This paper presents a lightweight sentiment classification model based on Long Short-Term Memory networks, developed as a foundational text-analysis component for future multimodal emotion recognition systems, and provides a reproducible and computationally efficient baseline suitable for integration into broader multi...
The work provides a reproducible, explainable, operationally applicable model of sentiment analysis in operationally sensitive, high-stakes Twitter sentiment analysis, and validate the hypothesis that hybrid stacking is an effective method for leveraging the complementary nature of lexical and contextual representation...
The study concluded that advances in deep learning models have significantly improved the accuracy of sentiment analysis, and recommends developing models capable of more effectively handling linguistic complexities, such as sarcasm, irony, regional dialects, and multiple dialects.
This proposed work addresses the persistent challenges of data sparsity, linguistic diversity, and limited annotated resources that hinder sentiment analysis in regional Indian languages by proposing a lightweight yet effective machine learning-based framework for automated sentiment classification of Hindi textual dat...
An innovative ABSA framework that synchronizes enhanced feature engineering with a lightweight hybrid deep learning architecture is proposed that suggests a balanced tradeoff between performance and computational cost making it suitable for real time ABSA applications.
A comparative analysis of two deep learning architectures, Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN), for sentiment classification using textual data showed that BiLSTM performed strongly in identifying neutral and positive sentiments but struggled with negative sentiment dete...
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