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Abstract This paper seeks to investigate the use of Natural Language Processing techniques in analyzing the Bhagavad Gita, a fundamental text in Hindu philosophy that has stood the test of time and centuries of interpret...

This study presents Duygu-Turk, a novel deep learning-based sentiment analysis framework specifically designed for the Turkish language which is characterized by its agglutinative and morphologically rich structure. Unli...

Customer feedback is pivotal in enhancing service quality and user satisfaction across digital platforms. However, traditional sentiment analysis methods often struggle with informal languages, contextual nuances, and as...

In classical vernacular Chinese fiction, inferring affective stance in the presence of rhetorical devices often depends on implicit semantics and contextual cues, which poses substantial challenges for joint pragmatic mo...

This study aims to identify the best predictive model for analysing online product reviews (OPRs) in the electronics industry, with a secondary focus on leveraging unstructured customer feedback to support product improv...

Understanding how consumers behave in e-commerce is essential for businesses, especially in today’s digital world where people rely heavily on online shopping platforms. A key part of this understanding comes from sent...

This paper addresses the growing need for implementing intelligent Natural Language Processing (NLP) systems on low-power, memory-limited devices such as Raspberry Pi, mobile phones, and IoT edge hardware. As edge comput...

Opinion mining, a critical subfield of natural language processing, seeks to extract and analyze user-generated expressions. This paper proposes a novel Multi-Task Opinion Mining framework that integrates psychological i...

Abstract This research proposes an innovative Arabic financial forecasting model that integrates linguistic relation extraction with advanced deep learning and machine learning techniques. The framework combines Arabic O...

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tunin...