Sentiment Analysis Research Articles

Hierarchical graph contrastive learning framework based on quantum neural networks for sentiment analysis

Existing multi-modal sentiment analysis (MSA) methods typically achieve interaction by connecting different layers or designing special structures, but rarely consider the synergistic effects among data. Moreover, most sentiment analysis research tends to focus solely on single sentiment polarity analysis, without considering the intensity and directional attributes of emotions. Addressing these issues, we propose a framework called Hierarchical Graph Contrastive Learning based on Quantum Neural Network (HGCL-QNN) to remedy these shortcomings. Specifically, a graph structure is established within and between modalities. In the quantum fuzzy neural network module, fuzzy quantum encoding is implemented by using complex-valued, then quantum superposition and entanglement are utilized to consider the intensity and directional attributes of emotions while analyzing emotional polarity. In the quantum multi-modal fusion neural network module, methods such as amplitude encoding and quantum entanglement are employed to further integrate information from different modalities, thereby enhancing the model's power to express emotional information. To enhance the model's understanding of fine-grained and global features, and to better align and integrate features from different modalities, hierarchical graph contrastive learning is employed on different levels. The experimental results demonstrate that HGCL-QNN outperforms the existing baseline methods on MOSI and MOSEI datasets, achieving significant efficacy improvements.

ANALISIS SENTIMEN APLIKASI MICROSOFT TEAMS BERDASARKAN ULASAN GOOGLE PLAY STORE MENGGUNAKAN MODEL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENT ESTIMATION (ADAM)

The Covid-19 virus pandemic with its rapid spread has made the government enact several policies that can reduce community activities that have the potential to cause crowds including social distancing, work from home (WFH) and online and hybrid learning. One of the platforms that was widely used during the WFH and online learning period was Microsoft Teams. Microsoft Teams is an app that lets people and organizations collaborate and interact online. Microsoft Teams can be downloaded through the Google Play Store and users can leave reviews. Sentiment analysis is used to classify user sentiment towards Microsoft Teams into positive and negative sentiments. Review data obtained from January to November 2022. The classification is carried out using the Neural Network which is a computational system inspired by biological neural networks. Weight optimization is done using Adaptive Moment Estimation which is popular in the field of deep learning because it achieves good results quickly. The classification model in this paper was built with a training test ratio of 80:20. The best hyperparameter combination is node hidden layer 100, learning rate 0.0001, and batch size 32. The accuracy of the ANN classification model was obtained by 88.16%.Keywords: Microsoft Teams, Google Play Store, Sentiment Analysis, Neural Network, Adaptive Moment Estimation

A Critical Review on Sentiment Analysis Based on Deep Learning Techniques

Sentiment analysis, a vital task in natural language processing, has evolved significantly with the adoption of deep learning techniques. This review critically examines the current state of sentiment analysis based on deep learning methods, focusing on their performance, scalability, and challenges. We explore key deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), and attention mechanisms. These models have shown remarkable improvements in sentiment prediction accuracy compared to traditional machine learning approaches. However, issues like data scarcity, interpretability, and computational complexity remain challenging. This review provides insights into existing solutions, evaluates emerging trends, and outlines future directions to enhance deep learning applications in sentiment analysis across diverse domains.

Customers' sentiment on food delivery services: An Arabic text mining approach

The Covid-19 pandemic has accelerated the shift in organizations' strategies toward innovative online services. Customer reviews on platforms for online ordering and delivery are a vital source of information about how well a business is performing. Businesses that provide food delivery services (FDS) seek to leverage consumer input to locate areas where customer satisfaction could be raised. Sentiment analysis (SA) has been the subject of an enormous amount of English-language research. Despite Arabic's increasing popularity as a writing language on the Internet, not much study has been conducted on sentiment analysis of Arabic up to this point, with a limited number of publicly available resources for Arabic SA such as datasets and lexicons. The present study collects FDS-related reviews in Arabic to conduct extensive emotion mining, taking advantage of Natural Language Processing, feature selection, and Machine Learning techniques to elicit personal judgments, identify polarity, and recognize customers’ feelings in the FDS domain. To demonstrate that the proposed approach is suitable for analyzing human perceptions of FDS, we designed and carried out excessive experiments that assess the utility of each phase. Our highest categorization accuracy was 90 % using Mutual Information with the SVM classifier. The study's findings provide various managerial insights for improving their plans and service delivery, as well as revealing the main reasons for consumer complaints. It also demonstrates how future academics might harness the power of online business reviews in Arabic using a variety of text-mining approaches.

EASE: An enhanced active learning framework for aspect-based sentiment analysis based on sample diversity and data augmentation

Aspect-Based Sentiment Analysis (ABSA) has received considerable attention in recent studies. Powerful pre-trained models were proposed which can be fine-tuned for many Natural Language Processing (NLP) tasks, including ABSA. However, fine-tuning these models needs a relatively large amount of labeled data. In this research, we propose EASE; an active learning framework to minimize manual labeling effort. We extend the active learning technique by incorporating the concept of sample diversity where similar samples are not selected for labeling. Furthermore, we maximize the utility of these samples by incorporating data augmentation. EASE was evaluated on three benchmark ABSA datasets from three different domains. The results show that the reduction of the number of needed labeled samples ranges from 88% to 94% among the three datasets while maintaining accuracy. Our results show that active learning is an effective approach to reduce manual labeling effort while maintaining comparable performance. Moreover, it is possible to reduce the number of labeled data even further by incorporating sample diversity and data augmentation while maintaining performance.

Applications of BERT in sentimental analysis

This research study emphasizes sentiment analysis and examines Natural Language Processing (NLP) by Bidirectional Encoder Representations from Transformers (BERT). BERT's bidirectional Transformer architecture pre-trained utilizes Next Sentence Prediction (NSP) and Masked Language Modeling (MLM) and has achieved a lot in terms of AI transformation. This paper provides a description of the BERT design, pre-training methods, and fine-tuning for sentiment analysis tasks. The study goes ahead and compares BERT's performance with other deep learning models, machine learning algorithms, and traditional rule-based techniques, highlighting the latter's limited ability to handle linguistic nuances and context. Additionally, studies proving the consistency and accuracy of BERT's sentiment analysis are examined, along with the challenges of handling irony, sarcasm, and domain-specific data. Ethical and privacy concerns that sentiment analysis inherently raises and makes recommendations for further research are also examined in the study, which also shows how integrating sentiment analysis with other domains can lead to multidisciplinary breakthroughs that can offer more comprehensive insights and applications.

Mapping the Mpox discourse: A network and sentiment analysis

Mpox, a zoonotic disease re-emerging from animals to humans, poses a risk of evolving into a global pandemic due to its high infectivity and potential asymptomatic transmission. This study maps the structure and configuration of mpox-related discussions on Twitter/X, identifies key influencers and top hashtags, and analyzes public sentiment. Data were collected using NodeXL Pro from May 7, 2022, to January 15, 2023, with the keyword “Monkeypox” and visualized using Gephi. Social network analysis ranked nodes by betweenness centrality scores to highlight key communicators, and the YifanHu layout algorithm visualized the network. Influential users, source topics, and hashtags were identified, and sentiment analysis was conducted using Azure Machine Learning tools. The analysis identified 11,397 mpox-related tweets. The network structure resembled a community with diverse participants. Influential users included health and science journalists, writers, academics, medical doctors, and public figures. News media and organizational websites were the top information sources, emphasizing the need for accessible scientific information. “Monkeypox” and “Mpox” were the most prevalent hashtags. Negative sentiments dominated the discussion. This analysis provides insights into network structure, key influencers, information sources, and public sentiment, aiding tailored health initiatives to address misinformation and advocate valid health information and emergency responses.

RPEPL: Tibetan Sentiment Analysis Based on Relative Position Encoding and Prompt Learning

Sentiment analysis is a critical task for natural language processing. Much research has been done for high-resource languages such as English and Chinese. However, Tibetan is an extremely low-resource language with less reference information. According to the practical demands, this paper proposes a Tibetan sentiment analysis method based on relative position encoding and prompt learning, the method is abbreviated as RPEPL. First, Word information is introduced to syllable sequences by converting the directed acyclic lattice into a squashed structure. Second, a relative position encoding is used to encode the position information of syllables and words. Then the association relations and semantic information of tokens are identified by leveraging the multi-attention. Finally, the sentiment category of Tibetan sentence is obtained through a prompt learning framework. Experimental results demonstrate that RPEPL significantly outperforms the baseline methods on the TUSA dataset and TNEC dataset. Additionally, Traditional recurrent neural networks cannot perform large-scale parallel computation and convolutional neural networks have difficulty modeling long-distance dependencies in Tibetan text both of which are resolved using RPEPL. Furthermore, the use of a multi-attention not only enriches the association relations between syllables and words, but also enhances the understanding of sentence semantic and syntactic structure information, and improves the performance of Tibetan sentiment analysis.

TCα-PIA: A Personalized Social Network Anonymity Scheme via Tree Clustering and α-Partial Isomorphism

Social networks have become integral to daily life, allowing users to connect and share information. The efficient analysis of social networks benefits fields such as epidemiology, information dissemination, marketing, and sentiment analysis. However, the direct publishing of social networks is vulnerable to privacy attacks such as typical 1-neighborhood attacks. This attack can infer the sensitive information of private users using users’ relationships and identities. To defend against these attacks, the k-anonymity scheme is a widely used method for protecting user privacy by ensuring that each user is indistinguishable from at least k−1 other users. However, this approach requires extensive modifications that compromise the utility of the anonymized graph. In addition, it applies uniform privacy protection, ignoring users’ different privacy preferences. To address the above challenges, this paper proposes an anonymity scheme called TCα-PIA (Tree Clustering and α-Partial Isomorphism Anonymization). Specifically, TCα-PIA first constructs a similarity tree to capture subgraph feature information at different levels using a novel clustering method. Then, it extracts the different privacy requirements of each user based on the node cluster. Using the privacy requirements, it employs an α-partial isomorphism-based graph structure anonymization method to achieve personalized privacy requirements for each user. Extensive experiments on four public datasets show that TCα-PIA outperforms other alternatives in balancing graph privacy and utility.

Impact Of Sarcasm Detection on Sentiment Analysis Using Bi-LSTM and FastText

Sentiment analysis categorizes a collection of texts in a document as either positive or negative. However, sometimes it cannot give accurate results due to sarcastic sentences. Sarcasm involves the use of positive language to convey negative meanings, So sarcasm detection is needed for sentiment classification to provide better results. One method that can be used to perform Sentiment classification is Bidirectional Long Short-Term Memory (Bi-LSTM). However, text data cannot be processed by Bi-LSTM, so it requires word embedding to convert text data into vectors. In this study, the word embedding used is FastText because it can learn the form of words by considering subword information. The results showed that sentiment classification with sarcasm detection could improve evaluation results by 0.08 for precision, 0.07 for recall, 0.07 for F1-score, and 0.07 for accuracy. A paired sample t-test was conducted on precision, recall, F1-score, and accuracy to examine whether there is a difference between sentiment classification with and without sarcasm detection. The obtained p-values are 2.84.10-9, 4.63.10-7, and 2.40.10-8, 6.22.10-8, respectively. This indicates a difference between sentiment classification with and without sarcasm detection. Therefore, with a 95% confidence level, it can be concluded that sarcasm detection impacts sentiment classification.

Exploring Recent NLP Advances for Tamil: Word Vectors and Hybrid Deep Learning Architectures

The advancements of deep learning methods and the availability of large corpora and data sets have led to an exponential increase in the performance of Natural Language Processing (NLP) methods resulting in successful NLP applications for various day-to-day tasks such as Language translation, Voice to text, Grammar checking, Sentiment analysis, etc. These advancements enabled the well-resourced languages to adapt themselves to the digital era while the gap for the low-resource languages widened. This research work explores the suitability of the recent advancements in NLP for Tamil, a low-resource language spoken mainly in South India, Sri Lanka, and Malaysia. From the literature survey, it has been found that there is a lack of comprehensive study on the effect of the recent advancements of NLP for the Tamil text. To fill this gap, this research work analysed the performance of deep learning based text representation and classification approaches namely word embedding, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) for Tamil text classification tasks. Different dimensional pretrained word2Vec and FastText word vectors were built for Tamil and their effectiveness on Text classification was evaluated. The study found that the pre-trained 300- dimensional FastText word vector showed better performance than other pre-trained word vectors for Tamil text classification. Further, in this study, four simple hybrid CNN and Bi-GRU models were proposed for Tamil text classification and their performances were evaluated. The study found that hybrid CNN and Bi-GRU models perform better compared to the classical machine learning models, individual CNN and RNN models, and the Multilingual BERT model. These results confirm that the jointly learned embeddings with different deep learning architectures like CNN and RNN can achieve remarkable results for Tamil text classification, thus ensuring that the deep learning approaches can be successful for NLP on Tamil text.

The Qualitative Election Study of Britain Party Leader Evaluations Database, 2010–2019

Abstract The authors describe the Qualitative Election Study of Britain (qesb) Party Leader Evaluation Database, a database containing 4,119 words and phrases evaluating British political party leaders. The data were collected during pre-election focus groups and interviews with participants from England, Scotland, and Wales during the General Election campaigns of 2010, 2015, 2017, and 2019. A supplementary dataset of leaders’ evaluation data from Dundee residents after the Scottish Independence Referendum in 2014 is also provided. To collect the data, participants viewed headshot pictures of major and minor party leaders (depending on where in Britain they lived) taken from party websites. Participants wrote down words or phrases they associated with each leader and coded their assessment as positive, negative, or neutral. These data are suitable for content, sentiment, and discourse analysis or analytic generalization.

Analisis Sentimen Ulasan Wisatawan Terhadap Alun-Alun Kota Batam: Perbandingan Kinerja Metode Naive Bayes dan Support Vector Machine

Batam City, as a rapidly developing tourism destination in Indonesia, continues to strive to enhance the potential of its tourist attractions to attract more visitors. The assessment of reviews from tourists is crucial in identifying necessary development measures to improve the quality of tourist attractions. This research aims to analyze the sentiment of reviews for the Alun-Alun Kota Batam tourist destination by leveraging data from Google Maps. Two classification methods, Naive Bayes and Support Vector Machine, are employed for sentiment analysis, and their performances are compared. From 1140 collected reviews, the data is categorized into three labels: positive, negative, and neutral. The research results indicate that the Support Vector Machine method achieves higher accuracy (94%) compared to Naive Bayes (83%). This study contributes insights into visitor sentiments towards Alun-Alun Kota Batam, with implications for policy development and more effective actions in enhancing local tourism appeal.

Dual syntax aware graph attention networks with prompt for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a challenging task due to the presence of multiple aspect words with different sentiment polarities in a sentence. Recently, pre-trained language models like BERT have been widely used as context encoders in ABSA. Graph neural networks have also been employed to extract syntactic and semantic information from sentence parsing trees, resulting in superior results. However, dependency trees may establish irrelevant dependencies for sentences with irregular syntax and complex structures. Additionally, previous methods have not fully utilized recent developments in pre-trained language models. Therefore, we propose a Dual Syntax aware Graph attention networks with Prompt (DSGP) model to address these issues. Our model utilizes prompt templates to maximize the potential of pre-trained models and masked vector outputs of templates as supplementary aspect feature representations. We also leverage both dependency trees and constituent trees with graph attention networks to extract different types of syntactic information. The dependency tree captures syntactic correlation between words, while the constituent tree provides a high-level formation of the sentence. Finally, the output from the prompt and parsing trees is fused and fed into a standard classifier. Experimental results on four public datasets demonstrate the competitive performance of our model.

A Machine Learning Approach for Sentiment Analysis Using Social Media Posts

Sentiment analysis on Twitter provides organizations and persons with quick and effective instrument to observe the public's perceptions of them and their competition. A modest number of assessment datasets have been produced in recent years to check the efficiency of sentiment analysis algorithms on Twitter. Researchers offer a review of eight publicly accessible as well as manually annotated assessment datasets for analyzing Twitter sentiment in this research. As a result of this evaluation, we demonstrate that is a widespread weakness of many when using these datasets performing at sentiment analysis the objective (entity) level is indeed the absence of different sentiment classifications across tweets as well as the objects contained in them.[1], As an example all of that "I love my iPhone but I despise my iPad." Could be marked with a made-by-mixing classify however the object iPhone contained within this Twitter post should be annotated with just a label with an optimism. To get around this restriction and enhance existing assessment We have datasets that provide STS-Gold a novel assessment of datasets in which tweets or objects (entities) remain tagged separately hence might show alternative opinion labels. Though research furthermore compares the various datasets on multiple characteristics such as an entire quantity of posts as well as vocabulary size and sparsity.[2] In addition, look at pair by pair relationships between these variables and how they relate to sentiment classifier performance on various data. In this study we used five different classifiers and compared them and, in our experiment, we found that the bagging ensemble classifier performed best among them and have an accuracy level of 94.2% for the GASP dataset and 91.3% for the STS-Gold dataset.

Navigating STEM careers with AI mentors: a new IDP journey.

Mentoring is crucial to the success of STEM higher education. The Individual Development Plan (IDP) is a common career development tool in STEM graduate education that facilitates structured mentor-mentee interactions and goal setting. This study examined the integration of AI mentors into the myIDP framework to provide real-time support and career insights. Using Google Gemini as an AI mentor, this study developed and assessed AI prompts within the myIDP framework. Eighteen STEM graduate students, primarily from underrepresented groups, were trained to engage with the AI mentor. Their interactions, feedback, and comments were analyzed using sentiment and thematic analysis. Participants reported positive experiences with AI mentors, noting benefits, such as immediate responses, up-to-date information, access to multiple AI mentors, enhanced ownership of career development, and time savings. However, concerns about misinformation, bias, privacy, equity, and algorithmic influences have also been raised. The study identified two hybrid human-AI mentoring models-Sequential Integration and Concurrent Collaboration-that combine the unique strengths of human and AI mentors to enhance the mentoring process. This study underscores the potential of AI mentors to enhance IDP practices by providing timely feedback and career information, thereby empowering students in their STEM career development. The proposed human-AI mentoring models show promise in supporting underrepresented minorities, potentially broadening participation in STEM fields.

Correction: What topics and emotions expressed by glaucoma patients? A sentiment analysis perspective

Correction: What topics and emotions expressed by glaucoma patients? A sentiment analysis perspective

Preservation of emotional context in tweet embeddings on social networking sites

In communication, emotional information is crucial, yet its preservation in tweet embeddings remains a challenge. This study aims to address this gap by exploring three distinct methods for generating embedding vectors of tweets: word2vec models, pre-trained BERT models, and fine-tuned BERT models. We conducted an analysis to assess the degree to which emotional information is conserved in the resulting embedding vectors. Our findings indicate that the fine-tuned BERT model exhibits a higher level of preservation of emotional information compared to other methods. These results underscore the importance of utilizing advanced natural language processing techniques for preserving emotional context in text data, with potential implications for enhancing sentiment analysis and understanding human communication in social media contexts.

On the Utilization of Emoji Encoding and Data Preprocessing with a Combined CNN-LSTM Framework for Arabic Sentiment Analysis

Social media users often express their emotions through text in posts and tweets, and these can be used for sentiment analysis, identifying text as positive or negative. Sentiment analysis is critical for different fields such as politics, tourism, e-commerce, education, and health. However, sentiment analysis approaches that perform well on English text encounter challenges with Arabic text due to its morphological complexity. Effective data preprocessing and machine learning techniques are essential to overcome these challenges and provide insightful sentiment predictions for Arabic text. This paper evaluates a combined CNN-LSTM framework with emoji encoding for Arabic Sentiment Analysis, using the Arabic Sentiment Twitter Corpus (ASTC) dataset. Three experiments were conducted with eight-parameter fusion approaches to evaluate the effect of data preprocessing, namely the effect of emoji encoding on their real and emotional meaning. Emoji meanings were collected from four websites specialized in finding the meaning of emojis in social media. Furthermore, the Keras tuner optimized the CNN-LSTM parameters during the 5-fold cross-validation process. The highest accuracy rate (91.85%) was achieved by keeping non-Arabic words and removing punctuation, using the Snowball stemmer after encoding emojis into Arabic text, and applying Keras embedding. This approach is competitive with other state-of-the-art approaches, showing that emoji encoding enriches text by accurately reflecting emotions, and enabling investigation of the effect of data preprocessing, allowing the hybrid model to achieve comparable results to the study using the same ASTC dataset, thereby improving sentiment analysis accuracy.

Public Sentiment Analysis of the Israel-Palestine Conflict on Social Media Using BERT

General background Israel-Palestine conflict has drawn significant global attention, particularly in how it is perceived and discussed on social media platforms. Specific background understanding public sentiment surrounding such geopolitical issues is crucial for media monitoring, diplomatic efforts, and reputation management. Knowledge gap previous sentiment analysis studies often lack the ability to accurately handle multilingual and context-rich datasets, especially in analyzing neutral sentiments, which are commonly overlooked. This study aims to apply the Bidirectional Encoder Representations from Transformers (BERT) model to analyze public sentiment towards the Israel-Palestine conflict on the X platform, focusing on Indonesian users. Results Using BERT, the model achieved 93% accuracy, with a precision of 0.95, recall of 0.93, and F1-score of 0.94. The model performed well in predicting positive and negative sentiments but showed room for improvement in handling neutral sentiment. Novelty this study introduces the implementation of the BERT Transformer model for the multilingual and context-sensitive sentiment analysis of tweets, specifically addressing a high-stakes geopolitical conflict. Implications the findings demonstrate the potential for using advanced natural language processing techniques like BERT for monitoring public opinion, brand management, and detecting societal tensions on social media, offering valuable insights for stakeholders involved in conflict resolution and diplomatic strategies. Highlights: Achieved 93% accuracy in sentiment analysis using BERT on X platform. Identified strengths in predicting positive/negative sentiments, with challenges in neutral sentiment. Demonstrated BERT’s effectiveness in handling complex geopolitical social media data. Keywords: Sentiment Analysis, BERT Transformer, Israel-Palestine Conflict, Social Media, NLP