Sentiment Analysis Research Articles

Sentiment analysis based on multimodal feature fusion

Sentiment analysis based on multimodal feature fusion

Emotional Boundaries and Intensity Aware Model for Incomplete Multimodal Sentiment Analysis

Emotional Boundaries and Intensity Aware Model for Incomplete Multimodal Sentiment Analysis

Neutrosophic Sets in Big Data Analytics: A Novel Approach for Feature Selection and Classification

Big Data Analytics are said to help in transforming huge amounts of raw data towards valuable information that can be used, but there are formidable challenges in feature selection and classification due to the complexity and high dimensionality of the data. Traditional methods are usually too weak to handle the built-in uncertainty, imprecision, and inconsistency within big data and they often fail to perform well. This paper aims to induce the new methodology on these problems using the sets of neutrosophic in dealing with more flexible and nuanced data analysis. The key contributions to the current approach proposed are threefold. First, generalization of the classical set through extension of the notions of truth, indeterminacy, and falsity by allowing representations of uncertainty in data. The second combines a powerful process for selecting features based upon neutrosophic set theory that is optimal by genetic algorithms and advances a step further by applying these features in training and validating the classification models across a set of different domains. Therefore, the major aim from this study is to increase accuracy and reliability in feature selection and classification in big data analytics. This methodology has been implemented and tested over datasets of the following types: healthcare, finance, social media, and more. Results have proved great improvement against conventional performance metrics, for example, the classification accuracy with an SVM classifier over the Cleveland Heart Disease dataset increases from 83.5% to 87.2%, and of a Random Forest classifier over a financial dataset from 76.4% to 81.9%. For instance, the accuracy of social media sentiment analysis changed to 82.7% from 78.3%. All these findings establish that the neutrosophic set-based method holds good advantages in addressing the limitations of classical alternatives. The proposed approach of neutrosophism, through an explicit model, enhances performances in classifications and, at the same time, augments overall robustness and reliability in big data analytic. The importance of this study lies in establishing the groundwork for further research and practical applications, thus indicating possible further development in this field.

Understanding students’ sentiment from feedback with a new feature selection and semantics networks

Sentiment analysis of students’ feedback using machine learning algorithms has emerged as a valuable tool for understanding students’ sentiments and improving educational outcomes. Currently, existing systems use frequency-based methods for feature selection (e.g., Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW)) not to capture the subtleties of emotions expressed in student feedback and do not provide insights into the specific concerns of students via topics or themes. In this study, we propose the Student Sentiment from Feedback (SSF) framework, which includes four main procedures: pre-processing, feature selection, classification, and theme finding. The SSF framework classifies student sentiments and subsequently groups feedback into themes using semantic networks based on word co-occurrence. Our innovative feature selection approach combines TF-IDF with sentiment-based features derived from SentiWordNet and intensifiers, creating a robust feature vector that enhances the dataset’s richness and improves classification accuracy and robustness. In the experiments, we utilize a public dataset from Kaggle, applying our proposed method and various machine learning models (e.g., k-nearest neighbor, decision tree, random forest, multilayer perceptron, support vector machine, gradient boosting, and extreme gradient boosting). The experimental results show that our concatenated features achieve the highest accuracy across all machine learning models (greater than 0.82). Our study demonstrates the efficacy of this hybrid feature selection method, contributing to better understanding and decision-making in educational settings.

Optimizing Airline Review Sentiment Analysis: A Comparative Analysis of LLaMA and BERT Models through Fine-Tuning and Few-Shot Learning

Optimizing Airline Review Sentiment Analysis: A Comparative Analysis of LLaMA and BERT Models through Fine-Tuning and Few-Shot Learning

Two Stage Portfolio Optimization Trough Sentiment Analysis: A Data Envelopment Analysis (DEA) Based Approach

Two Stage Portfolio Optimization Trough Sentiment Analysis: A Data Envelopment Analysis (DEA) Based Approach

Multimodal Sentiment Analysis with Mutual Information-based Disentangled Representation Learning

Multimodal Sentiment Analysis with Mutual Information-based Disentangled Representation Learning

Sentiment analysis of product reviews based on BERT_BiLSTM model

Sentiment analysis of product reviews based on BERT_BiLSTM model

Sentiment Analysis of Mobile Phone Reviews Using XGBoost and Word Vectors

Consumer reviews are an important source of data used to judge and examine consumer sentiment, and data mining for reviews of electronic products is an important way to help improve the design of electronic products. The research is based on the consumer reviews of online cell phone e-commerce, The paper constructs a sentiment dictionary in this field based on the Sentiment Oriented Point Mutual Information (SO-PMI) algorithm, and the sentiment weight of the review word vectors. An extreme Gradient Boosting Tree (XGBoost) is used to integrate word vectors and a Large Language Model (LLM) to construct a sentiment recognition model, and finally, a review sentiment index is derived, which unfolds from multiple dimensions to analyze the sentiment tendency in consumer reviews. The empirical analysis shows that the accuracy, recall, area under the curve (AUC), and other validation indexes of the constructed sentiment recognition model are further improved compared with the LLM model, which has a certain application value. When applying the weighted word vector method, the model has been significantly improved compared with the LLM model, the accuracy is increased by 5%, the accuracy is increased by 10%, and the comprehensive accuracy is increased by 2% after the comprehensive application of the two.

Neutrosophic Net-RBF Neural Networks with Bayesian Optimization Based Sentiment Analysis on Low Resource Language

Neutrosophic Net-RBF Neural Networks with Bayesian Optimization Based Sentiment Analysis on Low Resource Language

Large language model augmented syntax-aware domain adaptation method for aspect-based sentiment analysis

Large language model augmented syntax-aware domain adaptation method for aspect-based sentiment analysis

The advantages of lexicon-based sentiment analysis in an age of machine learning.

Assessing whether texts are positive or negative-sentiment analysis-has wide-ranging applications across many disciplines. Automated approaches make it possible to code near unlimited quantities of texts rapidly, replicably, and with high accuracy. Compared to machine learning and large language model (LLM) approaches, lexicon-based methods may sacrifice some in performance, but in exchange they provide generalizability and domain independence, while crucially offering the possibility of identifying gradations in sentiment. We demonstrate the strong performance of lexica using MultiLexScaled, an approach which averages valences across a number of widely-used general-purpose lexica. We validate it against benchmark datasets from a range of different domains, comparing performance against machine learning and LLM alternatives. In addition, we illustrate the value of identifying fine-grained sentiment levels by showing, in an analysis of pre- and post-9/11 British press coverage of Muslims, that binarized valence metrics give rise to different (and erroneous) conclusions about the nature of the post-9/11 shock as well as about differences between broadsheet and tabloid coverage. The code to apply MultiLexScaled is available online.

Global Perspectives on Laptop Features: A Sentiment Analysis of User Preferences in Developed and Developing Countries

Global Perspectives on Laptop Features: A Sentiment Analysis of User Preferences in Developed and Developing Countries

MMTF-DES: A fusion of multimodal transformer models for desire, emotion, and sentiment analysis of social media data

MMTF-DES: A fusion of multimodal transformer models for desire, emotion, and sentiment analysis of social media data

Improving Thai Sentiment Analysis Accuracy with Emoji Classification by Deep Learning and Stacking Models: A Case Study of Hotel Reviews

Improving Thai Sentiment Analysis Accuracy with Emoji Classification by Deep Learning and Stacking Models: A Case Study of Hotel Reviews

Leveraging Big Data Analytics and Machine Learning Techniques for Sentiment Analysis of Amazon Product Reviews in Business Insights 

Leveraging Big Data Analytics and Machine Learning Techniques for Sentiment Analysis of Amazon Product Reviews in Business Insights 

Two-way Sentiment Analysis Method of Multimedia Information Based on Deep Learning Algorithm

Two-way Sentiment Analysis Method of Multimedia Information Based on Deep Learning Algorithm

Applying deep learning for sentiment analysis: a case study of prediction 2023 president election

Applying deep learning for sentiment analysis: a case study of prediction 2023 president election

Optimized Feature Selection Approach with Elicit Conditional Generative Adversarial Network Based Class Balancing Approach for Multimodal Sentiment Analysis in Car Reviews

Multimodal Sentiment Analysis (MSA) is a growing area of emotional computing that involves analyzing data from three different modalities. Gathering data from Multimodal Sentiment analysis in Car Reviews (MuSe-CaR) is challenging due to data imbalance across modalities. To address this, an effective data augmentation approach is proposed by combining dynamic synthetic minority oversampling with a multimodal elicitation conditional generative adversarial network for emotion recognition using audio, text, and visual data. The balanced data is then fed into a granular elastic-net regression with a hybrid feature selection method based on dandelion fick’s law optimization to analyze sentiments. The selected features are input into a multilabel wavelet convolutional neural network to classify emotion states accurately. The proposed approach, implemented in python, outperforms existing methods in terms of trustworthiness (0.695), arousal (0.723), and valence (0.6245) on the car review dataset. Additionally, the feature selection method achieves high accuracy (99.65%), recall (99.45%), and precision (99.66%). This demonstrates the effectiveness of the proposed MSA approach, even with three modalities of data.

Fusion of Visual Attention Model and SVM for Sentiment Analysis of Planar Images

Fusion of Visual Attention Model and SVM for Sentiment Analysis of Planar Images