Title: Sentiment analysis using deep learning algorithms based on chat records and product reviews

Authors: Haili Lu; Lin He

Addresses: Faculty of Education, Shaanxi Normal University, Xi'an, 710062, China ' Faculty of Education, Shaanxi Normal University, Xi'an, 710062, China; School of Humanities, Weinan Normal University, Weinan, 714000, China

Abstract: With globalisation and the popularity of the internet, consumer evaluation and product feedback are no longer limited to traditional channels, but expressed through online chat and product reviews. These comments contain a wealth of emotional information and viewpoints, which are of great value to the enterprise. In response to the difficulty of traditional sentiment analysis models in handling complex emotional expressions and semantic information, a method combining support vector machines with bidirectional long short-term memory networks is proposed. The experimental results show that the average classification error of this model is less than 2.4% on the LAMAZON and Yelp datasets, which is superior to other schemes. In contrast, the fitting degree of this model is 99.7%, ranking the highest among all algorithms, and the accuracy of emotion classification exceeds 90%. Therefore, the model combining SVM and BiLSTM performs well in sentiment analysis tasks with high accuracy, providing valuable decision support for enterprises.

Keywords: support vector machine; SVM; long short-term memory network algorithm; sentimental analysis techniques; chat records; product reviews.

DOI: 10.1504/IJWET.2025.151155

International Journal of Web Engineering and Technology, 2025 Vol.20 No.4, pp.358 - 380

Received: 08 Apr 2024
Accepted: 15 Jan 2025
Published online: 15 Jan 2026 *

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