SENTIMENT ANALYSIS USING LONG TERM MEMORY (LSTM) BOOK CASE STUDY: UNSOLICITED ADVICE FOR MURDERERS BY VERA WONG'S
DOI:
https://doi.org/10.56127/ijst.v2i3.995Abstract
Reading books is one of the most effective ways to reduce stress. In today's digital era, access to finding and buying books is getting easier, so reader reviews are important in choosing books that match interests. However, with a large number of reviews, Natural Language Processing (NLP) with the Long Short-Term Memory (LSTM) method is used to help analyze positive and negative sentiments from many book reviews. This sentiment analysis is useful for readers to evaluate the quality of books, as well as for authors and sellers to find out the opinions of readers and improve the quality of their work. In this study, the book review dataset "Vera Wong's Unsolicited Advice for Murderers" from the Goodreads website is used, which is then divided into training data and validation data with a ratio of 75%: 25%. The Long Short-Term Memory (LSTM) method is used to analyze the sentiment of the reviews. The model architecture built consists of Embedding Layer, LSTM Layer with 128 neuron units, 3 Dense Layer with ReLU activation function, 3 Dropout Layer, and Fully Connected Layer with and Sigmoid activation function, Binary Cross Entropy loss function, and RMSprop optimizer. The model training process was conducted with 30 epochs. The evaluation results show that the model achieved an accuracy of 90%, indicating the model performs relatively well in correctly classifying positive sentiments.
References
L. Chen, P. Chen, and Z. Lin, “Artificial Intelligence in Education: A Review,” IEEE Access, vol. 8, pp. 75264–75278, 2020, doi: 10.1109/ACCESS.2020.2988510.
Y. Belinkov and J. Glass, “Analysis Methods in Neural Language Processing: A Survey”, doi: 10.1162/tacl_a_00254/1923061/tacl_a_00254.pdf.
Q. T. Ain et al., “Sentiment Analysis Using Deep Learning Techniques: A Review,” 2017. [Online]. Available: www.ijacsa.thesai.org
F. M. Bianchi, E. Maiorino, M. C. Kampffmeyer, A. Rizzi, and R. Jenssen, “An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting,” May 2017, doi: 10.1007/978-3-319-70338-1.
D. Norris, “Short-term memory and long-term memory are still different,” Psychol Bull, vol. 143, no. 9, pp. 992–1009, Sep. 2017, doi: 10.1037/bul0000108.
M. Grandini, E. Bagli, and G. Visani, “Metrics for Multi-Class Classification: an Overview,” Aug. 2020, [Online]. Available: http://arxiv.org/abs/2008.05756
D. Makowski, “The psycho Package: an Efficient and Publishing-Oriented Workflow for Psychological Science,” The Journal of Open Source Software, vol. 3, no. 22, p. 470, Feb. 2018, doi: 10.21105/joss.00470.
G. Schuh, P. Jussen, and T. Harland, “The Digital Shadow of Services: A Reference Model for Comprehensive Data Collection in MRO Services of Machine Manufacturers,” in Procedia CIRP, Elsevier B.V., 2018, pp. 271–277. doi: 10.1016/j.procir.2018.03.318.
C. G. Northcutt, A. Athalye, and J. Mueller, “Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks,” Mar. 2021, [Online]. Available: http://arxiv.org/abs/2103.14749
Q. Liu, P. Li, W. Zhao, W. Cai, S. Yu, and V. C. M. Leung, “A survey on security threats and defensive techniques of machine learning: A data driven view,” IEEE Access, vol. 6. Institute of Electrical and Electronics Engineers Inc., pp. 12103–12117, Feb. 12, 2018. doi: 10.1109/ACCESS.2018.2805680.
R. Annisa, I. Surjandari, and Zulkarnain, “Opinion mining on mandalika hotel reviews using latent dirichlet allocation,” in Procedia Computer Science, Elsevier B.V., 2019, pp. 739–746. doi: 10.1016/j.procs.2019.11.178.
V. N. Gudivada, A. Apon, and J. Ding, “Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transformations.” [Online]. Available: www.iaria.org
I. A. El-Khair, “Effects of Stop Words Elimination for Arabic Information Retrieval: A Comparative Study.” [Online]. Available: http://www.unine.ch/info/clef
A. Sithole, E. T. Chiyaka, P. McCarthy, D. M. Mupinga, B. K. Bucklein, and J. Kibirige, “Student Attraction, Persistence and Retention in STEM Programs: Successes and Continuing Challenges,” Higher Education Studies, vol. 7, no. 1, p. 46, Jan. 2017, doi: 10.5539/hes.v7n1p46.
R. Heines and R. Jung, “The Tokenization of Everything: Towards a Framework for Understanding the Potentials of Tokenized Assets,” 2021. [Online]. Available: https://aisel.aisnet.org/pacis2021/40
J. L. Whittaker et al., “Imaging with ultrasound in physical therapy: What is the PT’s scope of practice? A competency-based educational model and training recommendations,” Br J Sports Med, vol. 53, no. 23, pp. 1447–1453, Dec. 2019, doi: 10.1136/bjsports-2018-100193.
Y. Belinkov, N. Durrani, F. Dalvi, H. Sajjad, and J. Glass, “What do Neural Machine Translation Models Learn about Morphology?,” Apr. 2017, doi: 10.18653/v1/P17-1080.
E. Wallace, Y. Wang, S. Li, S. Singh, and M. Gardner, “Do NLP Models Know Numbers? Probing Numeracy in Embeddings,” Sep. 2019, [Online]. Available: http://arxiv.org/abs/1909.07940
E. Hindocha, V. Yazhiny, A. Arunkumar, and P. Boobalan, “Short-text Semantic Similarity using GloVe word embedding,” International Research Journal of Engineering and Technology, 2019, [Online]. Available: www.irjet.net
Q. Xia, E. B. Sifah, K. O. Asamoah, J. Gao, X. Du, and M. Guizani, “MeDShare: Trust-Less Medical Data Sharing among Cloud Service Providers via Blockchain,” IEEE Access, vol. 5, pp. 14757–14767, Jul. 2017, doi: 10.1109/ACCESS.2017.2730843.
Q. Liu, P. Li, W. Zhao, W. Cai, S. Yu, and V. C. M. Leung, “A survey on security threats and defensive techniques of machine learning: A data driven view,” IEEE Access, vol. 6. Institute of Electrical and Electronics Engineers Inc., pp. 12103–12117, Feb. 12, 2018. doi: 10.1109/ACCESS.2018.2805680.
S. Raschka, “Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning,” Nov. 2018, [Online]. Available: http://arxiv.org/abs/1811.12808
