Perform Sentiment Analysis Research Articles

Hybrid Deep Learning Model to Predict Students’ Sentiments in Higher Educational Institutions

Sentiment analysis has been widely used in various fields of social media, education, and business. Specifically, in the education domain, the usage of sentiment analysis is difficult due to the huge amount of information, the nature of language, and processing the diverse perceptions of students. Deep learning emerges as an advanced concept in the realm of machine learning that learns features automatically from raw text data, making them well-suited for sentiment analysis tasks. In recent years, deep learning has been used in analyzing the sentiments. Deep learning architectures have surpassed other machine learning paradigms for performing sentiment analysis. The ability to analyze automatically the students’ sentiments enables HEI to process huge amounts of unstructured data quickly, efficiently, and cost-effectively. The paper aims to predict the sentiments of students’ reviews posted in VLE regarding online learning that enables the educators to optimize their teaching methods for the best results. This study paper explores the usage of CNN, LSTM, and hybrid CNN-LSTM for the prediction of sentiments. The proposed hybrid CNN-LSTM architecture achieves superior performance compared to other baseline algorithms with respect to accuracy, precision, recall, and F1 score. According to outcomes, the recommended technique achieves remarkable accuracy of 97%. The findings facilitate the progress of a more efficient deep learning sentiment prediction system that gives valuable insights from a huge volume of students’ textual data.

Improving Sentiment Analysis Performance on Imbalanced Moroccan Dialect Datasets Using Resample and Feature Extraction Techniques

Improving Sentiment Analysis Performance on Imbalanced Moroccan Dialect Datasets Using Resample and Feature Extraction Techniques

Aspect-based Sentiment Analysis with an Ensemble Learning Framework for Requirements Elicitation from App Reviews

In the past three years, numerous studies have demonstrated the effective performance of aspect-based sentiment analysis (ABSA) in eliciting requirements from APP reviews. However, in aspect category detection (ACD) and aspect category polarity (ACP), traditional supervised machine learning techniques still dominate the latest research. Inherent limitations, such as poor generalization ability, low robustness, and high dependence on feature engineering, often constrain the methods. Additionally, while most research continues to center on the efficacy of singular models or strategies, the effective amalgamation of traditional and contemporary techniques has yet to be adequately explored. Given these challenges, this study proposes an ensemble learning framework based on XGBoost and Stacking. Compared to baseline, the framework achieves a performance improvement of 22.9%-28.4% in ACD and 9.3%-13.2% in ACP tasks based on different feature engineering. Overall, the preliminary attempt in this study to apply ensemble learning in ABSA for requirements elicitation from APP reviews, providing a feasible new direction for overcoming the inherent limitations of traditional techniques in fine-grained sentiment modeling in this field.

Sentiment Analysis of Online Reviews: A Machine Learning Based Approach with TF-IDF Vectorization

Nowadays, online reviews wield considerable influence over consumer decision-making processes. Surveys show 84% of people compare their trustworthiness to recommendations from personal connections in these online reviews. Online reviews of services or destinations can significantly benefit the tourism industry. Therefore, our primary intent of this study is to leverage Machine Learning (ML) and Natural Language Processing (NLP) for sentiment analysis of hotel reviews in Jordan in order to assist both hotel owners and tourists. In this study, we proposed a ML-based approach using Support Vector Machine (SVM) and TF-IDF to perform sentiment analysis of hotel reviews into positive or negative. In addition, our experiments were performed using our real dataset, “JOHotelRating”, which was gathered in the Jordanian context. In the feature extraction stage, we utilized the Term Frequency-Inverse Document Frequency (TF-IDF) method. In the machine learning (ML) classification phase, we utilized various algorithms such as Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), Bernoulli’s Naïve Bayes (BNB), Decision Tree (DT), and Random Forest (RF). SVM with TF-IDF for feature extraction, emerged as the standout performer, achieving an impressive 97% accuracy in sentiment classification. Our proposed approach offers the hotel owners a time-saving method to identify positive and negative reviews, allow them to understand trends, and enhance the overall customer experience. On the tourist side, the study attempts to tackle the challenge of comprehending numerous reviews by providing sentiment analysis, ultimately aiding them in making better-informed decisions when selecting a hotel in Jordan.

Sentiment Analysis in Green Finance with LLMs

Green finance has gained global significance as governments and financial institutions emphasize sustainable investment. Understanding the sentiment of green finance reports can provide valuable insights into public perception, investor sentiment, and policy reception. This study uses three different models FinBERT, GPT-3.5 Turbo, and GPT-4o -- to perform sentiment analysis on over 1000 reports obtained from the International Finance Corporation (IFC) website. To assess the accuracy of the models, this paper manually labeled the sentiment of the reports into three categories: Positive, Negative, and Neutral. We compared the models outputs using standard metrics such as F1-score, Accuracy, Precision, and Recall. The findings indicate that GPT-3.5 Turbo outperforms the other models in terms of accuracy. GPT-4o shows superior performance compared to Finbert which trained on financial texts in extracting sentiment from general text. Even though FinBERT and GPT-4 have stronger financial text processing capabilities, GPT-3.5 Turbo can often capture the true intent and sentiment of the text more quickly and clearly, especially when trained on a relatively small text corpus. Its generalization and speed make it efficient for less complex financial tasks.

Enhancing Stock Market Prediction with Sentiment Analysis Using a BERT-based Model

This study explores the prediction of stock price trends in the financial market, emphasizing the impact of investor sentiment and macroeconomic policies. Traditional research often uses mathematical, statistical, or deep learning methods to predict stock prices but overlooks the emotional factors in vast unstructured text data, such as financial news. This paper proposes a Bidirectional Encoder Representations from Transformers (BERT)-Transformer model that integrates sentiment analysis to enhance stock market prediction. Using financial news text data from the Oriental Fortune Network, the BERT model performs sentiment analysis to extract emotional polarity features. These features, combined with stock transaction data, are then input into a Transformer model to predict the index trends. The experimental results demonstrate a 60% accuracy in predicting stock indices' rise and fall, indicating the model's effectiveness while highlighting areas for improvement. The methodology details dataset preprocessing, the BERT- Back Propagation (BP) model structure, and how sentiment classification is combined with stock trading data for predictions. Performance comparisons with other prediction models confirm the benefits of incorporating sentiment analysis. The BERT-Transformer model’s long-term memory capabilities make it a promising tool for financial time series predictions, offering new research ideas and methods for the financial field. Future research will aim to automate the sentiment feature labeling process to save time and improve efficiency.

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 article proposes RPEPL, a Tibetan sentiment analysis method based on relative position encoding and prompt learning. 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. Third, the association relations and semantic information of tokens are identified by leveraging the multi-attention. Finally, the sentiment category of the 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 (Tibetan News Event Comments) 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 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.

Modeling of Naïve Bayes and Decision Tree Algorithms to Analyze Sentiment Related to Jaklingko Public Transportation on Social Media X (Twitter)

Technological developments have changed the way people access information and share opinions through the internet and social media such as X (Twitter). Public sentiment is now crucial in evaluating public services, especially in the era of advanced information technology. As a metropolitan city, public transportation in DKI Jakarta plays an important role in economic, business and government activities. The Jaklingko initiative launched by the DKI Jakarta Provincial Government aims to provide efficient and affordable public transportation. This research implements and compares Naive Bayes and Decision Tree classification methods to perform sentiment analysis of twitter users opinions regarding Jaklingko into positive and negative categories. Data was collected by crawling using tweet_harvest which obtained 6001 tweets about Jaklingko, then text preprocessing was carried out. Word weighting is done using the TF-IDF method to give value to each term in the document, and sentiment labeling is done using the Vader Lexicon library. The data is divided into training and testing data with a ratio of 80%:20% for the classification process. Evaluation of the method is done using confusion matrix. The results showed that the accuracy of Naive Bayes reached 84.9% and Decision Tree reached 84.2%. The wordcloud visualization depicts negative words including vehicle stoppage, bad driver attitude, and envy from people in other cities. Meanwhile, positive words included free system, useful programs, and user convenience. This research provides an in-depth understanding of public opinion towards Jaklingko, with potential implications for improving public transportation services in Jakarta.

The construction of a Chinese fine-grained sentiment dictionary for Chinese domestic investors (CN-FSD) and its application

Stock bar comment texts are used to forecast stock market trends because they contain sentiment data provided by investors. However, previous research has relied on generic or polar sentiment dictionaries to construct sentiment indicators, which has limited progress in this field. This paper crawls nearly 1.8 million stock bar comment texts from the Eastmoney (www.eastmoney.com) Stock Bar Forum to perform sentiment analysis in this specialized area of the financial market. It reclassifies existing Chinese sentiment dictionaries, and utilizes Word2Vec and CNN models and word frequency statistics with a penalty mechanism to extract polar sentiment words. Additionally, the improved PMI (Pointwise Mutual Information) algorithm was utilized for fine-grained segmentation of negative emotion words. Finally, a fine-grained sentiment dictionary (four categories: happy, anger, sad and fear; with a total of 6155 words) was established for Chinese domestic investors (denoted by CN-FSD). We found that the improved PMI algorithm increased accuracy by 14%. In addition, we introduce the investor sentiment quantification indicators from two aspects, i.e., fine-grained sentiments and on the emotion theory dimension of psychology from the valence and arousal dimensions. This is done to verify the validity and robustness of the constructed dictionary. The case study from 2019 to 2022 discovered that under the influence of extreme occurrences like COVID-19, the fine-grained sentiment indicators constructed in this study are found to be significantly more strongly correlated with market trend indicators like index return and trading volume than the existing polar or fine-grained sentiment dictionaries. ‘fear’ emotions in fine-grained emotions dominate the stock market. Just as this case study emphasizes the importance of ‘fear’ sentiment, studying the indicators of high arousal sentiment is also important. This case study further validates the efficacy of CN-FSD.

Consumer Feedback Sentiment Classification Improved Via Modified Metaheuristic Optimization Natural Language Processing

This study investigates the synergy between the virtual and real-world economies through e-commerce, where seller reputation is critical in guiding consumer decisions. As traditional businesses shift towards online retail, user reviews become essential, offering feedback to both sellers and potential buyers. Sentiment analysis through machine learning (ML) techniques presents significant advantages for consumers and retailers alike. This research proposes a novel approach combining bidirectional encoder representations from transformers (BERT) embeddings with an optimized XGBoost classification model to enhance sentiment analysis performance. A modified metaheuristic algorithm, derived from the firefly algorithm (FA), is introduced to optimize the model. Testing on publicly available datasets demonstrates that models optimized by this algorithm achieved a peak accuracy of .881336. Further statistical analyses substantiate these improvements, and SHAP interpretation on the best-performing model identifies key features impacting model predictions, shedding light on factors driving customer sentiment insights.

Reinforcement learning in sentiment analysis: a review and future directions

Sentiment analysis in natural language processing (NLP) is used to understand the polarity of human emotions (e.g., positive and negative) and preferences (e.g., price and quality). Reinforcement learning (RL) enables a decision maker (or agent) to observe the operating environment (or the current state) and select the optimal action to receive feedback signals (or reward) from the operating environment. Deep reinforcement learning (DRL) extends RL with deep neural networks (i.e., main and target networks) to capture the state information of inputs and address the curse of dimensionality issue of RL. In sentiment analysis, RL and DRL reduce the need for a large labeled dataset and linguistic resources, increasing scalability and preserving the context and order of logical partitions. Through enhancement, the RL and DRL algorithms identify negations, enhance the quality of the generated responses, predict the logical partitions, remove the irrelevant aspects, and ultimately capture the correct sentiment polarity. This paper presents a review of RL and DRL models and algorithms with their objectives, applications, datasets, performance, and open issues in sentiment analysis.

The Role of Sentiment Analysis in Machine Translation- A Review on the Example of ChatGPT

In today’s globalization, machine translation technology has become an indispensable bridge for cross-lingual communication. The integration of sentiment analysis makes machine translation not only able to convey information, but also able to accurately express and understand the emotional color of the original text, which is of great significance in the fields of cross-cultural communication, customer service, and social media interaction. This study overviews the development of machine translation technology, from early rule-based models to modern statistical models to Transformer-based neural network models, with a special focus on the integrated application of ChatGPT in sentiment analysis and machine translation. First, this study introduces the background and research significance of machine translation, and discusses the role of sentiment analysis in improving translation quality and user satisfaction. Subsequently, the historical development of machine translation technology is reviewed in detail, and the basic concepts and methodologies of sentiment analysis and its applications in different fields are elaborated. On this basis, this study analyzes in depth the technical architecture, language model and interaction capability of ChatGPT, as well as its specific application and performance in sentiment analysis. Further, this study explores the importance of sentiment analysis in machine translation, including sentiment recognition, translation strategies for sentiment expression, and the impact of sentiment analysis on improving translation quality and cultural adaptability. Meanwhile, the advantages and limitations of ChatGPT in sentiment analysis are assessed, and future research directions are proposed, including technology convergence, construction of datasets and evaluation criteria, and ethical and bias issues. Finally, this study summarizes the practical application value of sentiment analysis in machine translation and looks forward to the potential areas of future research, aiming to provide reference and inspiration for the further development of machine translation and sentiment analysis.

Laozi’s Influence on Contemporary Rap Music: A Study of Cultural Protection and Inheritance

This study investigates the influence of Laozi’s teachings on contemporary rap music, focusing on themes of cultural protection and inheritance. Through a mixed-methods approach, qualitative and quantitative analyses were conducted to explore the extent of Laozi's influence in rap lyrics. Qualitative analysis involved in-depth examination of lyrical content to identify thematic parallels with Laozi's philosophy, while quantitative analysis quantified instances of Laozi references in selected rap songs. The results reveal a steady increase in Laozi references in rap music over time, with prevalent themes including harmony, self-awareness, and balance. Geographic distribution highlights a global reach of Laozi-inspired rap, transcending cultural boundaries. Collaboration patterns, sentiment analysis, and commercial performance further elucidate the multifaceted relationship between Laozi's teachings and contemporary rap culture. This study contributes to understanding the dynamics of cultural transmission and adaptation in the modern music industry, showcasing the enduring relevance of ancient wisdom in shaping artistic expression.

Assessing the Effectiveness of MoSCoW Prioritization in Software Development: A Holistic Analysis across Methodologies

Effective software Requirement Prioritization plays a pivotal role in the success of the Software Development process, ultimately contributing to the successful delivery of high-quality products. Among the various methods for Requirement Prioritization, the MoSCoW method has gained widespread adoption due to its ease of use. However, its overall effectiveness remains a subject of inquiry. This paper presents a rigorous assessment of the MoSCoW Requirement Prioritization technique, drawing insights from software developers who engage in the Prioritization process. Our evaluation encompasses a distinct perspective: that of the developers tasked with Prioritization. The feedback solicited from developers encapsulates a diverse set of criteria, shedding light on the method's efficacy. Additionally, we perform sentiment analysis on the user experience of the Prioritization task to corroborate the method's accuracy and efficiency. Our study unfolds through a practical exercise involving the Prioritization of a predefined set of requirements using MoSCoW principles. A mixed method approach is employed for the purpose of assessing the effectiveness of MoSCoW. The findings of our quantitative research underscore the method's limitations, indicating that it may not be as effective and precise as previously believed. Furthermore, through qualitative analysis, we are able to highlight the complexities and challenges associated with MoSCoW-based Prioritization. The insights gained from this analysis prompt contemplation regarding the potential introduction of an evolved Requirement Prioritization method, while leveraging MoSCoW as a foundational framework. This research aims to inform the ongoing evolution of Requirement Prioritization methodologies, ultimately enhancing the efficiency and accuracy of Software Development processes.

Automatic Negation Detection for Semantic Analysis in Arabic Hotel Reviews Through Lexical and Structural Features: A Supervised Classification

One significant challenge in sentiment analysis is the presence of negation, which reverses the meanings of sentences, transformingpositive statements into negative ones and impacting the sentiment conveyed in the text. This issue is particularly pronounced in Arabic, a language known for its complex morphology. Detecting negation is crucial for enhancing sentiment analysis performance and various natural language processing applications. This paper presents an approach for automatically detecting negation in user-generated Arabic hotel reviews through lexical and structural features. It comprises several stages: data collection, text pre-processing, feature extraction, supervised learning classification, and evaluation. The study employed multiple supervised classification techniques, including naïve Bayes, random forest, logistic regression, support vector machines, and deep learning, to analyse lexical and structural features extracted from the dataset. The results of the experiments yielded promising outcomes, demonstrating the feasibility of the approach for practical applications. The classifiers exhibited highly comparable performance in identifying negation, with only marginal deviations in their performance metrics. Notably, the deep learning classifier consistently emerged as the top performer, achieving an exceptionally high overall accuracy rate of 99.24 percent, surpassing established benchmarks in Arabic text processing and underscoring its potential for practical applications. These findings hold significant implications for advancing Arabic text processing, particularly in sentiment analysis and related NLP tasks. The high accuracy of 99.24 percent achieved by the deep learning classifier highlights its robustness in accurately detecting negation, a critical challenge in sentiment analysis. This classifier performance demonstrates the potential to be integrated into real-world applications, such as automated review systems and opinion mining tools, where accurate sentiment interpretation is essential.

A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets

Sentiment analysis is a pivotal tool in understanding public opinion, consumer behavior, and social trends, underpinning applications ranging from market research to political analysis. However, existing sentiment analysis models frequently encounter challenges related to linguistic diversity, model generalizability, explainability, and limited availability of labeled datasets. To address these shortcomings, we propose the Transformer and Attention-based Bidirectional LSTM for Sentiment Analysis (TRABSA) model, a novel hybrid sentiment analysis framework that integrates transformer-based architecture, attention mechanism, and recurrent neural networks like BiLSTM. The TRABSA model leverages the powerful RoBERTa-based transformer model for initial feature extraction, capturing complex linguistic nuances from a vast corpus of tweets. This is followed by an attention mechanism that highlights the most informative parts of the text, enhancing the model’s focus on critical sentiment-bearing elements. Finally, the BiLSTM networks process these refined features, capturing temporal dependencies and improving the overall sentiment classification into positive, neutral, and negative classes. Leveraging the latest RoBERTa-based transformer model trained on a vast corpus of 124M tweets, our research bridges existing gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy and relevance. Furthermore, we contribute to data diversity by augmenting existing datasets with 411,885 tweets from 32 English-speaking countries and 7,500 tweets from various US states. This study also compares six word-embedding techniques, identifying the most robust preprocessing and embedding methodologies crucial for accurate sentiment analysis and model performance. We meticulously label tweets into positive, neutral, and negative classes using three distinct lexicon-based approaches and select the best one, ensuring optimal sentiment analysis outcomes and model efficacy. Here, we demonstrate that the TRABSA model outperforms the current seven traditional machine learning models, four stacking models, and four hybrid deep learning models, yielding notable gain in accuracy (94%) and effectiveness with a macro average precision of 94%, recall of 93%, and F1-score of 94%. Our further evaluation involves two extended and four external datasets, demonstrating the model’s consistent superiority, robustness, and generalizability across diverse contexts and datasets. Finally, by conducting a thorough study with SHAP and LIME explainable visualization approaches, we offer insights into the interpretability of the TRABSA model, improving comprehension and confidence in the model’s predictions. Our study results make it easier to analyze how citizens respond to resources and events during pandemics since they are integrated into a decision-support system. Applications of this system provide essential assistance for efficient pandemic management, such as resource planning, crowd control, policy formation, vaccination tactics, and quick reaction programs.

Sentiment Analysis of Dune: Part Two Movie Reviews Using the Naive Bayes Method

Research on films is fascinating because of the profound changes that the development of information and communication technology has brought about in our interactions with and consumption of media content. This study performs sentiment analysis on "Dune: Part Two" movie reviews using the Naïve Bayes method. Review data was collected from IMDb and then processed through several stages such as preprocessing, feature selection with TF-IDF, data splitting, and data mining and evaluation. Naïve Bayes was chosen for its simplicity and ability to handle large datasets effectively. The test results showed a high accuracy rate of 95%, indicating that this model can identify positive, negative, and neutral sentiments well. The use of TF-IDF in feature selection allowed the model to focus on important words, enhancing its sentiment classification ability. This research can provide insights into audience perceptions of the film "Dune: Part Two," which is beneficial for the film industry.

Analisis Sentimen pada Ekspedisi Kurir Online di Indonesia Menggunakan Algoritma Naive Bayes

This journal focuses on sentiment analysis of four popular online courier expeditions in Indonesia, namely JNT, JNE, Shoope Express, and Anteraja. The purpose of this research is to understand the opinions and responses of users towards the online courier expedition service. The research was conducted using the sentiment analysis method, which utilizes data from user reviews contained in each online courier application on the Playstore platform. In this study, the Naive Bayes algorithm is used to perform sentiment analysis. This algorithm was chosen with the aim of producing higher accuracy results in determining positive and negative sentiments from user reviews. By using this method, this research hopes to provide a deeper understanding of the perceptions and experiences of users of the online courier service under study. The results of the study show that there are variations in user sentiment for each online courier expedition. User reviews include aspects of satisfaction with service, desired complaints, and problems that often arise when using the online courier application. These findings provide valuable insights for online courier service providers, as it can help them improve service quality and customer satisfaction. By understanding user opinions and feedback, online courier service providers can identify deficiencies in their services and make necessary improvements.

Performing sentiment analysis to trace the history of identity and belonging in ancient Greek literature

Abstract This paper presents a Natural Language Processing approach for the in-depth study of ancient Greek literature. Specifically, our project mines the largest data collection of ancient Greek literature to extract descriptive words and their contextual collocations. We then present the Python libraries developed to perform sentiment analysis with a focus on words about gender, race, religion, ethnicity, and other distinguishable characteristics and how they were used by Greek authors. Finally, we showcase some initial findings as a proof of concept.

Harmonizing sentiments: Analyzing user reviews of Spotify through sentiment analysis

This research investigates the sentiment of user reviews on Spotify, with a particular focus on the Indonesian market, leveraging advanced sentiment analysis techniques. We employed three prominent classification models—Naïve Bayes, Support Vector Machine (SVM), and Random Forest—to analyze a dataset of 14,296 user reviews extracted from the Google Play Store and App Store. These findings reveal that the SVM model achieved the highest performance, with an F1-score of 0.875 and an accuracy of 0.874, outperforming Naïve Bayes and Random Forest, which scored accuracies of 0.857 and 0.856 respectively. These results highlight not only the significance of this research which offers valuable contributions to the broader academic discourse on digital marketing, sentiment analysis, and consumer behavior. Additionally, it also showcases the robustness and superior performance of SVM and Random Forest in various sentiment analysis contexts. This study not only provides valuable insights for Spotify’s future development strategies but also contributes to the broader academic discourse on sentiment analysis and machine learning model performance in digital marketing. By highlighting the efficacy of specific models, this research underscores the importance of model selection in sentiment analysis, paving the way for more accurate and effective sentiment analysis applications in the music streaming industry.