Approach For Sentiment Classification Research Articles

A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis

Social media sentiment classification was an essential consideration in natural language processing (NLP) for evaluating normal people’s perspectives on a given topic. With Twitter’s massive rise in popularity in recent years, the capacity to extract information about public sentiment from tweets became a major focus. This paper not only analyzed public sentiment through data from Twitter but introduced a novel ensemble approach in the methods employed for Twitter sentiment classification. Real-time tweets on various topics, including “covid,” “crime,” “spam,” “flipkart,” “migraine,” and “airlines,” were extracted and thoroughly examined to gain insight into public opinions. Leveraging the Twitter API for real-time tweet extraction, natural language processing techniques were applied to clean the tweet data. Subsequently, we applied several machine learning (ML) algorithms Naïve Bayes, decision tree (DT), random forest (RF), logistic regression (LGR), and deep learning (DL) algorithms recurrent neural network (RNN), LSTM, and GRU individually. Later, we proposed a novel ensemble of ML and DL algorithms for sentiment classification, with a novel emphasis on ensemble techniques and enhanced the accuracy with a significance compared to individual ML or DL model applied. The experimental results demonstrated that our novel ensemble approach achieved high accuracy when compared to existing work.

A novel self-supervised sentiment classification approach using semantic labeling based on contextual embeddings

AbstractSentiment Analysis (SA) is a domain or context-oriented task since the sentiment words convey different sentiments in various domains. As a result, the domain-independent lexicons cannot correctly recognize the sentiment of domain-dependent words. To address this problem, this paper proposes a novel self-supervised SA method based on semantic similarity, contextual embedding, and Deep Learning Techniques. It introduces a new Pseudo-label generator that estimates the pseudo-labels of samples using semantic similarity between the samples and their sentiment words. It proposes two new concepts to calculate semantic similarity: The Soft-Cosine Similarity of a sample with its Positive words (SCSP) and the Soft-Cosine Similarity of a document with its Negative words (SCSN). Then, the Pseudo-label generator uses these concepts and the number of sentiment words to estimate the label of each sample. Later on, a novel method is proposed to find the samples with highly accurate pseudo-labels. Finally, a hybrid classifier, composed of a Convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU), is trained using these highly accurate pseudo-labeled data to predict the label of unseen data. The comparison of the proposed method with the lexicons and other similar existing methods demonstrates that the proposed method outperforms them in terms of accuracy, precision, recall, and F1 score.

Cross-Domain Aspect-Based Sentiment Classification with a Pre-Training and Fine-Tuning Strategy for Low-Resource Domains

Aspect-based sentiment classification (ABSC) is a crucial sub-task of fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspects in a sentence as positive, negative, or neutral. Most existing ABSC methods are based on supervised learning. However, these methods rely heavily on fine-grained labeled training data, which can be scarce in low-resource domains, limiting their effectiveness. To overcome this challenge, we propose a low-resource cross-domain aspect-based sentiment classification (CDABSC) approach based on a pre-training and fine-tuning strategy. This approach applies the pre-training and fine-tuning strategy to an advanced deep learning method designed for ABSC, namely the attention-based encoding graph convolutional network (AEGCN) model. Specifically, a high-resource domain is selected as the source domain, and the AEGCN model is pre-trained using a large amount of fine-grained annotated data from the source domain. The optimal parameters of the model are preserved. Subsequently, a low-resource domain is used as the target domain, and the pre-trained model parameters are used as the initial parameters of the target domain model. The target domain is fine-tuned using a small amount of annotated data to adapt the parameters to the target domain model, improving the accuracy of sentiment classification in the low-resource domain. Finally, experimental validation on two domain benchmark datasets, restaurant and laptop, demonstrates significant outperformance of our approach over the baselines in CDABSC Micro-F1.

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

AbstractThe negative herding effect tends to cause the spread of negative emotions among netizens, resulting in significant public opinion crises. Therefore, linking the negative herding effect and online public opinion, we aim to exploring the connection between netizens' emotions, opinions, and the negative herding effect. To better distinguish the emotions in texts, a new BiGADG (Bidirectional Gated recurrent unit based on Attention with Distance‐based dependency syntax Graph convolutional network) model is proposed in this article. This model includes primarily the proposed dependency syntax based on dependency distance, Bidirectional Gated Recurrent Unit (BiGRU), multi‐head self‐attention and Graph Convolutional Network (GCN). Particularly, the dependency syntax based on dependency distances incorporates word‐to‐word dependency distances into the traditional dependency syntax, which enriches the syntactic structural features of the text. After the experiments, the accuracy of BiGADG model is 86.53%, which is 3.11% higher compared to BERT model. It is evident that our model for sentiment classification has been improved. Based on the results of sentiment classification, we investigate the existence of negative herding behaviour and its underlying motivation in online public opinion. We have found that the negative herding effect, if not effectively managed, may trigger large‐scale and uncontrollable public opinion crises.

Self-Adaptive Representation Learning Model for Multi-Modal Sentiment and Sarcasm Joint Analysis

Sentiment and sarcasm are intimate and complex, as sarcasm often deliberately elicits an emotional response in order to achieve its specific purpose. Current challenges in multi-modal sentiment and sarcasm joint detection mainly include multi-modal representation fusion and the modeling of the intrinsic relationship between sentiment and sarcasm. To address these challenges, we propose a single-input stream self-adaptive representation learning model ( SRLM ) for sentiment and sarcasm joint recognition. Specifically, we divide the image into blocks to learn its serialized features and fuse textual feature as input to the target model. Then, we introduce an adaptive representation learning network using a gated network approach for sarcasm and sentiment classification. In this framework, each task is equipped with its dedicated expert network responsible for learning task-specific information, while the shared expert knowledge is acquired and weighted through the gating network. Finally, comprehensive experiments conducted on two publicly available datasets, namely Memotion and MUStARD, demonstrate the effectiveness of the proposed model when compared to state-of-the-art baselines. The results reveal a notable improvement on the performance of sentiment and sarcasm tasks.

TMBL: Transformer-based multimodal binding learning model for multimodal sentiment analysis

Multimodal emotion analysis is an important endeavor in human–computer interaction research, as it enables the accurate identification of an individual’s emotional state by simultaneously analyzing text, video, and sound features. Although current emotion recognition algorithms have performed well using multimodal fusion strategies, two key challenges remain. The first challenge is the efficient extraction of modality-invariant and modality-specific features prior to fusion, which requires deep feature interactions between the different modalities. The second challenge concerns the ability to distinguish high-level semantic relations between modality features. To address these issues, we propose a new modality-binding learning framework and redesign the internal structure of the transformer model. Our proposed modality binding learning model addresses the first challenge by incorporating bimodal and trimodal binding mechanisms. These mechanisms handle modality-specific and modality-invariant features, respectively, and facilitate cross-modality interactions. Furthermore, we enhance feature interactions by introducing fine-grained convolution modules in the feedforward and attention layers of the transformer structure. To address the second issue, we introduce CLS and PE feature vectors for modality-invariant and modality-specific features, respectively. We use similarity loss and dissimilarity loss to support model convergence. Experiments on the widely used MOSI and MOSEI datasets show that our proposed method outperforms state-of-the-art multimodal sentiment classification approaches, confirming its effectiveness and superiority. The source code can be found at https://github.com/JackAILab/TMBL.

A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis

Sentiment Analysis is a useful tool in several social and business contexts. Aspect Sentiment Classification is a subtask in Sentiment Analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different Deep Learning models have been proposed to solve Aspect Sentiment Classification focus on a specific domain such as restaurant,hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The Continual Learning approach with neural networks has been used to solve aspect classification in multiple domains. However, avoid low aspect classification performance in Continual Learning is challenging. As a consequence, potential neural networkweight shifts in the learning process in different domains or datasets.In this paper, a novel Aspect Sentiment Classification approach is proposed. Our approach combines a Transformer Deep Learning technique with a Continual Learning algorithm in different domains. The input layer used is the pre‐trained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 .

A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis

Sentiment Analysis is a useful tool in several social and business contexts. Aspect Sentiment Classification is a subtask in Sentiment Analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different Deep Learning models have been proposed to solve Aspect Sentiment Classification focus on a specific domain such as restaurant,hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The Continual Learning approach with neural networks has been used to solve aspect classification in multiple domains. However, avoid low aspect classification performance in Continual Learning is challenging. As a consequence, potential neural networkweight shifts in the learning process in different domains or datasets.In this paper, a novel Aspect Sentiment Classification approach is proposed. Our approach combines a Transformer Deep Learning technique with a Continual Learning algorithm in different domains. The input layer used is the pre‐trained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 .

Data preprocessing approach for machine learning-based sentiment classification

Public sentiment regarding a particular issue, product, activity, or organization can be measured and monitored with an application based on artificial intelligence. The data come from comments circulating on social media. However, the rules for writing comments on social media have yet to be standardized, so non-standard words often appear in these comments. Non-standard words affect the determination of sentiment into positive, negative, and neutral categories. Therefore, this study proposes a data preprocessing approach by inserting the Rabin-Karp algorithm to improve non-standard words. This research consists of several stages, namely crawling data, data preprocessing, feature extraction, model development (based on Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) methods), and analysis of the results. The experimental results showed that the proposed approach influences the determination of the sentiment category composition. Then, model testing results showed that all models obtain the highest value in the Positive category for the precision parameter with a value 1. All models in the Neutral category obtain the highest value for the recall parameter, almost reaching 1. All models in the Neutral category achieve the highest value of the f1-score parameter, with an average value of 0.95. In general, the results of the performance analysis of the classification model showed that the NB and SVM-based models have better performance than the DT method.

Evaluating the Effectiveness of Different Machine Learning Approaches for Sentiment Classification

This study presents a comparison of four different machine learning algorithms for sentiment analysis on a small subset of the AR-P (Amazon Reviews - Polarity) dataset. The algorithms evaluated are multilayer perceptron (MLP), Naive Bayes, Decision Tree, and Transformer architectures. The results show that the Transformer-based DistilBERT model performed the best with an accuracy rate of 96.10%, while MLP had a better performance than the other remaining methods. Confusion matrices and ROC curves are provided to illustrate the results, and a comparison with previous studies is presented. The study concludes that the results can serve as a basis for future work, such as using larger datasets or comparing the performance of algorithms on different tasks. Overall, this study provides insights into the use of traditional machine learning and modern deep learning methods for sentiment analysis and their potential applications in real-world scenarios.

Implementation of n-gram Methodology to Analyze Sentiment Reviews for Indonesian Chips Purchases in Shopee E-Marketplace

Chips are a well-known product among Small and Medium Enterprises (SMEs). In order to enhance the quality of chips as an SME product, sentiment analysis is a crucial step. In this research, sentiment analysis of chip purchases on the Shopee E-marketplace was conducted using the Natural Language Processing (NLP) method, utilizing the N-Gram Model and Term Frequent-Inverse Document Frequency (TF-IDF) as feature extraction techniques, and the Support Vector Machine (SVM) algorithm for sentiment classification. The objective of this research is to identify the most suitable feature extraction model and optimal SVM kernel type from the options of Linear, Polynomial degree, Gaussian RBF, and Sigmoid kernels. Results from the experiments indicate that the TF-IDF and unigram feature extraction techniques offer the best performance for SVM classification when utilizing the Linear kernel. By labeling the dataset, it was observed that using a lexicon-based approach for sentiment classification resulted in 84.31% of the total reviews being positive. The words "price", "cheap" and "quality" in unigram have the highest weights above 0.040. In the unigram model, linear kernel accuracy and precision performance values are 88.4% and 87.3%. At the same time, the recall performance values is 88.4%. The results of the F1-Score assessment matrix from Unigram were 86.9%, Bigram was 78.5% and Trigram was 77.4%. Ultimately, the unigram model combined with a linear kernel in the SVM algorithm demonstrates strong potential for application in the development of various systems focused on detecting user reviews in the Indonesian language on the Shopee E-Marketplace.

Cluster-based ensemble learning model for improving sentiment classification of Arabic documents

AbstractThis article reports on designing and implementing a multiclass sentiment classification approach to handle the imbalanced class distribution of Arabic documents. The proposed approach, sentiment classification of Arabic documents (SCArD), combines the advantages of a clustering-based undersampling (CBUS) method and an ensemble learning model to aid machine learning (ML) classifiers in building accurate models against highly imbalanced datasets. The CBUS method applies two standard clustering algorithms:K-means and expectation–maximization, to balance the ratio between the major and the minor classes by decreasing the number of the major class instances and maintaining the number of the minor class instances at the cluster level. The merits of the proposed approach are that it does not remove the majority class instances from the dataset nor injects the dataset with artificial minority class instances. The resulting balanced datasets are used to train two ML classifiers, random forest and updateable Naïve Bayes, to develop prediction data models. The best prediction data models are selected based on F1-score rates. We applied two techniques to test SCArD and generate new predictions from the imbalanced test dataset. The first technique uses the best prediction data models. The second technique uses the majority voting ensemble learning model, which combines the best prediction data models to generate the final predictions. The experimental results showed that SCArD is promising and outperformed the other comparative classification models based on the F1-score rates.

A Deep Learning-Fuzzy based hybrid ensemble approach for Aspect level sentiment classification

A Deep Learning-Fuzzy based hybrid ensemble approach for Aspect level sentiment classification

Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning.

Social media platforms such as Twitter and Facebook have become popular channels for people to record and express their feelings, opinions, and feedback in the last decades. With proper extraction techniques such as sentiment analysis, this information is useful in many aspects, including product marketing, behavior analysis, and pandemic management. Sentiment analysis is a technique to analyze people's thoughts, feelings and emotions, and to categorize them into positive, negative, or neutral. There are many ways for someone to express their feelings and emotions. These sentiments are sometimes accompanied by sarcasm, especially when conveying intense emotion. Sarcasm is defined as a positive sentence with underlying negative intention. Most of the current research work treats them as two distinct tasks. To date, most sentiment and sarcasm classification approaches have been treated primarily and standalone as a text categorization problem. In recent years, research work using deep learning algorithms have significantly improved performance for these standalone classifiers. One of the major issues faced by these approaches is that they could not correctly classify sarcastic sentences as negative. With this in mind, we claim that knowing how to spot sarcasm will help sentiment classification and vice versa. Our work has shown that these two tasks are correlated. This paper proposes a multi-task learning-based framework utilizing a deep neural network to model this correlation to improve sentiment analysis's overall performance. The proposed method outperforms the existing methods by a margin of 3%, with an F1-score of 94%.

A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification

Over the last decade, many methods have been developed to address the domain dependency problem of sentiment classification under domain shift. This problem is exacerbated in Arabic by its feature sparsity induced by morphological complexity and dialect variability. However, only a few studies have proposed sentiment domain adaptation methods for Arabic, with inconsistent comparisons resulting from different datasets and settings, making it difficult to identify the most effective approaches. This is the first comparative study of the most effective domain adaptation methods for Arabic sentiment classification. We replicate the existing methods proposed for Arabic and compare their effectiveness on the standard dataset settings. To further examine the extent to which adaptation performance differs between Modern Standard Arabic (MSA) and Dialectal Arabic (DA), we employ two public multi-domain sentiment datasets. We also test two well-established methods that have been thoroughly utilized in English-related studies and examine if they maintain the same levels of performance when applied to Arabic. Our findings indicate that adaptation performanace on MSA is better than on DA for all traditional approaches. However, implementing adaptation on top of transformer-based language models shows superior performance on DA. Finally, methods that have proven to excel in English suffer from low performance when applied to Arabic and exhibit negative transfer in most cases.

VisdaNet: Visual Distillation and Attention Network for Multimodal Sentiment Classification.

Sentiment classification is a key task in exploring people's opinions; improved sentiment classification can help individuals make better decisions. Social media users are increasingly using both images and text to express their opinions and share their experiences, instead of only using text in conventional social media. As a result, understanding how to fully utilize them is critical in a variety of activities, including sentiment classification. In this work, we provide a fresh multimodal sentiment classification approach: visual distillation and attention network or VisdaNet. First, this method proposes a knowledge augmentation module, which overcomes the lack of information in short text by integrating the information of image captions and short text; secondly, aimed at the information control problem in the multi-modal fusion process in the product review scene, this paper proposes a knowledge distillation based on the CLIP module to reduce the noise information of the original modalities and improve the quality of the original modal information. Finally, regarding the single-text multi-image fusion problem in the product review scene, this paper proposes visual aspect attention based on the CLIP module, which correctly models the text-image interaction relationship in special scenes and realizes feature-level fusion across modalities. The results of the experiment on the Yelp multimodal dataset reveal that our model outperforms the previous SOTA model. Furthermore, the ablation experiment results demonstrate the efficacy of various tactics in the suggested model.

Attention-Based Multi-Channel Gated Recurrent Neural Networks: A Novel Feature-Centric Approach for Aspect-Based Sentiment Classification

Attention-Based Multi-Channel Gated Recurrent Neural Networks: A Novel Feature-Centric Approach for Aspect-Based Sentiment Classification

Cognitive Relationship-Based Approach for Urdu Sarcasm and Sentiment Classification

Cognitive Relationship-Based Approach for Urdu Sarcasm and Sentiment Classification

An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

In today’s digital world, millions of individuals are linked to one another via the Internet and social media. This opens up new avenues for information exchange with others. Sentiment analysis (SA) has gotten a lot of attention during the last decade. We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter. We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative, and neutral polarity. In addition, to show the efficient use of the annotated dataset, an ensemble-based model for sentiment analysis was created. In contrast to others machine learning classifier, we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation (cv), outcomes as 97.77%, f-score is 97.89%.

A Deep Learning Approach for Sentiment Classification of COVID-19 Vaccination Tweets

A Deep Learning Approach for Sentiment Classification of COVID-19 Vaccination Tweets