Performance Of Sentiment Classification Research Articles

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory

Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

리뷰 데이터 감성 분류 성능 향상을 위한 Fine-tuning 방법

Companies in modern society are increasingly recognizing sentiment classification as a crucial task, emphasizing the importance of accurately understanding consumer opinions across various platforms such as social media, product reviews, and customer feedback for competitive success. Extensive research is being conducted on sentiment classification as it helps improve products or services by identifying the diverse opinions and emotions of consumers. In sentiment classification, fine-tuning with large-scale datasets and pre-trained language models is essential for enhancing performance. Recent advancements in artificial intelligence have led to high-performing sentiment classification models, with the ELECTRA model standing out due to its efficient learning methods and minimal computing resource requirements. Therefore, this paper proposes a method to enhance sentiment classification performance through efficient fine-tuning of various datasets using the KoELECTRA model, specifically trained for Korean.

Enhancing image sentiment analysis: A user-centered approach through user emotions and visual features

To bridge the gap between user understanding of emotions and image representation, this paper amalgamated a variety of user features to formulate an image sentiment analysis model. Through controlled user experiments, participants were engaged in discerning sentiment from abstract painting images. User visual features were derived from eye-tracking data collected during the experiment. This paper employed the FFM (Five Factor Model) and POMS (Profile of Mood States) methodologies to gather personality and mood data, which were utilized as user emotional features. Additionally, fundamental components of abstract painting were captured as basic image features. By synergizing features across these three dimensions, the study introduced the "User-Feature-Enhanced-Image-Sentiment-Model" (UFE-ISM) for image sentiment recognition. The research findings underscore the model's superior performance in sentiment classification on abstract painting. Notably, both basic image features and user visual features exhibited significant contributions to the image sentiment classification model.

Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art

The emergence of AI Art has ignited extensive debates on social media platforms. Various online communities have expressed their opinions on different facets of AI Art and participated in discussions with other users, leading to the generation of a substantial volume of data. Analyzing these data can provide useful insights into the public’s opinions on AI Art, enable the investigation of the origins of conflicts in online debates, and contribute to the sustainable development of AI Art. This paper presents a deep learning-driven framework for analyzing the characteristics of public opinion on the Weibo topic of AI Art. To classify the sentiments users expressed in Weibo posts, the linguistic feature-enhanced pre-training model (LERT) was employed to improve text representation via the fusion of syntactic features, followed by a bidirectional Simple Recurrent Unit (SRU) embedded with a soft attention module (BiSRU++) for capturing the long-range dependencies in text features, thus improving the sentiment classification performance. Furthermore, a text clustering analysis was performed across sentiments to capture the nuanced opinions expressed by Weibo users, hence providing useful insights about different online communities. The results indicate that the proposed sentiment analysis model outperforms common baseline models in terms of classification metrics and time efficiency, and the clustering analysis has provided valuable insights for in-depth analyses of AI Art.

EEG emotion recognition based on the TimesNet fusion model

In recent years, emotion recognition based on electroencephalogram (EEG) has become an important research field. This paper proposes an innovative multi-scale emotion recognition method (MS-ERM), which is based on a deep learning model. First, we divide the EEG signal into time windows of 0.5 s in different frequency bands to extract the differential entropy feature and embed the feature into the brain electrode map to express spatial information. Then, the features of each segment are used as input to the new deep learning model (MS-TimesNet). The model combines multi-scale convolution and TimesNet network to effectively extract dynamic time features, cross-channel spatial features, and complex time features in 2D space. Through extensive tests on the DEAP dataset, we prove that this method is superior to existing methods in terms of sentiment classification performance. In the arousal and valence classification, the average classification accuracy of subject-dependent tests reached 91.31% and 90.45%, respectively, while in subject-independent tests, the average classification accuracy was 86.66% and 85.40%, respectively. Code is available at this repository: https://github.com/hyao0827/MS-ERM.

Sentiment analysis of video danmakus based on MIBE-RoBERTa-FF-BiLSTM

Danmakus are user-generated comments that overlay on videos, enabling real-time interactions between viewers and video content. The emotional orientation of danmakus can reflect the attitudes and opinions of viewers on video segments, which can help video platforms optimize video content recommendation and evaluate users’ abnormal emotion levels. Aiming at the problems of low transferability of traditional sentiment analysis methods in the danmaku domain, low accuracy of danmaku text segmentation, poor consistency of sentiment annotation, and insufficient semantic feature extraction, this paper proposes a video danmaku sentiment analysis method based on MIBE-RoBERTa-FF-BiLSTM. This paper constructs a “Bilibili Must-Watch List and Top Video Danmaku Sentiment Dataset” by ourselves, covering 10,000 positive and negative sentiment danmaku texts of 18 themes. A new word recognition algorithm based on mutual information (MI) and branch entropy (BE) is used to discover 2610 irregular network popular new words from trigrams to heptagrams in the dataset, forming a domain lexicon. The Maslow’s hierarchy of needs theory is applied to guide the consistent sentiment annotation. The domain lexicon is integrated into the feature fusion layer of the RoBERTa-FF-BiLSTM model to fully learn the semantic features of word information, character information, and context information of danmaku texts and perform sentiment classification. Comparative experiments on the dataset show that the model proposed in this paper has the best comprehensive performance among the mainstream models for video danmaku text sentiment classification, with an F1 value of 94.06%, and its accuracy and robustness are also better than other models. The limitations of this paper are that the construction of the domain lexicon still requires manual participation and review, the semantic information of danmaku video content and the positive case preference are ignored.

Sentiment analysis and implementation in film evaluation utilizing BERT

This study explores the use of the dmsc_v2 dataset, which is a rich collection of over 2 million ratings and commentary data from over 700,000 users on 28 films, to train the BERT model for sentiment analysis. This expansive dataset, drawn from the popular Chinese movie-rating website, Douban, has been meticulously curated for this research. In the context of the methodology, it is comprehensive and involves multiple stages. Initially, data preprocessing is conducted to refine and format the dataset suitably for model training. Subsequently, the BERT model is trained using the prepared data. Following the training process, the model's performance is critically evaluated to validate its efficacy and accuracy. The resulting model is adept at performing sentiment classification on comments pertaining to films across various social media platforms such as Weibo, Xiaohongshu, and more. This is particularly beneficial as it enables a nuanced analysis of user opinions and trending topics, offering invaluable insights for businesses, movie producers, or marketers. The findings of this study demonstrate that the BERT sentiment analysis model, developed with the dmsc_v2 dataset, exhibits impressive performance and has expansive potential for application within the sphere of social media commentary analysis. The successful development and validation of this model underscore its potential to transform the way sentiment analysis is conducted, especially in the context of entertainment and social media discussions.

Improving sentiment analysis using text network features within different machine learning algorithms

Sentiment analysis poses a significant challenge due to the inherent subjectivity of natural language and the prevalence of unstandardized dialects in social networks. Regrettably, existing literature lacks a dedicated focus on network representation learning for sentiment classification. This paper addresses this gap by investigating ten machine learning algorithms, including support vector machine (SVM), random forest (RF), logistic regression (LR), and Naive Bayes (NB). Our approach integrates text network analysis and sentiment analysis to propose a comprehensive solution. We begin by applying text preprocessing techniques and converting a text corpus into a text network using word co-occurrence. Subsequently, we employ network analysis techniques to extract features based on network topology and node attributes. These network-derived features serve as inputs for sentiment prediction on Yelp reviews. Through the incorporation of diverse text network features and various machine learning algorithms, we achieve significant enhancements in sentiment classification performance. Our evaluation demonstrates an improved area under curve (AUC) of 83% on the Yelp reviews corpus, underscoring the efficacy of integrating network features to enhance sentiment classifiers. This research underscores the critical role of network representation and its potential impact on sentiment analysis, highlighting the prospect of harnessing network features for sentiment classification tasks.

Enhancing Imbalanced Sentiment Analysis: A GPT-3-Based Sentence-by-Sentence Generation Approach

This study addresses the challenge of class imbalance in sentiment analysis by utilizing synthetic data to balance training datasets. We introduce an innovative approach using the GPT-3 model’s sentence-by-sentence generation technique to generate synthetic data, specifically targeting underrepresented negative and neutral sentiments. Our method aims to align these minority classes with the predominantly positive sentiment class in a Coursera course review dataset, with the goal of enhancing the performance of sentiment classification. This research demonstrates that our proposed method successfully enhances sentiment classification performance, as evidenced by improved accuracy and F1-score metrics across five deep-learning models. However, when compared to our previous research utilizing fine-tuning techniques, the current method shows a relative shortfall. The fine-tuning approach yields better results in all models tested, indicating the importance of data novelty and diversity in synthetic data generation. In terms of the deep-learning model used for classification, the notable finding is the significant performance improvement of the Recurrent Neural Network (RNN) model compared to other models like CNN, LSTM, BiLSTM, and GRU, highlighting the impact of the model choice and architecture depth. This study emphasizes the critical role of synthetic data quality and strategic deep-learning model implementation in sentiment analysis. The results suggest that the careful consideration of training data and model attributes is vital for optimal sentiment classification.

Modeling Structured Dependency Tree with Graph Convolutional Networks for Aspect-Level Sentiment Classification.

Aspect-based sentiment analysis is a fine-grained task where the key goal is to predict sentiment polarities of one or more aspects in a given sentence. Currently, graph neural network models built upon dependency trees are widely employed for aspect-based sentiment analysis tasks. However, most existing models still contain a large amount of noisy nodes that cannot precisely capture the contextual relationships between specific aspects. Meanwhile, most studies do not consider the connections between nodes without direct dependency edges but play critical roles in determining the sentiment polarity of an aspect. To address the aforementioned limitations, we propose a Structured Dependency Tree-based Graph Convolutional Network (SDTGCN) model. Specifically, we explore construction of a structured syntactic dependency graph by incorporating positional information, sentiment commonsense knowledge, part-of-speech tags, syntactic dependency distances, etc., to assign arbitrary edge weights between nodes. This enhances the connections between aspect nodes and pivotal words while weakening irrelevant node links, enabling the model to sufficiently express sentiment dependencies between specific aspects and contextual information. We utilize part-of-speech tags and dependency distances to discover relationships between pivotal nodes without direct dependencies. Finally, we aggregate node information by fully considering their importance to obtain precise aspect representations. Experimental results on five publicly available datasets demonstrate the superiority of our proposed model over state-of-the-art approaches; furthermore, the accuracy and F1-score show a significant improvement on the majority of datasets, with increases of 0.74, 0.37, 0.65, and 0.79, 0.75, 1.17, respectively. This series of enhancements highlights the effective progress made by the STDGCN model in enhancing sentiment classification performance.

Sentiment Analysis of Self Driving Car Dataset: A comparative study of Deep Learning approaches

Sentiment Analysis (SA) is a crucial task in understanding public opinions and perceptions towards emerging technologies. In this study, we focus on SA for a self-driving car dataset as it provides valuable insights into public perceptions and opinions towards a transformative technology. The dataset consists of textual reviews associated with sentiment labels, providing insights into how people perceive self-driving car technology. Our objective is to analyze the sentiments expressed in these reviews using Deep Learning (DL) models, namely, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). We compared our results with an existing technique in the field of self-driving car sentiment classification, that implemented various Machine Learning (ML) and DL models, including Support Vector Machines (SVM), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), CNN, and LSTM. In our study, we expanded upon this research by evaluating the performance of ANN, BiLSTM, GRU, and BiGRU. Results reveal that BiLSTM, GRU, and BiGRU exhibit superior performance in sentiment classification within the self-driving car dataset. These findings offer valuable insights into public sentiment towards self-driving cars, contributing significantly to the advancement of SA techniques in the domain of autonomous vehicles. Additionally, the results are statistically tested and are statistically significant.

Hybrid cross-modal interaction learning for multimodal sentiment analysis

Multimodal sentiment analysis (MSA) predicts the sentiment polarity of an unlabeled utterance that carries multiple modalities, such as text, vision and audio, by analyzing labeled utterances. Existing fusion methods mainly focus on establishing the relationship of characteristics among different modalities to enhance their emotion recognition abilities. However, they always ignore the all-round interaction between different modalities, especially the cross-modal interaction, which is critical to the sentiment decision of multimodal data. To address these issues, we propose a novel hybrid cross-modal interaction learning (HCIL) framework for hybrid learning of intra-modal, inter-modal, interactive-modal and cross-modal interactions, with which the model can fully utilize the sentiment information of multimodalities and enhance the sentiment assistance between modalities. Specifically, we propose two core substructures to learn discriminative multimodal features. One is the comparative learning interaction structure that can track the class dynamics in the intra-modal, reduce the modal gap in the inter-modal and establish emotional communication in the interactive-modal; the other is the cross-modal prediction structure, which can build the sentiment relationship between cross-modal pairs, especially exploring the auxiliary sentiment effect of audio on the vision and text. Furthermore, we adopt a hierarchical feature fusion structure to generate the multimodal feature for the final sentiment prediction. Extensive experiments on three benchmark datasets showed that our HCIL approach can obtain significant performance on the MSA task and that the design of a cross-modal interaction structure can directly promote the improvement of sentiment classification performance.

A GCN- and Deep Biaffine Attention-Based Classification Model for Course Review Sentiment

In recent years, the increasing use of online surveys for course evaluation in schools has led to an outpouring of evaluation texts. These texts, with their emotional polarity, can give schools the most direct feedback. Emotional analysis on course evaluation, therefore, has great implications. However, the not-so-rigid text grammar and rich text content pose a challenge for sentiment analysis in Chinese course evaluation. To solve this problem, this paper proposes a sentiment classification model BiLSTM-GCN-Att (BGAN). Here, BiLSTM is used to extract the features of the text and output the hidden state vector. Then, the deep biaffine attention mechanism is used to analyze the dependence of the text and generate a dependency matrix. Next, input the hidden state vector to the GCN. Finally, the softmax function is used as the output layer of the model to perform sentiment classification. The model proves effective and experimental results, showing that the BGAN achieved a maximum improvement of 11.02% and 14.47% in precision and F1-score respectively compared with the classical models.

Analisis Sentimen Ulasan Pada Aplikasi E-Commerce Shopee Dengan Menggunakan Algoritma Naïve Bayes

Every year, the e-commerce industry in Indonesia grows and develops at a rapid pace. In Indonesia, many online marketplaces have sprung up, including Tokopedia, Lazada, and Shopee. People are very interested in all e-commerce companies because they are hassle-free and instant. Among the most famous is Shopee, which offers a wide range of services and also presents a ratings and reviews column. This feature allows users to express their feelings about Shopee. Based on the information gathered from previous user comments, consumers can use these ratings to identify and trust both excellent and negative recommendations of the app they want to use. Sentiment analysis results include both favorable and negative user reviews by scoring, classifying, and filtering viewpoints to help businesses or users. The author uses the naïve bayes algorithm in this research. The Naïve Bayes Classifier approach will be used in this research to perform sentiment classification. The author then uses associations between frequently discussed word terms or themes that are related to each other for the extraction and exploration process, as well as descriptive statistics. Naïve Bayes Classifier is a binary classification technique that applies Bayesian principles with a strong assumption of independence, utilizing simple statistical probabilities.

Sentiment classification on online product reviews using dwarf mongoose optimization with attention based deep learning model

Opinion Mining and Sentiment Analysis acts as a pivotal role in facilitating businesses to actively operate on enhancing the business strategies and accomplish detailed insights of the consumer’s feedback regarding the products. In recent times, deep learning (DL)technique has been used for many sentiment analysis tasks and has attained effective outcomes. Huge quantity of product reviews is being posted by the customer on different e-commerce and social networking platforms which can assist the developers to improve the quality of the products. The study focuses on the design of Sentiment Classification on Online Product Reviews using Dwarf Mongoose Optimization with Attention based Deep Learning (DMO-ABDL) model. The proposed DMO-ABDL technique analyzes the product reviews for the identification of sentiments. To accomplish this, the DMO-ABDL technique performs different stages of preprocessing to transform the actual data into suitable format. Furthermore, the Glove technique is employed for word embedding process. Moreover, attention based long short-term memory (ALSTM) approach was exploited for sentiment classification and its hyperparameters can be optimally chosen by the DMO technique. A comprehensive set of experiments were performed in order to guarantee the enhanced sentiment classification performance of the DMO-ABDL algorithm. A brief comparative study highlighted the supremacy of the DMO-ABDL technique over other existing approaches under different measures.

Transforming sentiment analysis in the financial domain with ChatGPT

Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an addition evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35% enhanced performance in sentiment classification and a 36% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT’s potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.

Analyze IMDb movies by sentiment and topic analysis

Movie is an important cultural form, carrying multiple levels and meanings such as art, entertainment and social value. Movie review and rating data sets are huge, and deep learning and natural language processing methods are widely used today. Advances in big data and deep learning offer unprecedented opportunities to understand moviegoer behavior and preferences while providing a cost-effective way to gain insights relevant to the entertainment industry. This project conducts sentiment analysis, topic modeling, and visual statistical analysis based on the IMDb movie data set to identify key factors and deeper insights that influence successful decision-making in film production. This project first uses the word embedding method to vectorize the movie review text, and then uses Bidirectional Long Short-Term Memory (Bi-LSTM) to perform sentiment classification. In addition, statistical methods such as visualization were used to discover conclusions such as the highest average number of movies released in November, and identify trends, patterns and relationships between the variables of IMDb movies. Finally, the Latent Dirichlet Allocation (LDA) topic modeling model was constructed to find out that the important topic with increased demand is light entertainment movies, highlighting the commercial feasibility of comedy movies as a profitable business model. In summary, this project uses an emotion-topic fusion analysis method based on the Bi-LSTM emotion classification method and the LDA topic modeling method. The results show that the Bi-LSTM model can better identify positive and negative emotions in movie reviews, and the LDA topic model performs well in mining popular topics.

A SENTIMENT ANALYSIS MODEL FOR ELECTROENCEPHALOGRAM SIGNALS OF STUDENTS IN UNIVERSITIES USING A CONVOLUTIONAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE MODELS

Sentiment analysis in teaching evaluation has significant implications. By analyzing students’ sentiments toward instructors, educational institutions can gain valuable insights into teaching effectiveness. These data can guide curriculum development, instructional improvements, and faculty training initiatives. Positive sentiment indicates effective teaching methods, engagement, and student satisfaction; negative sentiment flags areas that need attention. Sentiment analysis can help identify patterns, trends, and outliers, aiding in targeted interventions and personalized support. It also enables comparisons across different courses, instructors, and departments. However, it is crucial to ensure the accuracy and fairness of sentiment analysis algorithms, considering potential biases and the contextual nature of the feedback. This study proposes a sentiment classification model CNN–SVM that combines a convolutional neural network (CNN) and a support vector machine (SVM). Taking students majoring in art in comprehensive colleges and universities as the research object, by collecting the electroencephalogram (EEG) signals of students during teaching evaluation. CNN–SVM is used as the emotional analysis model to obtain the emotional analysis of teaching evaluation results. EEG is a typical physiological signal, and data based on this signal can more truly reflect student emotions. The adaptive CNN feature extraction function and the super generalization classification performance of SVM can reduce the individual differences and data noise between data, thereby improving sentiment classification performance. The experimental results demonstrate that using technology to analyze sentiment can assist educational institutions in more properly comprehending the feedback and opinions of students on instruction. With regard to sentiment analysis, the CNN–SVM method that is derived to produce the fusion algorithm has solid performance.

Sentiment Analysis and Topic Modeling of Kitabisa Applications using Support Vector Machine (SVM) and Smote-Tomek Links Methods

Kitabisa is an Indonesian application that functions to raise funds online. Users can easily support various types of campaigns and donate funds to various social causes through the app. User reviews of the application are very diverse, and it is not sure whether user reviews of the application tend to be positive, neutral, or negative. This research aimed to analyze the sentiment of the Kitabisa application by modeling topics using Latent Dirichlet Allocation (LDA) and classifying user reviews using a Support Vector Machine (SVM). The scrapped dataset showed imbalanced dataset problems, so the SMOTE-Tomek Links oversampling technique was proposed. The results of this study show that using LDA produces five topics often discussed in 750 reviews. Then, the performance of SVM without using SMOTE-Tomek Links was 72% accuracy, 76% precision, 72% recall, and 64% f1 score. Meanwhile, using SMOTE-Tomek Links could significantly improve the performance, namely 98% accuracy, 98% precision, 98% recall, and 98% f1 score. Based on this research, the application of SVM achieved high performance for user sentiment classification, especially when the dataset was in a balanced state. Therefore, the SMOTE-Tomek Links oversampling technique is recommended for dealing with unbalanced sentiment datasets.

Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine

In the current digital era, sentiment analysis has become an effective method for identifying and interpreting public opinions on various topics, including public health issues such as COVID-19 vaccination. Vaccination is a crucial measure in tackling this pandemic, but there are still a number of people who are skeptical and reluctant to receive the COVID-19 vaccine. This public perception is largely influenced by, including information received from social media and online platforms. Therefore, sentiment analysis of the COVID-19 vaccine is one way to understand the public's perception of the COVID-19 vaccine. This research has the purpose to enhance the classification performance in sentiment analysis of COVID-19 vaccines by implementing Information Gain Ratio (IGR) and Particle Swarm Optimization (PSO) on the Support Vector Machine (SVM). With a dataset of 2000 entries consisting of 1000 positive labels and 1000 negative labels, validation was performed through a combination of data splitting with an 80:20 ratio and stratified 10-Fold cross-validation. Applying the basic SVM, an accuracy of 0.794 and an AUC value of 0.890 were obtained. Integration with Information Gain Ratio (IGR) feature selection improved the accuracy to 0.814 and an AUC of 0.907. Furthermore, through the combination of SVM based on PSO and IGR, the accuracy significantly improved to 0.837 with an AUC of 0.913. These results demonstrate that the combination of feature selection techniques and parameter optimization can enhance the performance of sentiment classification towards COVID-19 vaccines. The conclusions drawn from this research indicate that the integration of IGR and PSO positively contributes to the effectiveness and predictive capability of the SVM model in sentiment classification tasks.