Advances in Sentiment Analysis in Deep Learning Models and Techniques

The article investigates the advantages, disadvantages, and areas of research that need more exploration regarding deep learning architectures used in sentiment analysis. These architectures let models learn complex language features from data without explicit feature engineering, changing sentiment analysis. The models' capacity to capture long-range dependencies has improved their context and nuanced expression interpretation, especially in long or metaphorical texts. Deep learning sentiment analysis algorithms have improved, yet they still face obstacles. The complexity of these models raises ethical questions about bias and transparency. They also require huge, annotated datasets and computational resources, which limits their use in resource-constrained contexts. Adopting deep learning models requires balancing performance and practicality. Explore critical deep learning sentiment analysis research gaps. Cross-domain and cross-lingual sentiment analysis requires context- and language-specific models. Textual and non-textual multimodal sentiment analysis offers untapped potential for complex sentiment interpretation. Responsible AI deployment requires model interpretability, robustness against adversarial assaults, and domain consistency. Finally, deep learning and sentiment analysis have changed our knowledge of human emotion. Accuracy and contextual comprehension have improved, but model transparency, data prerequisites, and practical applicability remain issues. Overcoming these restrictions and exploring research gaps will enable responsible sentiment analysis AI innovation

Text Classification: A Review, Empirical, and Experimental Evaluation

The explosive and widespread growth of data necessitates the use of text classification to extract crucial information from vast amounts of data. Consequently, there has been a surge of research in both classical and deep learning text classification methods. Despite the numerous methods proposed in the literature, there is still a pressing need for a comprehensive and up-to-date survey. Existing survey papers categorize algorithms for text classification into broad classes, which can lead to the misclassification of unrelated algorithms and incorrect assessments of their qualities and behaviors using the same metrics. To address these limitations, our paper introduces a novel methodological taxonomy that classifies algorithms hierarchically into fine-grained classes and specific techniques. The taxonomy includes methodology categories, methodology techniques, and methodology sub-techniques. Our study is the first survey to utilize this methodological taxonomy for classifying algorithms for text classification. Furthermore, our study also conducts empirical evaluation and experimental comparisons and rankings of different algorithms that employ the same specific sub-technique, different sub-techniques within the same technique, different techniques within the same category, and categorie

An Ensemble Model-Based Recommendation Approach for Consumer Decision-Making System

A recommendation system can suggest items aligned with diverse user interests by leveraging multiple sources of information. While many recommendation systems heavily rely on the collaborative filtering (CF) approach—where user preference data is combined with others to predict additional items of potential interest—this study introduces a novel weighted recommendation system to enhance consumer decision-making using CF. The methodology includes the development of equations to calculate the weights for both the product and review, as well as to determine the similarity between consumer reviews. To ensemble the model, Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) are employed in the methodology. The study considers Ensemble Classifiers (RF+SVM+LR) to implement the results, aiming for improved outcomes compared to prior research. The proposed model is trained and tested using an open-source dataset on Kaggle's website. Numerical analysis of the proposed model reveals superior performance, outperforming conventional methods in terms of accuracy (0.821), precision (0.802), recall (0.821), F-measure (0.833), error rate (0.100), and more

A Machine Learning Ensemble Model for the Detection of Cyberbullying

The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms. Motivated by this necessity, we present this paper to contribute to developing an automated system for detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to previous experiments on the same dataset. We employed the stacking ensemble machine learning method, utilizing four various feature extraction techniques to optimize performance within the stacking ensemble learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we achieved superior results compared to traditional machine learning classifier models. The stacking classifier achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive

Enhancing YouTube Spam Detection

This culminating experience project investigated various methods for enhancing spam detection on YouTube, a prevalent issue impacting user experience and platform integrity. The research questions addressed were: Q1) How do different spam detection methods compare regarding robustness, efficiency, and accuracy? Q2) What role do deep learning approaches like RNNs and CNNs play in improving spam comment identification? Q3) What are the unique benefits of using deep learning models for spam comment identification on YouTube? Q4) How can machine learning models be optimized for real-time spam detection on YouTube? The study gave adequate findings that explained each research question. In the case of (Q1), while algorithms like the Naïve Bayes and Logistic Regression offered precision in identifying spam emails, the models have proven ineffectual at adapting to new forms of spam and constant enhancement in spam techniques, deep learning algorithms like the CNN and RNN offered high accuracy through their robustness due to the models\u27 abilities of feature extraction independently from the text data. The results shown in (Q2) indicate that RNNs and CNNs are critical in transforming the level of spam detection by addressing the problem of semantic meaning and temporal relationships in comments and surpassing traditional methods. Concerning (Q3), it was pointed out that deep learning models are the most accurate, scalable, and resistant to false negatives when identifying spam comments on the videos hosted on YouTube, which helps regain users\u27 trust and enhance the platform\u27s security as the traffic continues to grow. (Q4) was focused on advancing machine learning models for real-time processing, using methods such as model pruning and distribution. The findings were as follows: (Q1) found that although conventional approaches are efficient at meeting accurate results, deep learning models are highly effective in dealing with the changes in spam strategies. (Q2) pointed out that RNNs and CNNs contribute immensely to discovering spam in SM platforms due to their raw power in NLP and pattern recognition. (Q3) established that the deep learning models\u27 accuracy, scalability, and adaptability, including CNN and RNN, are beneficial in identifying spam on YouTube due to their effectiveness in tackling the ever-evolving spam tactics. (Q4) It has emerged that the fine-tuning of machine learning models is imperative for scaling up the approaches by deploying high-end methodologies for real-time spam detection, which subserves the daunting task of training the algorithms to deal with the flood of user-generated content in the context of YouTube. Areas of further study include analyzing other complex natural language processing methods combined with classifiers for better spam identification, improving the computational time for multi-modal learning for spam comment detection, and considering federated learning for real-time spam identification on platforms such as YouTube. These research directions are being carried out to boost the existing permutations and improve the permeate spam detection technologies in Information Systems so that they can be efficient, effective, and highly accurate systems capable of coping with the newly emerged spam technologies in flexible, transparent, and effective ways