Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sentence Classification Using Attention Model for E-Commerce Product Review Subject The topic of the resource Deep Learning; Linear Support Vector Machine (LSVC); Logistic Regression (LR); Machine Learning; Multinomial Nae Bayes (MNB); N-Gram; Natural Language Processing (NLP); Neural Networks; Random Forest (RF); Text Classification; TF-IDF Description An account of the resource The importance of aspect extraction in text classification, particularly in the e-commerce sector. E-commerce platforms generate vast amounts of textual data, such as comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Aspect extraction involves identifying and classifying individual traits or aspects mentioned in textual reviews to understand customer opinions, improve products, and enhance the customer experience. The role of product reviews in e-commerce is discussed, emphasizing their value in aiding customers' purchase decisions and guiding businesses in product stocking and marketing strategies. Reviews are essential for boosting sales potential, maintaining a good reputation, and promoting brand recognition. Customers extensively research product reviews from different sources before purchasing, making them vital user-generated content for e-commerce businesses. The current work provided an efficient and novel classification model for sentence classification using the ABNAM model. The automated text classification models available cannot categorize the data into sixteen distinct classes. The technologies applied for the mentioned work contain TF-IDF, N-gram, CNN, linear SVM, random forest, Nae bays, and ABNAM with significant results. The best-performing ML method for the successful classification of a given sentence into one of the sixteen categories is achieved with the proposed model named the based Neural Attention Model (ABNAM), which has the highest accuracy at 97%. The research acclaimed ABNAM as a novel classification model with the highest-class categorizations. 2024 Nagendra N and Chandra J. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Creator An entity primarily responsible for making the resource Nagendra N.; Chandra J. Source A related resource from which the described resource is derived Journal of Computer Science, Vol-20, No. 5, pp. 535-547. Publisher An entity responsible for making the resource available Science Publications Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3844/jcssp.2024.535.547" target="_blank" rel="noreferrer noopener">https://doi.org/10.3844/jcssp.2024.535.547</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188239085&amp;doi=10.3844%2Fjcssp.2024.535.547&amp;partnerID=40&amp;md5=b28053f96307a12a68fb5e458856566a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188239085&amp;doi=10.3844%2fjcssp.2024.535.547&amp;partnerID=40&amp;md5=b28053f96307a12a68fb5e458856566a</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 15493636 | CS; 2023-2024; Vol-1; 00093-00106 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nagendra N., Department of Computer Science, Christ Deemed to be University, Bengaluru, India; Chandra J., Department of Computer Science, Christ Deemed to be University, Bengaluru, India