Sentiment Classification Model Research Articles

Machine learning of public sentiments towards wind energy in Norway

AbstractAcross Europe, negative public opinion has and may continue to limit the deployment of renewable energy infrastructure required for the transition to net‐zero energy systems. Understanding public sentiment and its spatio‐temporal variations is as such important for decision‐making and for developing socially accepted energy systems. In this study, we apply a sentiment classification model based on a machine learning framework for natural language processing, NorBERT, on data collected from Twitter between 2006 and 2022 to analyse the case of wind power opposition in Norway. From the 68,828 tweets with geospatial information, we show how discussions about wind power intensified in 2018/2019 together with a trend of more negative tweets up until 2020, both on a regional level and for Norway as a whole. Furthermore, we find weak geographical clustering in our data, indicating that discussions are country wide and not dominated by specific regional events or developments. Twitter data allow for detailed insight into the temporal nature of public sentiments and extending this research to additional case studies of technologies, countries and sources of data (e.g. newspapers, other social media) may prove important to complement traditional survey research and the understanding of public sentiment.

Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm

Sentiment analysis plays a pivotal role in comprehending public sentiment, notably within digital communication, where copious amounts of textual data are generated daily. This study delves into the efficacy of sentiment classification models, namely the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), within the imbalanced datasets commonly encountered in sentiment analysis tasks. Employing a comparative analysis methodology, a dataset comprising robot hotel reviews from online platforms is the basis for evaluation. Both NBC and SVM models undergo training and assessment, with and without the Synthetic Minority Over-sampling Technique (SMOTE), to rectify the class imbalance. Performance evaluation relies on critical metrics, including accuracy, recall, precision, f-measure, and Area Under Curve (AUC) to gauge model effectiveness. Findings demonstrate SVM's superiority over NBC in terms of accuracy (SVM: 76.88%, NBC: 67.43%), precision (SVM: 92.03%, NBC: 86.87%), recall (SVM: 58.88%, NBC: 41.00%), f-measure (SVM: 71.78%, NBC: 55.63%), and AUC (SVM: 0.907, NBC: 0.961). Incorporating SMOTE significantly enhances both models' performance, particularly in addressing class imbalance concerns. Although NBC exhibits a more balanced performance across precision and recall metrics, SVM demonstrates heightened accuracy and predictive capability in sentiment classification tasks. These findings underscore the pivotal role of algorithm selection and preprocessing techniques in optimizing sentiment analysis performance, thereby providing invaluable insights for practitioners and researchers alike.

Multi-domain aspect-oriented sentiment analysis for movie recommendations using feature extraction

<span>Sentiment analysis is a well-recognized research field that has acknowledged significant attention in recent years. Researchers have made extensive efforts in employing various methodologies to explore these domains. Sentiment classification plays a fundamental role in natural language processing (NLP). However, studies have shown that sentiment classification models heavily depend on the specific domain. In the context of movie industry, where the demand for reliable movie reviews is high and not all movies are of exceptional quality and worthy of viewers time. Therefore, people depend on movie reviews before watching a movie. This explores the use of data from various domains to improve classification performance within each domain, addressing the difficulty of multi-domain sentiment classification in natural language processing. Therefore, it is crucial to effectively utilize shared sentiment knowledge across different domains for real-world applications. To solve these issues, a multi-domain aspect-oriented sentiment analysis for movie recommendation using feature extraction techniques. The main contribution of this work is to eliminate the time for users to go through a lengthy list of movies to make their decision. The novelty of this work is analysis of different movie genres, TV shows genres with accurate results. The presented approach's performance is validated by evaluating various metrics, including precision, recall, mean square error (MSE) and F1-score.</span>

Multimodal Sentiment Analysis Based on Composite Hierarchical Fusion

Abstract In the field of multimodal sentiment analysis, it is an important research task to fully extract modal features and perform efficient fusion. In response to the problems of insufficient semantic information and poor cross-modal fusion effect of traditional sentiment classification models, this paper proposes a composite hierarchical feature fusion method combined with prior knowledge. Firstly, the ALBERT (A Lite BERT) model and the improved ResNet model are constructed for feature extraction of text and image, respectively, and high-dimensional feature vectors are obtained. Secondly, to solve the problem of insufficient semantic information expression in cross-scene, a prior knowledge enhancement model is proposed to enrich the data characteristics of each modality. Finally, to solve the problem of poor cross-modal fusion effect, a composite hierarchical fusion model is proposed, which combines the temporal convolutional network and the attention mechanism to fuse the sequence features of each modality information and realizes the information interaction between different modalities. Experiments on MVSA-Single and MVSA-Multi datasets show that the proposed model is superior to a series of comparison models and has good adaptability in new scenarios.

Sentence-level sentiment classification based on multi-attention bidirectional gated spiking neural P systems

The GSNP model is a new recurrent-like network inspired by nonlinear spiking mechanisms in nonlinear spiking neural P systems. In this study, a novel sentiment classification model MA-BiGSNP is established by using bidirectional GSNP model combined with multi-attention mechanism. BiGSNP, which is created by two GSNP models with opposite directions, is used to capture semantic correlations between word contexts in a sentence. The multi-attention mechanism simulates the variety of relationships between sentences as well as the significance of words in sentences. To evaluate the effectiveness of the proposed MA-BiGSNP model, we perform comparative experiments and ablation experiments on five real datasets and twelve baseline models. The experimental results show that the proposed MA-BiGSNP model is effective for sentiment classification task.

Design of Intelligent Sentiment Classification Model Based on Deep Neural Network Algorithm in Social Media

Design of Intelligent Sentiment Classification Model Based on Deep Neural Network Algorithm in Social Media

Sentiment classification model for student teaching evaluation based on deep learning technology

Sentiment classification model for student teaching evaluation based on deep learning technology

An Efficient Sentiment Classification Method with the Help of Neighbors and a Hybrid of RNN Models

Some texts that are challenging to recognize on their own may become more understandable in a neighborhood of related texts with similar contexts. Motivated by this intuition, a novel deep text sentiment classification (DTSC) model is proposed to improve the model’s performance by incorporating the neighborhood of related texts. Our framework uses a nonparametric approach to construct neighborhoods of related texts based on Jaccard similarities. Then, a new deep recurrent neural network architecture is proposed, comprising two distinct modules: bidirectional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU). The proposed model aims to effectively capture informative features from the input text and its neighbors. The result of each module is processed through the maximum operation, which selects the most pertinent data. Finally, the extracted features are concatenated and subjected to classification to achieve accurate sentiment prediction. Previous studies have commonly employed a parametric approach to represent textual metadata. However, our approach utilizes a nonparametric approach, enabling our model to perform strongly even when the text vocabulary varies between training and testing. The proposed DTSC model has been evaluated on five real-world sentiment datasets, achieving 99.60% accuracy on the Binary_Getty (BG) dataset, 98.32% accuracy on the Binary_iStock (BIS) dataset, 96.13% accuracy on Twitter, 82.19% accuracy on the multi-view sentiment analysis (MVSA) dataset, and 87.60% accuracy on the IMDB dataset. These findings demonstrate that the proposed model outperforms established baseline techniques in terms of model evaluation criteria for text sentiment classification.

Analyzing online public opinion on Thailand-China high-speed train and Laos-China railway mega-projects using advanced machine learning for sentiment analysis

Sentiment analysis is becoming a very popular research technique. It can effectively identify hidden emotional trends in social networks to understand people’s opinions and feelings. This research therefore focuses on analyzing the sentiments of the public on the social media platform, YouTube, about the Thailand-China high-speed train project and the Laos-China Railway, a mega-project that is important to the country and a huge investment to develop transportation infrastructure. It affects both the economic and social dimensions of Thai people and is also an important route to connect the rail systems of ASEAN countries as part of the Belt and Road Initiative. We gathered public Thai reviews from YouTube using the Data Application Program Interface. This dataset was used to train six sentiment classifiers using machine learning and deep learning algorithms. The performance of all six models by means of precision, recall, F1-score and accuracy are compared to find the most suitable model architecture for sentiment classification. The results show that the transformer model with the WangchanBERTa language model yields best accuracy, 94.57%. We found that the use of a Thai language-specific model that was trained from a large variety of data sources plays a major role in the model performance and significantly increases the accuracy of sentiment prediction. The promising performance of this sentiment classification model also suggests that it can be used as a tool for government agencies to plan, make strategic decisions, and improve communication with the public for better understanding of their projects. Furthermore, the model can be integrated with any online platform to monitor people's sentiments on other public matters. Regular monitoring of public opinions could help the policy makers in designing public policies to address the citizens’ problems and concerns as well as planning development strategies for the country.

An interactive multi-head self-attention capsule network model for aspect sentiment classification

An interactive multi-head self-attention capsule network model for aspect sentiment classification

The Influence Of Applying Stopword Removal And Smote On Indonesian Sentiment Classification

Information, like public opinions or responses, can be obtained through Twitter tweets. These opinions can expressed as a sentiment. Sentiments can be positive, neutral, or negative. Sentiment analysis (opinion mining) on a text can performed through text classification. This research aims to determine the influence of implementing Stopword Removal and SMOTE on the sentiment classification model for Indonesian tweets. The algorithms used in this research are Logistic Regression and Random Forest. Based on the evaluation, the best classification model in this research was achieved by implementing the Random Forest algorithm along with SMOTE, with an f1-score value of 75.03%. Meanwhile, implementing the Random Forest algorithm and Stopword Removal achieved the worst classification model, with an f1-score value of 68.09%. Implementing Stopword Removal in both algorithms has a negative impact in the form of a decrease in the resulting f1-score. Meanwhile, the performance of SMOTE provides a positive impact in the form of an increase in the resulting f1-score. This happened since Stopword Removal could reduce information and alter the meaning of processed tweets, causing the tweet to lose its sentiment.

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.

Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app; there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80.

Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention Interaction

Many existing image and text sentiment analysis methods only consider the interaction between image and text modalities, while ignoring the inconsistency and correlation of image and text data, to address this issue, an image and text aspect level multimodal sentiment analysis model using transformer and multi-layer attention interaction is proposed. Firstly, ResNet50 is used to extract image features, and RoBERTa-BiLSTM is used to extract text and aspect level features. Then, through the aspect direct interaction mechanism and deep attention interaction mechanism, multi-level fusion of aspect information and graphic information is carried out to remove text and images unrelated to the given aspect. The emotional representations of text data, image data, and aspect type sentiments are concatenated, fused, and fully connected. Finally, the designed sentiment classifier is used to achieve sentiment analysis in terms of images and texts. This effectively has improved the performance of sentiment discrimination in terms of graphics and text.

CoBiAt: A Sentiment ClassificatiCobiat: A Sentiment Classification Model Using Hybrid Convnet- Dual-lstm with Attention Mechanismon Model using Hybrid ConvNet- Dual-LSTM with Attention Mechanism

CoBiAt: A Sentiment ClassificatiCobiat: A Sentiment Classification Model Using Hybrid Convnet- Dual-lstm with Attention Mechanismon Model using Hybrid ConvNet- Dual-LSTM with Attention Mechanism

Advancement in Bangla Sentiment Analysis: A Comparative Study of Transformer-Based and Transfer Learning Models for E-commerce Sentiment Classification

Background: As a direct result of the Internet's expansion, the quantity of information shared by Internet users across its numerous platforms has increased. Sentiment analysis functions at a higher level when there are more available perspectives and opinions. However, the lack of labeled data significantly complicates sentiment analysis utilizing Bangla natural language processing (NLP). In recent years, nevertheless, due to the development of more effective deep learning models, Bangla sentiment analysis has improved significantly. Objective: This article presents a curated dataset for Bangla e-commerce sentiment analysis obtained solely from the "Daraz" platform. We aim to conduct sentiment analysis in Bangla for binary and understudied multiclass classification tasks. Methods: Transfer learning (LSTM, GRU) and Transformers (Bangla-BERT) approaches are compared for their effectiveness on our dataset. To enhance the overall performance of the models, we fine-tuned them. Results: The accuracy of Bangla-BERT was highest for both binary and multiclass sentiment classification tasks, with 94.5% accuracy for binary classification and 88.78% accuracy for multiclass sentiment classification. Conclusion: Our proposed method performs noticeably better classifying multiclass sentiments in Bangla than previous deep learning techniques. Keywords: Bangla-BERT, Deep Learning, E-commerce, NLP, Sentiment Analysis

Employing synthetic data for addressing the class imbalance in aspect-based sentiment classification

ABSTRACT The class imbalance problem, in which the distribution of different classes in training data is unequal or skewed, is a prevailing problem. This can lead to classifier algorithms being biased, negatively impacting the performance of the minority class. In this paper, we addressed the class imbalance problem in datasets for aspect-based sentiment classification. Aspect-based Sentiment Classification (AbSC) is a type of fine-grained sentiment analysis in which sentiments about particular aspects of an entity are extracted. In this work, we addressed the issue of class imbalance by creating synthetic data. For synthetic data generation, two techniques have been proposed: paraphrasing using the PEGASUS fine-tuned model and backtranslation using the M2M100 neural machine translation model. We compared these techniques with two other class balancing techniques, such as weighted oversampling and cross-entropy loss with class weight. An extensive experimental study has been conducted on three benchmark datasets for restaurant reviews: SemEval-2014, SemEval-2015, and SemEval-2016. We applied these methods to the BERT-based deep learning model for aspect-based sentiment classification and studied the effect of balancing the data on the performance of these models. Our proposed balancing technique, using synthetic data, yielded better results than the other two existing methods for dealing with multi-class imbalance.

EERCA-ViT: Enhanced Effective Region and Context-Aware Vision Transformers for image sentiment analysis

Different parts of an image have a strong or weak guiding effect on emotions. The key to emotion recognition of images is to fully exploit the regions associated with emotions. Therefore, this paper proposes a visual sentiment classification model with two branches based on visual transformer, termed as Enhanced Effective Region and Context-Aware Vision Transformers (EERCA-ViT). This model includes a primary branch and an auxiliary branch. The primary branch simulates interdependencies between patches by squeezing and stimulating patches (P-SE), thereby highlighting effective region features in the global feature. The auxiliary branch removes feature patches that have been tagged by the primary branch through the context-aware module (CAM), forcing the network to discover different sentiment discriminative regions. At the same time, a two-part loss function is constructed to improve the robustness of the model. Finally, extensive experiments on six benchmark datasets show that the proposed method outperforms the state-of-the-art image sentiment analysis methods. Furthermore, the effectiveness of the different modules of the framework (P-SE and CAM) has been well demonstrated through extensive comparative experiments.

Youtube Comment Feature Selection And Classification Using Fused Machine Learning

The exponential rise of internet platforms, notably YouTube, has resulted in a massive volume of user-generated material, including video comments. Understanding audience input and enhancing user experience require analyzing and forecasting the mood of YouTube comments. This work provides a comprehensive method for YouTube comment prediction and sentiment classification that combines feature selection using Recursive Feature Elimination (RFE), Elastic Net Random Forest with Logistic Regression (RF with LR), and Principal Component Analysis (PCA). The first stage is to choose the most informative features from a given dataset using RFE, a common approach for doing so. RFE aids in the elimination of unnecessary or redundant features, resulting in enhanced model performance and decreased computing complexity. The Elastic Net Random Forest with Logistic Regression (RF with LR) technique is then used to construct a robust sentiment classification model. Elastic Net regularization combines the advantages of both L1 (Lasso) and L2 (Ridge) regularization, allowing for improved feature selection and management of multicollinearity concerns. By integrating many decision trees, the Random Forest ensemble approach improves the model's predictive potential even more. We employ Principal Component Analysis (PCA) to increase the classification model's effectiveness and solve possible difficulties created by high-dimensional data. PCA decreases the dataset's complexity while retaining its fundamental qualities, resulting in a more manageable and efficient feature space for classification. Finally, we compare the performance of three prominent classifiers on the preprocessed dataset: Linear Support Vector Machine (LSVM), Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Decision Tree (DT). We can select the best-performing model for YouTube comment categorization by comparing these classifiers.

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.