Aug 2026· Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology· 0 citations· 27 references
TL;DR
This is the first application of DeepONet to textual emotion recognition, introducing a fundamentally different perspective on function approximation in language understanding tasks and suggesting that DeepONet can serve as a robust alternative framework for capturing and modeling complex relationships in natural language processing tasks and opens new avenues for operator-based learning in text analysis.
Abstract
Emotion detection from textual data is a key challenge in natural language processing (NLP), playing an important role in applications such as sentiment analysis, human-computer interaction, and psychological evaluation. Growing utilization of social networks and online portals leads to the creation of a huge amount of reviews and ratings. Analyzing users’ and customers’ reviews and opinions are so important for governments and businesses. While recent advances have primarily leveraged transformer-based architectures for this task, we propose a novel approach by employing Deep Operator Networks (DeepONets), originally designed for learning operators in scientific computing, to model the mapping between textual representations and emotional states. In this study, we extract high-dimensional semantic embeddings from text using a pre-trained sentence transformer model and feed these embeddings into a DeepONet architecture for emotion classification. The primary contribution of this work lies in architectural innovation. To the best of our knowledge, this is the first application of DeepONet to textual emotion recognition, introducing a fundamentally different perspective on function approximation in language understanding tasks. Results obtained from experiments on benchmark emotion-labeled datasets indicate that our proposed model attains performance and results comparable to related baselines, with notable generalization capabilities across emotion categories. To ensure a fair and comprehensive evaluation, we assessed the performance of the proposed model using widely adopted classification metrics, including accuracy, precision, recall, and F1-score. We used two datasets and the results were around 80 percent on one dataset for all named metrics, and around 88 percent on the other. The findings suggest that DeepONet can serve as a robust alternative framework for capturing and modeling complex relationships in natural language processing tasks and opens new avenues for operator-based learning in text analysis.
Nowadays, Natural Language Processing, or NLP, is a key component of many programs that analyze and comprehend human language. The sentiment analysis of mobile product reviews collected from the Kaggle repository—more especially, the 20,710-review Amazon Mobile evaluations dataset—is the main emphasis of this research. Reviews of well-known cellphone companies including Samsung, Nokia, Apple, Redmi, and others are included in the dataset. The main goal of this research is to categorize consumer attitudes into three groups: neutral, negative, and positive. This research heavily relies on Natural Language Processing (NLP), particularly in the commercial and e-commerce domains where decision-making and product enhancement depend on a comprehension of client input. In this research, sentiment categorization is carried out using deep learning methods like Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN). Lowercase conversion, punctuation and symbol removal, tokenization, stopword removal, stemming, and lemmatization are some of the preprocessing methods used to enhance text quality and model performance. The algorithms are compared based on execution time and accuracy. The survey also determines the top-performing mobile brand by counting the amount of positive reviews. The outcomes show that deep learning models perform better and produce the best results. This research promotes business intelligence in the e-commerce industry and advances our understanding of consumer sentiment behavior.
Dhananchezhiyan R, M. Rameshkumar· International journal of com...· 0 citations
With the widespread growth of digital platforms, online interaction has become an essential part of everyday life. Users
frequently express their opinions, feedback, and emotions through reviews and comments on various platforms. Analyzing such
textual data plays a crucial role in understanding user sentiment and supporting effective decision-making. However, sentiment
analysis faces several challenges, including long-range dependencies within text and the presence of unknown words and
symbols. Traditional sentiment analysis approaches mainly rely on sequential models, which process text step by step and often
require higher computational time. In contrast, Transformer-based models offer improved efficiency through parallel
processing. To address these challenges, this paper presents a context-aware hybrid deep learning approach by integrating the
Robustly Optimized BERT Pretraining Approach (RoBERTa) with Bidirectional Long Short-Term Memory (BiLSTM) networks.
RoBERTa is employed to generate rich contextual word embeddings, while BiLSTM captures long-term semantic dependencies
by processing text in both forward and backward directions. The proposed model is trained and evaluated on the Twitter US
Airline Sentiment dataset comprising 14,299 samples across three sentiment classes. Experimental analysis demonstrates that
the hybrid approach achieves an accuracy of 85.14% and an F1-score of 0.8487, highlighting its effectiveness for sentiment
analysis tasks compared to baseline models
Dr. Veguru Gayatri, Dr. Rajani Rajalingam· International Journal for Re...· 0 citations
Sentiment analysis has become an essential Natural Language Processing (NLP) technique for extracting opinions and emotions from textual data generated through social media, online reviews, blogs, and customer feedback. Although deep learning models such as Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and other transformer-based architectures have achieved remarkable accuracy in sentiment classification, their black-box nature limits interpretability and user trust. Explainable Artificial Intelligence (XAI) addresses this limitation by providing transparent and understandable explanations for model predictions, enabling users to identify the key words, phrases, and contextual features that influence sentiment classification. This paper presents a comprehensive study of Explainable AI techniques applied to sentiment analysis, focusing on both model-agnostic methods, including Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), and attention-based explanation mechanisms. The proposed framework integrates text preprocessing, feature extraction using TF-IDF and contextual embeddings, sentiment classification through machine learning and deep learning models, and explanation generation to improve model transparency. Performance is evaluated using publicly available sentiment datasets based on accuracy, precision, recall, F1-score, and explanation quality. Experimental results demonstrate that XAI techniques significantly enhance the interpretability of sentiment prediction without substantially compromising classification performance. Furthermore, explainable sentiment analysis supports fairness assessment, bias detection, regulatory compliance, and informed decision-making in critical domains such as healthcare, finance, education, and social media analytics. The findings highlight that integrating explainability with sentiment analysis not only increases model reliability but also promotes greater user confidence and responsible deployment of artificial intelligence systems.
Aishwarya P. A., N. K· International Journal of Res...· 0 citations
With the rapid growth of e-commerce platforms, understanding customer feedback has become crucial for businesses to improve their products and services. Sentiment analysis, a subfield of Natural Language Processing (NLP), plays a vital role in extracting opinions from user-generated content such as product reviews. This project aims to perform sentiment analysis on e-commerce product reviews using a hybrid deep learning approach. A CNN-LSTM (Convolutional Neural Network - Long Short-Term Memory) model was implemented to classify customer reviews into positive and negative sentiments. The CNN layer effectively captures local features from the text, while the LSTM layer processes sequential dependencies for better contextual understanding. The dataset was preprocessed using techniques such as tokenization, stop word removal, and padding, followed by word embedding for semantic representation. Experimental results demonstrate that the CNN-LSTM model achieves an impressive accuracy of 95%, outperforming traditional machine learning models. The proposed model provides an efficient solution for automating sentiment classification in large-scale e-commerce platforms.
Mounika Garikapati, Nageswara Rao Kapu· International Journal For Mu...· 0 citations
Sentiment analysis and sarcasm detection as become an important area in natural language processing
(NLP) due to growth of e-commerce and social media platforms. Customers give feedback through reviews which
helps to understand the contextual meaning and sentiment present in the text.The system integrates DistilBERT and
CNN-LSTM hybrid model where CNN (Convolutional Neural Network) used to extract the local features, and LSTM
(Long Short-Term Memory) learns sequential and long-term dependencies in the text, and DistilBERT is used to
capture contextual embeddings in the text. The proposed system processes the reviews through preprocessing
techniques including text cleaning, negation handling, and tokenization. Experimental results shows that the hybrid
model achieves high accuracy compared to standalone and architecture architectures. The proposed model is applied in
e-commerce platforms, recommendation systems and customer feedback analysis for better decision making.
Unknown authors· International Journal of Inn...· 0 citations
Understanding emotional tone and sentiment in text has driven significant advancements in Natural Language Processing (NLP), particularly in sentiment analysis and emotion detection. This study addresses the challenge of developing effective NLP tools for low-resource languages, focusing on the Hausa language. By leveraging ensemble methods and pre-trained transformer models like BERT and XLM-R, along with traditional classifiers such as Logistic Regression, SVM, Naive Bayes, Random Forest, and XGBoost, we aim to improve sentiment analysis and emotion detection for Hausa text. Utilizing a balanced sentiment dataset (9,958 samples) and a complex multi-label emotion dataset (19,757 samples across 11 categories), we benchmark individual classifiers, voting ensembles, and deep contextual models. For sentiment analysis, a Hard Voting Ensemble of TF-IDF-vectorized base learners achieved a highly competitive F1-score of 0.8748. However, Transformer models significantly outperformed traditional baselines, with Multilingual BERT (mBERT) achieving a peak F1-score of 0.8983. In the multi-label emotion detection task, individual traditional models struggled with label sparsity, yielding low Subset Accuracy scores (2.88% to 8.30%) and moderate Micro-F1 scores. Standard Hard Voting ensembles further underperformed due to discrete prediction conflicts. To resolve this, a Probability-based Majority Voting mechanism with calibrated thresholding (0.3) was introduced, boosting the Micro-F1 to 0.3825 and reducing the Hamming Loss to 0.1967. Ultimately, XLM-RoBERTa emerged as the superior architecture, achieving a Subset Accuracy of 0.1545, a Micro-F1 of 0.4275, and the lowest Hamming Loss of 0.1804. This research establishes a rigorous benchmark for Hausa NLP, highlighting the indispensable role of subword tokenization, contextual embeddings, and threshold calibration in handling the morphological richness and multi-label complexities of low-resource languages
Taiwo Kolajo, Kabir Garba· International journal of res...· 0 citations
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