{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T05:46:31Z","timestamp":1773380791829,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>This paper provided multi-class classification models for customer complaints based on neural network architectures. It considered one-dimensional convolutional models corresponding to one, two, and three layers, and also included Long Short-Term Memory and Convolutional Long Short-Term Memory models. The models were assessed through performance indicators such as accuracy, area under the curve, precision, and recall. The findings indicated that Convolutional Long Short-Term Memory exhibited superior performance, achieving 88.06% accuracy and 0.92 area under the curve, respectively. This outperformed one convolutional layer models, Long Short-Term Memory, two convolutional layers, and three convolutional layers models, whose accuracy rates were 86.48%, 84.02%, 83.87%, and 74.71%, respectively. Moreover, Convolutional Long Short-Term Memory also surpassed other models in terms of precision and recall, achieving 0.89 and 0.86, respectively. The study demonstrated that it is superior to simple models to apply a hybrid Convolutional Long Short-Term Memory to classifying customer complaints. This technique combines effectively the advantages of convolutional and recurrent layers, facilitating an ability to learn both local and long-term features. The findings underscored the significance of appropriating optimal neural network architectures for complex text classification problems and also made an original contribution to research on consumer feedback.<\/jats:p>","DOI":"10.31449\/inf.v50i9.8239","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:30Z","timestamp":1773354090000},"source":"Crossref","is-referenced-by-count":0,"title":["Multi-Class Classification of Customer Complaints Using Convolutional LSTM and Neural Network Models"],"prefix":"10.31449","volume":"50","author":[{"given":"Jing","family":"Yi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,12]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/8239\/6569","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/8239\/6569","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:30Z","timestamp":1773354090000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/8239"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":0,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,3,12]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i9.8239","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,12]]}}}