Sentiment Analysis Research Articles

The role of feed spacers in membrane technology: 45 years of research

Feed spacers are crucial for enhancing the performance and efficiency of membrane-based technologies. Researchers have long focused on optimizing feed spacer designs and materials to improve fluid mixing and mass/heat transfer across membranes. However, these improvements often increase pressure drops, raising energy requirements. Therefore, innovative designs are sought to balance enhanced mass/heat transfer with reduced pressure drops and improved antifouling properties. This study analyzes patterns and trends in the feed spacer field using bibliometric methods, data analysis, and machine learning. The analysis includes 457 articles from the Scopus database, collected on March 5, 2024, covering publications from 1978 to 2023 across 45 journals. The analysis revealed that reverse osmosis technology emerges as the most studied membrane process, with 153 articles. Furthermore, experimental research in this field is preferred over theoretical evaluations, and the impact of feed spacers on mass transfer and pressure drops is the most explored topic, with 197 articles addressing one or more of these phenomena. Additionally, 204 articles focused on feed spacers’ role in fouling, scaling, and biofouling, with commercial feed spacers being the most frequently studied. The Journal of Membrane Science leads in publication volume with 152 articles, and Vrouwenvelder J.S. is the top author in 4 out of 6 metrics. Sentiment analysis of abstracts shows that authors generally express positive sentiments (385 articles) while maintaining an objective (453 articles) and emotion-free (446 articles) writing style.

Implicit expression recognition enhanced table-filling for aspect sentiment triplet extraction

Aspect sentiment triplet extraction (ASTE) is a challenging task in aspect-based sentiment analysis (ABSA), involving the identification of aspect terms, opinion terms, and their corresponding sentiment polarities within comments to form triplets. The emergence of more realistic DMASTE datasets, featuring diverse domains, implicit aspect terms, and longer comments, poses challenges for existing methods. In particular, these methods struggle with recognizing implicit expressions effectively and capturing sufficient information. To overcome these hurdles, we propose an implicit expression recognition enhanced table-filling (IERET) method. This approach integrates modeling of overall implicit expression in sentences and employs a bidirectional information aggregation module to capture word pair information comprehensively. During the decoding process, a table-filling method accurately delineates aspect-opinion pair boundaries. Experimental results across in-domain, single-source cross-domain, and multi-source cross-domain on the DMASTE dataset demonstrate that our proposed IERET method achieves state-of-the-art performance.

A Multimodal Sentiment Analysis Method Based on Fuzzy Attention Fusion

A Multimodal Sentiment Analysis Method Based on Fuzzy Attention Fusion

IDSV-GCN: Integrating Dual Syntactic Views Graph Convolutional Network for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards given aspects in a sentence. Recent research has highlighted that graph neural networks based on syntactic dependency trees effectively convey rich syntactic information and are suitable for ABSA tasks. Most existing work typically adopt either dependency type or minimum distance between nodes in the original syntactic tree to construct different views. However, existing work overlooks the high-dimensional local feature information in dependency relations, resulting in inadequate exploration of this view. Meanwhile, most work ignores that the minimum distance view and the dependency view can be complementary and integrated in syntactic analysis. Unfortunately, directly adding the two views introduces noise issues, leading to unsatisfactory results after their integration. To address these issues, this paper introduces an integrating dual syntactic views graph convolutional network (IDSV-GCN) model that seamlessly integrates dependency type view and minimum distance view, enabling comprehensive utilization of syntactic structure and information. To better address the computation of weights in the dependency type view, we innovatively propose a fusion attention mechanism. This approach effectively captures both global and local feature information. To more effectively integrate the two views and reduce the impact of noise, we employ a syntactic mask layer to filter out noise in the dependency type view and highlighting important dependency feature information. Then, we construct a minimum distance weight matrix based on the matrix obtained from the dependency type view. Finally, we merge the two matrices and feed them into the GCN to facilitate the model’s learning of syntactic information. Experimental results on three widely-used benchmark datasets demonstrate that our proposed IDSV-GCN model outperforms state-of-the-art methods.

Natural language processing in finance: A survey

This survey presents an in-depth review of the transformative role of Natural Language Processing (NLP) in finance, highlighting its impact on ten major financial applications: (1) financial sentiment analysis, (2) financial narrative processing, (3) financial forecasting, (4) portfolio management, (5) question answering, virtual assistant and chatbot, (6) risk management, (7) regulatory compliance monitoring, (8) ESG (Environmental, Social, Governance) and sustainable finance, (9) explainable artificial intelligence (XAI) in finance and (10) NLP for digital assets. With the integration of vast amounts of unstructured financial data and advanced NLP techniques, the study explores how NLP enables data-driven decision-making and innovation in the financial sector, alongside the limitations and challenges. By providing a comprehensive analysis of NLP applications combining both academic and industrial perspectives, this study postulates the future trends and evolution of financial services. It introduces a unique review framework to understand the interaction of financial data and NLP technologies systematically and outlines the key drivers, transformations, and emerging areas in this field. This survey targets researchers, practitioners, and professionals, aiming to close their knowledge gap by highlighting the significance and future direction of NLP in enhancing financial services.

Word- and Sentence-Level Representations for Implicit Aspect Extraction

Word- and Sentence-Level Representations for Implicit Aspect Extraction

An Optimal Multi-Modal Approach for Stock Market Price Forecasting with Fused Sentiment Analysis for Real Time Data

Abstract: This study presents an innovative fusion-based methodology that integrates real-time stock market technical indicators with news sentiment analysis from financial news feeds to enhance stock selection decisions. The proposed framework employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model for forecasting stock prices and a Deep Neural Network (DNN) used in conjunction with transformer-based model for sentiment classification, both optimized through the incorporation of real-time datasets. To further refine feature selection, Artificial Bee Colony (ABC) and Firefly algorithms are utilized, significantly improving the accuracy of price trend predictions and sentiment analysis. Technical indicators from stock market data are processed using the Bi-LSTM model to predict future stock prices. Concurrently, sentiment data from the Economic Times is pre-processed through Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and pre-trained transformer models to extract key sentiment scores. The ABC algorithm enhances textual feature selection, while the Firefly algorithm reduces skewness in the error distribution of technical indicators, thereby improving the alignment between forecasted and actual stock prices. The fusion mechanism integrates predicted price movements with sentiment analysis to provide actionable insights for stock selection. The decision-making framework classifies stocks based on the consistency between price movements and sentiment: stocks predicted to rise with positive sentiment are labelled as "Sure Selection"; rising prices with negative sentiment are categorized as "Dicey Selection"; declining prices with positive sentiment result in a "Risky Selection"; and negative sentiment with falling prices trigger an "Avoid Selection". The proposed approach achieved a prediction accuracy of 67.00% for the Bi-LSTM model, with a precision of 0.714, recall of 0.695, and an F1 score of 0.677, demonstrating the model's robustness. The mean absolute error (MAE) for the Bi-LSTM model was 4.30, indicating strong predictive performance. The combined use of ABC and Firefly algorithms optimizes both technical and sentiment features, significantly contributing to the overall performance of the prediction model. By fusing multiple data sources and employing advanced optimization techniques, this methodology offers a unique and effective solution for stock market prediction and decision-making

Sentiment Analysis on Twitter Social Media Regarding Covid-19 Vaccination with Naive Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT)

Abstract. The Covid-19 vaccine is an important tool to stop the Covid-19 pandemic, however, there are pros and cons from the public regarding this Covid-19 vaccine. Purpose: These responses were conveyed by the public in many ways, one of which is through social media such as Twitter. Responses given by the public regarding the Covid-19 vaccination can be analyzed and categorized into responses with positive, neutral or negative sentiments. Methods: In this study, sentiment analysis was carried out regarding Covid-19 vaccination originating from Twitter using the Naïve Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT) algorithms. The data used in this study is public tweet data regarding the Covid-19 vaccination with a total of 29,447 tweet data in English. Result: Sentiment analysis begins with data preprocessing on the dataset used for data normalization and data cleaning before classification. Then word vectorization was performed with TF-IDF and data classification was performed using the Naïve Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT) algorithms. From the classification results, an accuracy value of 73% was obtained for the Naïve Bayes Classifier (NBC) algorithm and 83% for the Bidirectional Encoder Representations from Transformers (BERT) algorithm. Novelty: A direct comparison between classical models such as NBC and modern deep learning models such as BERT offers new insights into the advantages and disadvantages of both approaches in processing Twitter data. Additionally, this study proposes temporal sentiment analysis, which allows evaluating changes in public sentiment regarding vaccination over time. Another innovation is the implementation of a hybrid approach to data cleansing that combines traditional methods with the natural language processing capabilities of BERT, which more effectively addresses typical Twitter data issues such as slang and spelling errors. Finally, this research also expands sentiment classification to be multi-label, identifying more specific sentiment categories such as trust, fear, or doubt, which provides a deeper understanding of public opinion.

Analisa Sentimen Pada Media Sosial“X” Pencarian Keyword ChatGPT Menggunakan Algoritma K-Nearest Neighbors (KNN)

Sentiment analysis of the use of Artificial Intelligence (AI) is becoming increasingly important in public understanding of today's rapidly evolving technology, as it helps facilitate human activities. One of the key applications is the presence of ChatGPT, an AI capable of interacting with users through user input, such as answering various questions posed. This topic generates a lot of pros and cons, as widely discussed on social media. Research is needed to evaluate how wisely people use this AI. This study proposes an approach using the K-Nearest Neighbors (KNN) algorithm to analyze AI-related sentiment. The KNN algorithm is used to classify sentiment into positive, negative, or neutral, based on the similarity with the closest word in the feature space derived from text data. This method allows for efficient sentiment grouping without the need for complex models. Researchers chose sentiment analysis because it is an appropriate technique for data processing. Of the 1000 reviews collected from social media users on “X,” 853 were positive, and 147 were negative. The data was classified using the KNN algorithm, followed by an accuracy evaluation yielding 84.80%. The results of this sentiment analysis are expected to guide decision-makers in developing and applying AI technology more intelligently, in line with societal needs and expectations.

Comparative analysis of machine learning algorithms for sentiment classification in social media text

In the era of social media, sentiment analysis has become crucial for understanding public opinion. This study presents a comparative analysis of five machine learning algorithms for sentiment classification in social media text: Logistic Regression, Support Vector Machines (SVM), Random Forest, Naive Bayes, and Gradient Boosting. Using a dataset of 100,000 tweets collected over three months, we evaluated these algorithm's performance in classifying sentiments as positive, negative, or neutral. The data underwent extensive preprocessing, including cleaning, normalization, and addressing class imbalance using SMOTE. Our results show that Logistic Regression and SVM achieved the highest overall accuracy at 86.22%, demonstrating balanced performance across all sentiment classes. Random Forest followed closely with 82.59% accuracy, while Naive Bayes and Gradient Boosting showed lower but still noteworthy performance at 70.45% and 69.96% respectively. All models exhibited challenges in classifying negative sentiments, suggesting potential areas for improvement. The study provides insights into each algorithm's strengths and weaknesses, offering guidance for practitioners in selecting appropriate methods for sentiment analysis tasks. Our findings contribute to the ongoing research in applying machine learning to the complex task of sentiment analysis in the rapidly evolving landscape of social media communication.

Comparison of Machine Learning Algorithms in Public Sentiment Analysis of TAPERA Policy

The rapid development of information technology has changed the way people interact and express their opinions on public policies, including the People's Housing Savings (Tapera) policy in Indonesia. People now primarily express their views openly on social media platforms like Twitter, generating a substantial amount of text data for analysis to understand public sentiment. However, the main challenge in this sentiment analysis is determining the most effective machine learning algorithm for classifying public opinion with high accuracy. This study aims to compare the performance of three machine learning algorithms, namely Naïve Bayes, Support Vector Machine, and Random Forest, in analyzing public sentiment towards the Tapera policy. This study analyzes public comment data obtained from Twitter. We measure the accuracy of each algorithm to determine its optimal performance in sentiment classification. The research method consists of several stages, starting with data collection, text preprocessing to clean and prepare data, and then applying the three algorithms to analyze sentiment. The results showed that Naïve Bayes had the highest accuracy of 69.17%, followed by Support Vector Machine with an accuracy of 68.42%, and Random Forest with an accuracy of 66.17%. This shows that Naïve Bayes is the most effective algorithm to use in sentiment analysis of public comments related to the Tapera policy, especially in the context of complex text data from social media. The conclusion of this study is that Naïve Bayes is superior in classifying public sentiment towards the Tapera policy compared to Support Vector Machine and Random Forest. As a result, this study makes a significant contribution to selecting the most appropriate machine learning algorithm for public sentiment analysis towards public policy, which in turn can help the government understand and respond to public perceptions more effectively.

Analisa Sentimen Drama Korea Melalui Media Sosial X dengan Menggunakan Algoritma Naïve Bayes

In this digital era, technological advancements allow easy access to various social media platforms, including Twitter or X, which has over 500 million users. This platform is often used to share opinions through the concept of microblogging. In Indonesia, Korean trends like K-Pop and K-Drama are highly popular, sparking various discussions on social media, especially on X, generating both positive and negative opinions. Sentiment analysis is an effective method for extracting public opinion from large text data, such as those found on X, by classifying comments into positive and negative classes using the Naïve Bayes method. Tweets were collected from X using hashtags related to Korean dramas. The data was then cleaned and processed for sentiment analysis using Naïve Bayes. The Naïve Bayes algorithm is based on Bayes' theorem and is often used in sentiment analysis due to its accuracy and speed in producing results. This study achieved a precision rate of 88.44%. For negative sentiment data, precision was 67.86%, and recall (specificity) for negative sentiment was 59.38%, indicating that the model is quite good at identifying truly negative data. Meanwhile, recall for positive sentiment reached 91.70%. The model's accuracy was 84.33%, showing that the algorithm used can classify sentiment well with the given data. These results can be useful for content creators, marketers, and cultural and social research

A Comparative Analysis of MultinomialNB, SVM, and BERT on Garuda Indonesia Twitter Sentiment

This study investigates customer sentiment towards Garuda Indonesia Airlines (GIA) using sentiment analysis of Twitter data. The research aims to identify prevailing sentiments, uncover common themes in customer feedback, and provide recommendations for improving customer satisfaction and brand loyalty. A dataset of 1,250 tweets from March 2007 to July 2024 was collected and pre-processed, including cleaning, language detection, and tokenization. Sentiment analysis was conducted using three models: MultinomialNB, SVM, and BERT.The results indicate that BERT outperformed both MultinomialNB and SVM in sentiment classification accuracy, achieving 75.6%. This highlights the effectiveness of BERT in capturing contextual meaning within customer reviews. The findings of this research will contribute to a deeper understanding of customer sentiment towards GIA and inform strategies for enhancing customer experience and brand image.

Enhancing Customer Experience through Sentiment Analysis and Natural Language Processing in E-commerce

The research explores the use of sentimental analysis and natural language processing (NLP) in e-commerce applications to enhance customer experience. It identifies a positive correlation between customer reviews and products, overcoming previous limitations in language training and customer loyalty. The study aims to increase e-commerce business sales by 15.4%, resulting in a 15% increase in business profit and a 1% increase in positive recommendations. The study underscores the potential of these technologies in enhancing consumer satisfaction, brand loyalty, and e-commerce performance.

Analysis Of The Use Of Nazief-Adriani Stemming And Porter Stemming In Covid-19 Twitter Sentiment Analysis With Term Frequency-Inverse Document Frequency Weighting Based On K-Nearest Neighbor Algorithm

Abstract. This system was developed to determine the accuracy of sentiment analysis on Twitter regarding the COVID-19 issue using the Nazief-Adriani and Porter stemmers with TF-IDF weighting, along with a classification process using K-Nearest Neighbor (KNN) that resulted in a comparison of 48.24% for Nazief-Adriani and 48.24% for Porter. Purpose: This research aims to determine the accuracy of the Nazief-Adriani and Porter stemmer algorithms in performing text preprocessing using a dataset from Indonesian-language Twitter. This research involves word weighting using TF-IDF and classification using the K-Nearest Neighbor (KNN) algorithm. Methods/Study design/approach: The experimentation was conducted by applying the Nazief-Adriani and Porter stemmer algorithm methods, utilizing data sourced from Twitter related to COVID-19. Subsequently, the data underwent text preprocessing, stemming, TF-IDF weighting, accuracy testing of training and testing data using K-Nearest Neighbor (KNN) algorithm, and the accuracy of both stemmers was calculated employing a confusion matrix table. Result/Findings: This study obtained reasonably accurate results in testing the Nazief-Adriani stemmer with an accuracy of 50.98%, applied to sentiment analysis of COVID-19-related Twitter data using the Indonesian language. As for the accuracy of the Porter stemmer, it achieved an accuracy rate of 48.24%. Novelty/Originality/Value: Feature selection is crucial in stemmer accuracy testing. Therefore, in this study, feature selection is carried out using the Nazief-Adriani and Porter stemmers for testing purposes, and the accuracy data classification is conducted using the K-Nearest Neighbor (KNN) algorithm

Sentiment Analysis and Facial Expression Recognition in Customer Service Interactions

In the evolving landscape of digital customer service, the need for advanced methods to accurately understand and respond to customer emotions has become critical. Traditional systems often rely solely on textual data, missing non-verbal cues that significantly contribute to the customer's emotional state. This study proposes a combined approach integrating Facial Expression Recognition (FER) and Natural Language Processing (NLP) to enhance emotion detection accuracy in customer service interactions. The FER component employs Convolutional Neural Networks (CNNs) to analyze facial expressions, while the NLP component uses Long Short-Term Memory (LSTM) networks to process textual data. This multimodal system aims to provide a comprehensive understanding of customer emotions by capturing both verbal and non-verbal cues. Experiments demonstrate that the integrated FER and NLP model significantly outperforms standalone models, achieving an accuracy of 92.3%, compared to 85.2% for FER-only and 87.4% for NLP-only models. The results highlight the benefits of a multimodal approach, showing substantial improvements in both training and validation performance. This study also compares the proposed model with other state-of-the-art models such as the Deep Learning Assisted Semantic Text Analysis (DLSTA) and Multimodal Emotion Recognition using Deep Belief Networks (DBN). While DLSTA achieves higher accuracy in text-based emotion detection, and DBNs provide robust emotion classification by integrating various modalities, our model effectively balances the strengths of both visual and textual data. The findings suggest that integrating FER and NLP can significantly enhance the quality of customer service by enabling more empathetic and effective interactions. Future work will focus on optimizing computational efficiency, addressing data variability, and ensuring adaptability across diverse customer service scenarios.

Assessing Institutional Performance Using Machine Learning Algorithms

In Middle Eastern nations, social media has grown in importance in influencing political and governmental choices. In Iraq, Facebook is regarded as one of the most widely used social networking sites. The underutilization of this tool for evaluating institutional performance persists. Thus, using sentiment analysis on Facebook, this study suggests a methodology that aids organizations like the Ministry of Justice in Iraq in assessing their own performance. The model makes use of a variety of machine learning methods, including Naive Bayes, Logistic Regression, and Support Vector Machine. TF-IDF (Term Frequency-Inverse Document Frequency) was used to convert the textual data into numerical features, which is essential for effective text analysis. Additionally, features were carefully managed by utilizing both unigram and bigram models. Using datasets from (Facebook pages belonging to the Iraqi Ministry of Justice), a thorough experimental investigation was conducted. The results of our experiments showed that the SVM algorithm produced the best accuracy, at 98.311%. following the suggested model's retention of a few stop words, which was shown to significantly improve the algorithm's performance and guarantee accurate categorization of comments while maintaining the content of the phrase.

Text Analytics on YouTube Comments for Food Products

YouTube is a popular social media platform in the contemporary digital landscape. The primary focus of this study is to explore the underlying sentiment in user comments about food-related videos on YouTube, specifically within two pivotal food categories: plant-based and hedonic product. We labeled comments using sentiment lexicons such as TextBlob, VADER, and Google’s Sentiment Analysis (GSA) engine. Comment sentiment was classified using advanced Machine-Learning (ML) algorithms, namely Support Vector Machines (SVM), Multinomial Naive Bayes, Random Forest, Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average metrics, including accuracy, precision, recall, and F1 score. The results from GSA showed a high accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets, measuring crucial metrics, such as views, likes, comments, and engagement rate. The findings illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but the plant-based dataset maintains a superior overall engagement rate.

Penerapan Support Vector Machine Untuk Analisis Sentimen pada X (Twitter) Mengenai Obat Penyebab Gagal Ginjal Akut pada Anak

In October 2022, there were many cases of children suffering from acute kidney failure due to harmful chemical compounds detected in the history of children's cough medicine use. The statement caused controversy and became a conversation on social media, especially Twitter. Exploring this opinion can lead to a decision that can be applied in machine learning and sentiment analysis, namely support vector machines (SVM). The purpose of this research is to find out the sentiment of the community towards drugs that cause acute kidney failure in children and see the performance of the support vector machine algorithm. The data used was 1128. Based on the results of the study, the community responded negatively to this topic, as evidenced by the fact that the negative sentiment obtained was greater than the positive sentiment, and the support vector machine algorithm with a linear kernel performed very well, as evidenced by the excellent accuracy value of 91%.

Optimisation of Sentiment Analysis for E-Commerce

Sentiment analysis is widely used today to make data-driven decisions in different industries, starting from marketing and including brand management, reputation monitoring, and customer satisfaction analysis. Its growing importance is closely linked with so-called ‘word-of-mouth’ communication, from reading online reviews to writing comments on social networks. Effective separation of sentiments ensures that companies' responses are timely and critical patterns are seen in big data sets. Statistical measures, information gain, correlation-based approaches, etc, have been employed for the feature selection. Still, the problem associated with text data mining is that they don’t convey the text's relative difficulty and additional features. To fill this gap, our research proposes a new feature selection technique through Ant Colony Optimization (ACO) and K Nearest Neighbour (KNN) performed on 28,000 customer reviews in different product categories. The results, therefore, showed an overall accuracy of 80.1%, with the Support Vector Machine (SVM) set at 80.5% on each selected feature, which was slightly higher than the Convolutional Neural Network (CNN), which scored a 78.41% accuracy. SVM remains on the mark of 83%, and for CNN, the rate achieved on the same was 80.8% when both were applied to the entire dataset. These facts rejected the infallibility of the simple and complex algorithms used singly in the sentiment classification, indicating that more sophisticated algorithms like ACO and KNN can provide business solutions to improve their service delivery based on customers’ feedback.