Aspect based sentiment analysis of Twitter mobile phone reviews using LSTM and Convolutional Neural Network

Nitendra Kumar; Ritu Talwar; Sadhana Tiwari; Priyanka Agarwal

doi:10.52756/ijerr.2024.v43spl.011

Authors

Nitendra Kumar Department of Decision Sciences, Amity Business School, Amity University, Noida-201303, Uttar Pradesh, India https://orcid.org/0000-0001-7834-7926
Ritu Talwar Department of Human Resource Management, NDIM, New Delhi -110062, India https://orcid.org/0000-0002-8617-4479
Sadhana Tiwari Department of Operations and Supply Chain Management, GNIOT Institute of Management Studies, Greater Noida-201310, Uttar Pradesh, India https://orcid.org/0000-0003-3786-3398
Priyanka Agarwal Department of Human Resource Management, Amity Business School, Amity University, Noida-201303, Uttar Pradesh, India https://orcid.org/0000-0002-2943-2507

DOI:

https://doi.org/10.52756/ijerr.2024.v43spl.011

Keywords:

LSTM, Recurrent neural network, Convolutional Neural Network, Sentiment analysis, Artificial intelligence, Fuzzy logic, Mobile phone reviews

Abstract

The proliferation of online shopping has led to a surge in product reviews, providing valuable information to consumers. However, the overwhelming volume and subjective nature of these reviews make it difficult to assess product performance accurately. We propose a machine learning-based system that extracts sentiment from online reviews to address this challenge. Our system effectively identifies positive, negative, and neutral sentiments and classifies sentiment for specific product aspects. By offering concise and clear information, our system empowers consumers to make informed purchasing decisions and assists manufacturers in improving their products. Our proposed LSTMCNN model, trained on a dataset of 62,563 reviews, achieved impressive results with an accuracy of 95.84%, precision of 95.6% and recall of 95.8%. This significantly outperforms existing models, demonstrating the effectiveness of our approach. Moving forward, we aim to further enhance the accuracy of our system, track sentiment changes over time, and develop personalized product recommendations. These advancements will continue to increase the value and utility of online reviews in e-commerce.

References

Ahmed, Z., & Wang, J. (2023). A fine-grained deep learning model using embedded-CNN with BiLSTM for exploiting product sentiments. Alexandria Engineering Journal, 65, 731–747. https://doi.org/10.1016/j.aej.2022.10.037 DOI: https://doi.org/10.1016/j.aej.2022.10.037

Ananthajothi, K., Karthikayani, K., & Prabha, R. (2022). Explicit and implicit oriented Aspect-Based Sentiment Analysis with optimal feature selection and deep learning for demonetization in India. Data & Knowledge Engineering, 142, 102092.

https://doi.org/10.1016/j.datak.2022.102092 DOI: https://doi.org/10.1016/j.datak.2022.102092

Asif, M., Ishtiaq, A., Ahmad, H., Aljuaid, H., & Shah, J. (2020). Sentiment analysis of extremism in social media from textual information. Telematics and Informatics, 48, 101345. https://doi.org/10.1016/j.tele.2020.101345 DOI: https://doi.org/10.1016/j.tele.2020.101345

Catelli, R., Pelosi, S., Comito, C., Pizzuti, C., & Esposito, M. (2023). Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy. Computers in Biology and Medicine, 158, 106876.

https://doi.org/10.1016/j.compbiomed.2023.106876 DOI: https://doi.org/10.1016/j.compbiomed.2023.106876

Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. E. (2020). Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Applied Soft Computing, 97, 106754. https://doi.org/10.1016/j.asoc.2020.106754 DOI: https://doi.org/10.1016/j.asoc.2020.106754

Cheng, L.C., Chen, Y.L., & Liao, Y.Y. (2022). Aspect-based sentiment analysis with component focusing multi-head co-attention networks. Neurocomputing, 489, 9–17. https://doi.org/10.1016/j.neucom.2022.03.027 DOI: https://doi.org/10.1016/j.neucom.2022.03.027

Dangi, D., Telang Chandel, S., Kumar Dixit, D., Sharma, S., & Bhagat, A. (2023). An efficient model for sentiment analysis using artificial rabbits optimized vector functional link network. Expert Systems with Applications, 225, 119849.

https://doi.org/10.1016/j.eswa.2023.119849 DOI: https://doi.org/10.1016/j.eswa.2023.119849

Elahi, M., Khosh Kholgh, D., Kiarostami, M. S., Oussalah, M., & Saghari, S. (2023). Hybrid recommendation by incorporating the sentiment of product reviews. Information Sciences, 625, 738–756. https://doi.org/10.1016/j.ins.2023.01.051 DOI: https://doi.org/10.1016/j.ins.2023.01.051

García-Méndez, S., De Arriba-Pérez, F., Barros-Vila, A., & González-Castaño, F. J. (2023). Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages. Expert Systems with Applications, 218, 119611. https://doi.org/10.1016/j.eswa.2023.119611 DOI: https://doi.org/10.1016/j.eswa.2023.119611

Han, Y., Zhou, X., Wang, G., Feng, Y., Zhao, H., & Wang, J. (2023). Fusing sentiment knowledge and inter-aspect dependency based on gated mechanism for aspect-level sentiment classification. Neurocomputing, 551, 126462. https://doi.org/10.1016/j.neucom.2023.126462 DOI: https://doi.org/10.1016/j.neucom.2023.126462

Huang, B., Guo, R., Zhu, Y., Fang, Z., Zeng, G., Liu, J., Wang, Y., Fujita, H., & Shi, Z. (2022). Aspect-level sentiment analysis with aspect-specific context position information. Knowledge-Based Systems, 243, 108473.

https://doi.org/10.1016/j.knosys.2022.108473 DOI: https://doi.org/10.1016/j.knosys.2022.108473

Huang, H., Zhang, B., Jing, L., Fu, X., Chen, X., & Shi, J. (2022). Logic tensor network with massive learned knowledge for aspect-based sentiment analysis. Knowledge-Based Systems, 257, 109943. https://doi.org/10.1016/j.knosys.2022.109943 DOI: https://doi.org/10.1016/j.knosys.2022.109943

Huang, Y., Peng, H., Liu, Q., Yang, Q., Wang, J., Orellana-Martín, D., & Pérez-Jiménez, M. J. (2023). Attention-enabled gated spiking neural P model for aspect-level sentiment classification. Neural Networks, 157, 437–443.

https://doi.org/10.1016/j.neunet.2022.11.006 DOI: https://doi.org/10.1016/j.neunet.2022.11.006

J, S., & U, K. (2023). Sentiment analysis of amazon user reviews using a hybrid approach. Measurement: Sensors, 27, 100790.

https://doi.org/10.1016/j.measen.2023.100790 DOI: https://doi.org/10.1016/j.measen.2023.100790

Kumar, N., Agarwal, P., Bansal, S., Kumar Yadav, V., & Bhowmik, D. (2024). Sentiment analysis using novel deep learning methods. Proceedings on Engineering Sciences, 6(2), 853–862. https://doi.org/10.24874/PES06.02A.012 DOI: https://doi.org/10.24874/PES06.02A.012

Leelawat, N., Jariyapongpaiboon, S., Promjun, A., Boonyarak, S., Saengtabtim, K., Laosunthara, A., Yudha, A. K., & Tang, J. (2022). Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning. Heliyon, 8(10), e10894. https://doi.org/10.1016/j.heliyon.2022.e10894 DOI: https://doi.org/10.1016/j.heliyon.2022.e10894

Lv, Y., Wei, F., Cao, L., Peng, S., Niu, J., Yu, S., & Wang, C. (2021). Aspect-level sentiment analysis using context and aspect memory network. Neurocomputing, 428, 195–205. https://doi.org/10.1016/j.neucom.2020.11.049 DOI: https://doi.org/10.1016/j.neucom.2020.11.049

Lyu, Z., & Takikawa, H. (2022). Media framing and expression of anti-China sentiment in COVID-19-related news discourse: An analysis using deep learning methods. Heliyon, 8(8), e10419. https://doi.org/10.1016/j.heliyon.2022.e10419Mehra, P. (2023). Unexpected surprise: Emotion analysis and aspect based sentiment analysis (Absa) of user generated comments to study behavioral intentions of tourists. Tourism Management Perspectives, 45, 101063. https://doi.org/10.1016/j.tmp.2022.101063 DOI: https://doi.org/10.1016/j.tmp.2022.101063

Melton, C. A., Olusanya, O. A., Ammar, N., & Shaban-Nejad, A. (2021). Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence. Journal of Infection and Public Health, 14(10), 1505–1512. https://doi.org/10.1016/j.jiph.2021.08.010 DOI: https://doi.org/10.1016/j.jiph.2021.08.010

Piryani, R., Gupta, V., Singh, V. K., & Ghose, U. (2017). A linguistic rule-based approach for aspect-level sentiment analysis of movie reviews. In S. K. Bhatia, K. K. Mishra, S. Tiwari, & V. K. Singh (Eds.), Advances in Computer and Computational Sciences, 553, 201–209. Springer Singapore. https://doi.org/10.1007/978-981-10-3770-2_19 DOI: https://doi.org/10.1007/978-981-10-3770-2_19

‘7 Reasons Why Customer Feedback Is Important To Your Business - Startquestion - create online surveys and forms’. [Online]. Available: https://www.startquestion.com/blog/7-reasons-‘Understanding Customer Experience’. [Online]. Available: https://hbr.org/2007/02/understanding-customer-experience.

Sharifi, Z., & Shokouhyar, S. (2021). Promoting consumer’s attitude toward refurbished mobile phones: A social media analytics approach. Resources, Conservation and Recycling, 167, 105398. https://doi.org/10.1016/j.resconrec.2021.105398 DOI: https://doi.org/10.1016/j.resconrec.2021.105398

Sunitha, D., Patra, R. K., Babu, N. V., Suresh, A., & Gupta, S. C. (2022). Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries. Pattern Recognition Letters, 158, 164–170.

https://doi.org/10.1016/j.patrec.2022.04.027 DOI: https://doi.org/10.1016/j.patrec.2022.04.027

why-customer-feedback-is-important-to-your-business/.

Wu, H., Huang, C., & Deng, S. (2023). Improving aspect-based sentiment analysis with Knowledge-aware Dependency Graph Network. Information Fusion, 92, 289–299. https://doi.org/10.1016/j.inffus.2022.12.004 DOI: https://doi.org/10.1016/j.inffus.2022.12.004

Xiang, Y., Zhang, J., & Guo, J. (2023). Block-level dependency syntax based model for end-to-end aspect-based sentiment analysis. Neural Networks, 166, 225–235. https://doi.org/10.1016/j.neunet.2023.05.008 DOI: https://doi.org/10.1016/j.neunet.2023.05.008

You, L., Han, F., Peng, J., Jin, H., & Claramunt, C. (2022). ASK-RoBERTa: A pretraining model for aspect-based sentiment classification via sentiment knowledge mining. Knowledge-Based Systems, 253, 109511. https://doi.org/10.1016/j.knosys.2022.109511 DOI: https://doi.org/10.1016/j.knosys.2022.109511

Zhao, Z., Tang, M., Tang, W., Wang, C., & Chen, X. (2022). Graph convolutional network with multiple weight mechanisms for aspect-based sentiment analysis. Neurocomputing, 500, 124–134. https://doi.org/10.1016/j.neucom.2022.05.045 DOI: https://doi.org/10.1016/j.neucom.2022.05.045

Zhou, T., Shen, Y., Chen, K., & Cao, Q. (2023). Hierarchical dual graph convolutional network for aspect-based sentiment analysis. Knowledge-Based Systems, 276, 110740. https://doi.org/10.1016/j.knosys.2023.110740 DOI: https://doi.org/10.1016/j.knosys.2023.110740

Zou, F., Zhu, X., Qian, Y., & Chang, D. (2022). A sustainable product design approach based on data mining of dynamic user demands: A case study on Huawei mate 40 series. IFAC-Papers OnLine, 55(10), 1056–1061. https://doi.org/10.1016/j.ifacol.2022.09.529 DOI: https://doi.org/10.1016/j.ifacol.2022.09.529

Žuni?, A., Corcoran, P., & Spasi?, I. (2021). Aspect-based sentiment analysis with graph convolution over syntactic dependencies. Artificial Intelligence in Medicine, 119, 102138. https://doi.org/10.1016/j.artmed.2021.102138 DOI: https://doi.org/10.1016/j.artmed.2021.102138