Jul 2026· International Journal of Drug Delivery Technology· Vol 16· 0 citations· 9 references
TL;DR
The research proves that RoBERTa is a very powerful and reliable model for the current sentiment classification problems and can be a major step towards developing intelligent opinion mining and automated text analysis systems.
Abstract
With the vast amount of user-generated content on social media and online platforms, sentiment analysis has emerged as
an important research field in the domain of Natural Language Processing (NLP). Although moderate success has been
obtained by traditional machine learning or deep learning approaches in text sentiment classification, they are not very
good in capturing the semantics in the context, the sarcasm and the informal language patterns used in social media text.
To overcome these drawbacks, this research introduces a RoBERTa based approach for sentiment classification, which
leverages transformer-based contextual embeddings to better understand and classify sentiment. The proposed
methodology includes data pre-processing, Byte-Pair Encoding tokenization, transfer learning and fine-tuning of a pretrained model RoBERTa on a balanced sentiment dataset of textual and sentiment samples. Standard performance metrics
such as accuracy, precision, recall and F1-score are used to evaluate the model. The experimental results presented show
that the proposed framework using RoBERTa outperforms the conventional NLP methods in terms of sentiment
classification performance and interpretation ability. RoBERTa's bidirectional attention mechanism, optimized pretraining
strategy, allows for efficient processing of semantic relationships, social media sentiment and noisy text. The study also
examines the pros and cons of sentiment analysis systems based on transformers and their potential for future
development. Overall, the research proves that RoBERTa is a very powerful and reliable model for the current sentiment
classification problems and can be a major step towards developing intelligent opinion mining and automated text analysis
systems.
Sentiment analysis is an important area of Natural Language Processing (NLP) used to interpret opinions from text such as reviews and social media. Traditional methods, including rule-based and machine learning approaches, struggled with complex language features like sarcasm and context. Deep learning models like RNNs and CNNs improved performance but had limitations in capturing long-range dependencies. Transformer-based models such as BERT, RoBERTa, DistilBERT, and XLNet overcome these issues using self-attention mechanisms to better understand context. This study explores how these models enhance sentiment analysis accuracy through transfer learning, fine-tuning, and domain adaptation. Experimental results on benchmark datasets show that transformer models outperform traditional methods in accuracy, precision, recall, and F1-score. The findings highlight that transformer-based approaches provide more efficient and scalable solutions for real-world sentiment analysis applications.
Ibrahim Lawal· International Journal of App...· 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
Considering the growth of multilingual user made content within social-media platforms, there is an urgent need for developing scalable, language-agnostic approaches for their analysis. Within this paper, we analyze mBERT's performance in sentiment classification in a binary setting as well as the possibility of performing transfer learning between languages. Specifically, the fine-tuned model is applied for sentiment analysis of tweets from the preprocessed TweetEval dataset, obtaining 79.2% of accuracy and 74.7% of the F1 score. It is shown that cross-language transfer learning without any preliminary training on multilingual sentiment datasets provides quite satisfactory performance. However, a more complex approach can be used, which consists of applying filtering of negative sentiments, categorization of subcategories through a sentence transformer with zero-shot settings, and grouping the resulting data in several major categories to obtain severity scores according to frequency thresholds. The application of the sentiment classification with transformers in combination with issue prioritization makes it possible to develop an end-to-end approach to structuring multilingual social media content.
S. Nahar, P. P. Agnihotri· International Journal of Sci...· 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
An integrated framework based on transformer architecture for topic modeling and sentiment analysis for Hindi and Italian social-media discourse, customer reviews and news corpus is introduced and it is suggested that there is clear benefit for morphologically complex text and mixed script text for using contextual embeddings and language-specific pretraining.
Sunita Basalingayya, T. J. Peter· Journal of Intelligent Decis...· 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