2020· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
Experimental results on benchmark datasets show that transformer models outperform traditional methods in accuracy, precision, recall, and F1-score, highlighting that transformer-based approaches provide more efficient and scalable solutions for real-world sentiment analysis applications.
Abstract
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.
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.
M. Arathi, Asripathi Nikhitha· International Journal of Inn...· 0 citations
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 to generate rich contextual word embeddings.
V. Gayatri, Rajani Rajalingam· International Journal for Re...· 0 citations
The analysis of email sentiment is an important aspect of natural language processing because it allows the automated detection of user sentiments. Existing machine learning approaches tend to focus on incorporating dependencies between words and their significance among texts under email analysis. This study offers an innovative solution based on deep learning using Long Short-Term Memory (LSTM) model that includes an attention mechanism. The proposed framework incorporates an LSTM unit to retain the long term dependencies in the email text and an attention mechanism that focuses on the most relevant words for efficient sentiment classification. Hyperparameter tuning is performed for the proposed model via the Bayesian optimization method for better convergence and generalization. For the experimental evaluations, various baseline machine learning and deep learning methods are applied and their performances are compared with the attention-based deep learning models. From the experimental findings, the proposed Attention-based LSTM model outperformed the other models with an accuracy of 98.63%, F1-Score of 90.39%, and Area Under the Curve (AUC) of 0.99. It is also confirmed from the comparison study that the use of deep learning models is better than other techniques in terms of understanding sequential dependencies in the input data. Further improvement in the accuracy of the classification results is achieved through the application of an attention-based technique that emphasizes informative aspects. The incorporation of explainable AI methods into the framework provides valid explanations for model predictions, interpretability and reliability. This study reveals the effectiveness of attention-enhanced sequence models for robust and scalable email sentiment classification.
B. Pradhan, Amiya Ranjan Panda, S. Rautaray et al.· F1000Research· 0 citations
Comparing and analysing the performance of several machine learning algorithms on fine-grained sentiment classification problems to examine their suitability and shortcomings for use as models in sentiment analysis suggests large language models perform significantly worse on the 28-class classification task in zero-shot settings, suggesting that they are better suited for generative and open-ended emotional interaction than for standardized classification benchmarks.
Shangjiafeng Guo· International journal of eng...· 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
Sentiment analysis is one of the most important branches of natural language processing. Sentiment analysis has become a key research area in natural language processing, driven by rapid advancements in deep learning architectures. Where It is used to extract opinions and emotional sentiments from texts about a specific topic or new product, as well as from political and financial news headlines. In recent years, recurrent neural network models, convolutional neural networks, and, notably, transformer-based architectures have significantly improved performance across a wide range of natural language processing tasks. This paper presents a systematic review of deep learning approaches used in sentiment analysis published between 2020 and 2025, following established systematic review guidelines, including a specific search strategy . After applying inclusion and exclusion criteria, a number of final studies were selected for analysis and comparison in terms of methodologies, accuracy, and data used.The study concluded that advances in deep learning models have significantly improved the accuracy of sentiment analysis. The review also recommends developing models capable of more effectively handling linguistic complexities, such as sarcasm, irony, regional dialects, and multiple dialects.
Shaima Orebi· International Innovations Jo...· 0 citations
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