), 2010.
G. Csányi, &T. Orosz, “Comparison of data augmentation methods for legal document classification,” Acta TechnicaJaurinensis, Vol.15, Issue.1, pp.15-21, 2022.
Z. Jiang, B. Gao, Y. He, Y. Han, P. Doyle, & Q. Zhu, “Text classification using novel term weighting scheme-based improved TF-IDF for internet media reports,” Mathematical Problems in Engineering, 2021.
K. Chen, Z. Zhang, J. Long, J., & H. Zhang, “Turning from TF-IDF to TF-IGM for term weighting in text classification,” Expert Systems with Applications, Vol.66, pp.245-260, 2016.
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