The limited dataset size may lead to undersampling, which can negatively affect the model’s performance. Detection difficulties in a wider range of emotions from texts. Struggles to detect a wider ran

The gap

Consensus across the literature

Supporting evidence — 5 representative gaps

• NLP Framework to Safeguard Youngsters Online Using Advanced Transformer-Based Models (2026) · doi

  The limited dataset size may lead to undersampling, which can negatively affect the model’s performance. Detection difficulties in a wider range of emotions from texts. Struggles to detect a wider range of emotions from texts, beyond basic sentiments like positive/ negative. The use of Mechanical Turk and human annotation may not be scalable for creating large- scale emotion lexicons. Data imbalance and cross-lingual applications are not covered. 2 Journal of Data Science and Intelligent Systems Vol. 00 Iss. 00 2026 sentiment analysis using social media text conversations to recognize young users’ harmful or inappropriate communication and interactions, to examine current research studies on sentiment analysis methods and their effectiveness to detect young social media users’ harmful or inappropriate communication and interactions, to evaluate the effectiveness of transformer-based models to recognize harmful and inappropriate content, to analyze the challenges in handling imbalanced datasets and computational resource constraints while enhancing model performance toward real-world applications, and to compare the performance of different NLP models for capturing sentiment nuances within the text. Further, the following steps are used to conduct the study: data collection to retrieve three datasets containing text with different emotions; preprocessing will include text normalization, tokenization, and removal of noise from the dataset; sentiment analysis to apply RoBERTa, BERT, logistic regression (LR), and random forest (RF) for sentiment classification; temporal analysis will include the examination of sentiment trends over time; the event correlation phase will involve the identification of events influencing sentiment fluctuations; and the comparative analysis phase will involve the valuation of RoBERTa and BERT’s performance in sentiment analysis.

  Keywords: sentiment performance text emotions harmful inappropriate dataset model wider range texts detect applications social media
• Sentiment Analysis of Social Media Reviews: A Machine Learning and Deep Learning Approach (2026) · doi

  systems), but results are broadly applicable to any scenario involving textual opinion mining on social media. applications (e.g., II. LITERATURE REVIEW Early work by Pang and Lee [8] demonstrated that machine learning classifiers (SVM, Naïve Bayes) could achieve high accuracy (≈83%) on movie review sentiment data using unigram features. Subsequently, many researchers have explored varied approaches: lexicon-based, ML-based, and deep learning. A. ML and Preprocessing Symeonidis et al. [9] conducted a comprehensive study on Twitter sentiment preprocessing. They evaluated multiple features (n-grams, TF-IDF) and classifiers (Linear SVM, Naïve Bayes, CNN). Their results showed that a CNN using word embeddings outperformed traditional ML, achieving higher classification accuracy [9]. Similarly, Huq et al. [10] applied k-NN and SVM on they reported Twitter data with n-gram features; the moderate accuracy (58–80%) and highlighted © 2026 The Author(s). Published by IJCOPE Journal. Website: https://ijcope.org/ 2 International Journal of Creative and Open Research in Engineering and Management finding importance of feature selection [10]. Amolik et al. [11] analyzed movie-related tweets with feature vector approaches, that SVM yielded better recall/sensitivity than Naïve Bayes [11]. In contrast, Liao et al. [12] compared a simple CNN (with word2vec) against SVM on Twitter, concluding that CNN achieved higher accuracy in Twitter sentiment classification [12]. These studies illustrate that deep models often surpass shallow classifiers when sufficient data is available. B. Deep Learning Methods Convolutional and recurrent neural networks have become popular. For instance, a CNN+word2vec model on a Twitter dataset achieved balanced precision/recall of 88.7% [13]. Another study by Zheng et al. [14] used a hybrid bidirectional RNN on mixed datasets (Sogou news, Yelp, Douban reviews), achieving accuracy up to ~97% on some data. Zhao et al. [15] proposed a weakly- supervised deep embedding model for Amazon product reviews, reaching 87.9% accuracy. These advances show the power of DL architectures for sentiment analysis. However, DL performance can depend heavily on data size and representation quality. C. Contextual Embeddings (BERT and Transformers) found Recent approaches use large pre-trained language models. Basarslan and Kayaalp [1] compared word embedding methods (Word2Vec, GloVe, BERT) and classifiers on multiple review datasets (IMDb, Yelp, Twitter). They that models using BERT embeddings "have the best performance" over TF-IDF or static embeddings [16]. For example, BERT-based models achieved up to 94–98% accuracy on benchmarks, outperforming traditional ML by 5–10 percentage points [1]. This agrees with the broader literature: contextual models capture nuances of language that simple bag-of- words methods miss [6][7]. D. Research Gaps is a Despite numerous studies, gaps remain. Many papers evaluate one or two datasets in isolation, without cross- lack of systematic domain analysis. There comparison of modern Transformer-based models versus classic methods on review data. Furthermore, few studies examine hybrid pipelines that combine multiple feature types or adapt pretrained models specifically for social reviews. Our work addresses these gaps by benchmarking diverse approaches on the same datasets and proposing an integrated method. social media ISSN: 3108-1754 (Online) Volume 02 Issue 04 April-2026 | Impact Factor: 3.5 III. METHODOLOGY This section details the proposed sentiment analysis framework. We adopt a hybrid pipeline combining advanced text representation with a neural classifier. Key components are: (i) text preprocessing, (ii) feature extraction, (iii) classification model, and (iv) training loss. The overall system architecture is illustrated conceptually in Fig. 1. [← Fig. 1: Proposed BERT+BiLSTM Framework →] Fig. 1. Proposed sentiment analysis framework: input text → BERT encoder → BiLSTM → Softmax classifier. A. Preprocessing Raw text reviews are first cleaned by lowercasing, removing URLs, user mentions, and non-alphanumeric characters. Standard NLP preprocessing such as and tokenization, stemming/lemmatization are applied to normalize input. This step reduces noise in social media text, consistent with prior studies [9]. stop-word removal, B. Feature Extraction of vectors

  Keywords: accuracy models sentiment twitter word bert preprocessing feature text social review classifiers approaches based deep
• Deep learning-based sentiment analysis of customer reviews using bidirectional LSTM (2026) · doi

  This research presents an effective and robust approach for sentiment analysis using a Bidirectional Long Short-Term Memory (BiLSTM) model. The primary objective of the study was to classify customer reviews into positive and negative sentiments by leveraging deep learning techniques and a well-structured Natural Language Processing pipeline. The proposed methodology integrates key steps such as data cleaning, text preprocessing, tokenization, sequence padding, and word embedding, followed by model construction using the BiLSTM algorithm. This systematic approach ensures that raw textual data is transformed into meaningful representations, enabling accurate sentiment classification. 1713 World Journal of Advanced Research and Reviews, 2026, 30(01), 1703-1716 The experimental results demonstrate that the proposed model achieves exceptionally high performance, with accuracy reaching nearly 100% on both training and validation datasets. The model also maintains a strong balance between precision and recall, resulting in a high F1-score, which confirms its effectiveness even in the presence of class imbalance. The training and validation graphs further indicate that the model converges quickly, with minimal loss and stable learning behaviour. The confusion matrix analysis shows that almost all predictions are correct, with negligible misclassification, highlighting the reliability of the system. A key strength of this research lies in the use of the BiLSTM algorithm, which processes textual data in both forward and backward directions. This bidirectional learning mechanism allows the model to capture contextual relationships between words more effectively than traditional machine learning models. As a result, the proposed system significantly outperforms conventional approaches such as Naive Bayes and Support Vector Machines, which lack the ability to understand sequential dependencies in text. Additionally, the use of regularization techniques such as dropout and early stopping plays a crucial role in improving model generalization and preventing overfitting. The incorporation of class weighting further ensures balanced learning across different sentiment classes. Overall, the developed system proves to be scalable, efficient, and suitable for real- world applications such as customer feedback analysis, product review monitoring, and opinion mining. 5.1. Future Work Despite achieving high accuracy and strong performance, there are several opportunities for future enhancement. The current model focuses on binary classification; however, it can be extended to multi-class sentiment analysis to capture more detailed sentiment categories such as neutral or highly polarized sentiments. Future work may also include aspect-based sentiment analysis, which can identify sentiments related to specific product features or attributes, providing deeper insights for business decision-making. Furthermore, integrating advanced transformer-based models such as BERT can further improve contextual understanding and overall model performance. In addition, the development of real-time sentiment analysis systems capable of processing streaming data from social media platforms can enhance the practical applicability of the model. Extending the framework to support multilingual sentiment analysis is another promising direction, enabling the system to handle diverse datasets across different languages. These enhancements will further strengthen the scalability, adaptability, and real-world usability of the proposed sentiment analysis system.

  Keywords: model sentiment learning system proposed further bilstm sentiments world high performance class real future approach
• An Efficient Deep Learning Framework for Real-Time Product Recommendation in E-Commerce (2026) · doi

  approach that improves personalization by leveraging user review polarity. • Enhances recommendation accuracy by capturing contextual meaning and sequential patterns in textual feedback using deep learning. • Improves learning from imbalanced e-commerce datasets, leading to more reliable and unbiased predictions. • Achieves consistently performance compared to existing ML and DL baselines across all evaluation metrics. superior • Strengthens real-time recommendation quality by linking user sentiment with product directly relevance. • Demonstrates high scalability and robustness for deployment in large-scale e-commerce environments. For e-commerce real-time product recommendation, the proposed strategy is innovative since it combines sentiment analysis with an LSTM-based DL model. Rather of relying on generic recommendations, it improves customization by using the emotion of user reviews. With the help of SMOTE, the model is able to handle class imbalance and accurately capture sequential textual patterns, resulting in more accurate predictions. Its better performance over conventional ML and DL approaches across all of the evaluation measures justifies its explanation. Its ability to provide scalable, accurate, and sentiment-aware product suggestions has been proven to greatly improve customer satisfaction. A. Structure of Paper The rest of the paper is organized as follows: Section II reviews the relevant literature. A thorough description of the recommended method is given in Section III. The experiments and their findings are presented in Section IV. Lastly, Section V concludes and outlines future directions. II. LITERATURE REVIEW The following sections include machine learning, product recommendation systems and a literature review on techniques and algorithms used to develop better recommendation systems. images. To handle noisy and Siddharth and Sariki, (2025) present a multimodal deep learning approach, in this study that uses a fusion-based model to merge structured attribute information with product incomplete metadata, we apply preprocessing steps such as one-hot encoding and class balancing, while CNNs are used to extract RichVisual features. Our experiments show that the fusion model consistently outperforms image-only and attribute-only baselines, reaching 84.2% ± 2.1 test accuracy and a macro F1score of 0.87 across five folds [9]. Goranthala et al., (2025) use of the GPU-YOLO Ensembled Classifier is essential in reducing the usual problems of bias and variance that older classifiers have. The Am. J. Interdiscip. Innov. Res. 2026 57 The American Journal of Interdiscipli

  Keywords: recommendation product learning model improves user review commerce across sentiment literature approach accuracy sequential patterns
• An Efficient Deep Learning Framework for Real-Time Product Recommendation in E-Commerce (2026) · doi

  This study proposes a new approach to e-commerce product combining sentiment analysis with a LSTMmodel based on DL. The system is structured to efficiently handle large volumes of user-generated Amazon review data, using systematic preprocessing. The LSTM architecture which is comprised of embedding, stacked LSTM, and dense layers is trained on the Binary Cross-Entropy loss and the AdamOptimizer to ensure effective convergence. Experimental results show that performance is excellent, with an acc of 98.42, and F1score of 98.70, which indicate a high predictive performance and balanced classification performance. This is further indicated by the ROC-AUC score that indicates better class separability. Evaluations against both conventional ML and competing DL models demonstrate the superiority of the suggested method for extracting complicated sequential patterns from textual input. This approach is great for e-commerce systems because it improves suggestion quality by considering user sentiment, which improves customization and decision making. The proposed framework may be used in real-time recommendation systems and the creation of intelligent e-commerce apps since it is generally scalable, precise, and efficient. The proposed model has some disadvantages even when it is doing well. It may not pick up on indicators of user activity because of its dependence on textual review data. Also, TF-IDF is ineffective in terms of the ability to extract in-depth semantic meaning. The model has high computing resource demands, which might compromise scalability in real-time. In future studies, greater contextual understanding can be realized through a combination of transformer-based models such as BERT and multi-modal information such as user behavior and product metadata. The computational cost and real-time efficiency of large-scale e-commerce platforms may be further optimized. REFERENCES 1. K. Dixit, “Predictive Analytics in Business Intelligence for Sales Forecasting,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 60, no. 3, p. 981, Sep. 2023, doi: 10.48175/IJARSCT-12750G. in 2024 2. M. S. Rahman, T. D. Sarkar, U. T. Mitasha, M. S. Mia, and S. Karthikeyan, “E-commerce-based Smart Recommendation System using element-by-element collaborative filtering following with the Machine Learning Technology,”

  Keywords: commerce user based performance real time approach product sentiment system large review using lstm score