Sentiment Analysis Research Articles

Sentiment Analysis of the MK Decision Trial of the Result of the 2024 President and Vice President General Election on Social Media X Using the Support Vector Machine Method

Support Vector Machine (SVM) is a method of machine learning often used in classification and regression issues, especially in the classification of commentary reviews on social media such as Twitter. The Constitutional Court (MK) has the authority to resolve disputes resulting from the general election, including the 2024 presidential election. As an institution that maintains fairness and transparency in the democratic process, the Constitutional Court's decisions are often at the center of public attention and debate, especially on social media. In the 2024 general election, various allegations of fraud led to protests from several parties who felt aggrieved. The final and binding Constitutional Court's decision is expected to resolve the conflict that arises, but it often does not satisfy all parties, causing political and social tensions. This conflict can be reflected through public opinion expressed on social media, such as Twitter, where various responses and sentiments to the decision are essential analysis materials. This Research uses the Support Vector Machine (SVM) algorithm with a dataset of 1383 review comments divided by an 80:20 ratio for training and testing. The system was implemented using the Python programming language, with evaluations showing the highest accuracy at 61.00%, precision at 61.00%, and recall at 62.00%. This study aims to analyze public sentiment regarding the Constitutional Court's decision using the SVM method and identify the tendency of public opinion as positive, negative, or neutral. Through this study, it is expected that a deeper understanding of the public's perception of the Constitutional Court's decision is obtained. In addition, this Research is likely to contribute to developing sentiment analysis methods in the future and provide a basis for recommendations for the Constitutional Court in handling election result disputes better.

JST and rJST: joint estimation of sentiment and topics in textual data using a semi-supervised approach

ABSTRACT This paper demonstrates the performance of the Joint Sentiment Topic model (JST) and the reversed Joint Sentiment Topic model (rJST) in measuring sentiment in political speeches, comparing them against a set of popular methods for sentiment analysis: widely used off-the-shelf sentiment dictionaries; an embeddings-enhanced dictionary approach; Latent Semantic Scaling, a semi-supervised approach; and a zero-shot transformer-based approach using a large language model (GPT-4). The findings reveal JST’s superiority over all non-transformer-based approaches in predicting human-coded sentiment in multiple languages and its ability to replicate known sentiment trends in legislative speech. rJST, meanwhile, provides valuable topic-specific sentiment estimates, responsive to political dynamics and significant events. Both models are, however, outperformed by transformer-based models like GPT-4. Additionally, the paper introduces the ’sentitopics’ R-package, designed to facilitate the use of JST and rJST in computational text analysis workflows. This package is compatible with popular text analysis tools, making the models accessible for applied researchers in communication science.

Revolutionizing Retail: An Empirical Study on the Impact of Generative AI in Omnichannel Strategies

This article examines the transformative impact of Generative AI (Gen AI) on omnichannel retail, exploring its role in enhancing customer experiences and streamlining operations across digital and physical platforms. Through a comprehensive analysis of current literature, industry case studies, and empirical data, the study investigates six key applications of Gen AI in retail: personalization and dynamic content creation, virtual shopping assistants, inventory and fulfillment optimization, marketing content generation, customer insights and sentiment analysis, and immersive AR/VR experiences. The findings reveal that Gen AI significantly improves personalization, operational efficiency, and cross-channel integration in retail. However, the research also identifies challenges in implementation, including data privacy concerns and the need for seamless technology integration. This article contributes to the growing body of knowledge on AI in retail and provides practical insights for retailers seeking to leverage Gen AI in their omnichannel strategies. The paper concludes by discussing future research directions and the potential long-term implications of Gen AI on consumer behavior and global retail practices.

How Gastronomic Image Shapes Tourism Competitiveness: An Approach with Sentiment Analysis

The competitiveness of tourist destinations is influenced by their relative attractiveness, which will play an essential role in their global success; gastronomy is one of the main motivations that lead tourists to visit a given destination. This research aims to investigate gastronomy’s role in the destination’s competitiveness and image through the analysis of online reputation, both in terms of ratings and sentiments provided by the experience, through the creation of an index of the online reputation of gastronomic image. Online restaurant reviews retrieved from TripAdvisor, from restaurants belonging to eight tourism destination regions, considered the competitive set to the Algarve, are analysed by applying sentiment analysis algorithms. With regard to the Algarve region, it was concluded that the Costa del Sol and the Tropical coast were the most competitive regions in terms of gastronomic image, where the inclusion of seafood products in meals was one of the strategic aspects used to increase the competitiveness of this region. These results can help restaurant managers and destination management organisations to better understand the different customer needs and how to increase their competitiveness.

Sensing Facts and Emotions: Intelligent Technology's Redefinition of News Text Presentation

Intelligent technology represented by artificial intelligence has profoundly changed the boundaries and patterns of journalism. Traditional media reports are constrained by conditions such as the shape of the newspaper layout, presenting independent and objective content of individual reports. Intelligent technology has changed this pattern, the presentation of news text will reflect the characteristics of "information layer of basic facts" and "opinion layer of emotional analysis" co-presentation, while adhering to the separation of the facts and opinions of the report itself, the typical attitudes and emotions of the user feedback and the facts of the media report are jointly presented to the public. While maintaining the separation of facts and opinions in the report itself, the typical attitudes and emotions of user feedback are presented to the public together with the facts of the media report. This will present the public with an intuitive and visualized opinion environment through sentiment and text analysis while quickly distilling and summarizing the main facts in the information flood. At the same time, intelligent technology further shortens the time lag between the occurrence of an event and the release of a news report, shortening the time lag and the perception of the importance of time lag in a cyclical manner, shaping the practitioner's view of time lag.

A Novel NLP-based Stock Market Price Prediction and Risk Analysis Framework

Stock market prediction is an interesting and complex problem that has recently been in the limelight, thanks to the significant accuracy achieved by deep learning models. However, a complete platform with prediction and risk analysis ability is unavailable. In the current work, we present a novel framework for investment analysis designed to create ease for investors and provide a confidence measure along with the stock price to depict the risk involved in investing in stocks of a particular company. The model integrates two different approaches successfully to improve accuracy significantly. The model inputs two sources – a stock price dataset depicting the original scores as numerals and textual data extracted from Reddit news articles. The traditional problem of stock price prediction is dealt with using LSTMs on individual stock prices. At the same time, the confidence is represented by a risk value calculated intelligently using XGBoost and LSTM output. We have deployed natural language processing techniques for performing sentiment and subjectivity analyses, which are then used to extract features for further investigation in the study. The results show that an accuracy of 94% for stock trend prediction can be achieved using PCA as the feature extractor with tuned parameters for XGBoost and around 76% accuracy for stock price prediction with a tuned LSTM. It removes the hassle for investors to research the project or company they want to invest in and provides all relevant analysis and data.

Revisiting forum shopping: navigating normative pluralism, navigating sentiments

People live their lives and experience the conflicts in which they are involved in the context of competing normative orders. But how do people navigate this normative pluralism? To understand this, Keebet von Benda-Beckmann’s model of ‘forum shopping’ and ‘shopping forums’, derived from an ethnographic analysis of conflict resolution, remains an indispensable starting point. Building on this approach, I argue that in order to understand the navigation of normative orders more broadly, it is important to bring the analysis of sentiments to the study of normative pluralism. Drawing on ethnographic fieldwork on the International Criminal Court’s transitional justice interventions in northern Uganda, I argue that when confronted with different international, national, religious, and indigenous normative options for dealing with mass violence, people navigate normative pluralism by creating, mobilising, maintaining, and transforming sentiments.

Key Drivers of Consumption, Conceptual, Sensory, and Emotional Profiling of Cheeses Based on Origin and Consumer Familiarity: A Case Study of Local and Imported Cheeses in Greece

The origin of a product, consumer familiarity, and purchasing identity are factors that affect the perception of cheese consumption. The present study aims at identifying consumers’ conceptualizations and attitudes towards local Greek cheeses of the North-Aegean Sea islands, such as Ladotyri, Graviera, Kasseri, Kaskavali, Melichloro, and Kalathaki, some of which have a Protected Designation of Origin (PDO) status, as opposed to cheeses of non-Greek origin, such as Cheddar, Regatto, and Gouda. Sensory and emotional attributes of local, local PDO, and imported cheeses, as well as drivers associated with consumers’ choice and acceptance above and beyond their sensory attributes, were studied using three methods: (a) flash profile to gain insight into the sensory positioning of products and description of samples; (b) qualitative analysis of focus groups to pinpoint consumer knowledge, preference, and consumption criteria; and (c) a new methodology for natural language processing and sentiment analysis of social media posts to determine consumer conceptualizations. Social media posts have proven to be a valuable source of linguistic and cultural data for cheeses. Local cheeses, including PDO products, were found to be linked to village life and family gatherings, home, tradition, and childhood memories, with saltiness and hardness being their main sensory attributes. Imported cheeses were linked to fast food, pizza, and snacking, with elasticity and gumminess as prominent sensory qualities. The main criteria for purchase were intended usage and versatility, taste and texture, price, and familiarity. The findings provide key sensory attributes, information about consumer purchasing criteria, and relevant vocabulary for the promotion of cheeses as agri-food and gastronomic identity key products.

Enhancing aspect-based sentiment analysis through graph attention networks and supervised contrastive learning

Enhancing aspect-based sentiment analysis through graph attention networks and supervised contrastive learning

Urban Color Perception and Sentiment Analysis Based on Deep Learning and Street View Big Data

The acceleration of urbanization has resulted in a heightened awareness of the impacts of urban environments on residents’ emotional states. This present study focuses on the Lixia District of Jinan City. By using urban street view big data and deep learning methods, we undertook a detailed analysis of the impacts of urban color features on residents’ emotional perceptions. In particular, a substantial corpus of street scene image data was extracted and processed. This was performed using a deep convolutional neural network (DCNN) and semantic segmentation technology (PSPNet), which enabled the simulation and prediction of the subjective perception of the urban environment by humans. Furthermore, the color complexity and coordination in the street scene were quantified and combined with residents’ emotional feedback to carry out a multi-dimensional analysis. The findings revealed that color complexity and coordination were significant elements influencing residents’ emotional perceptions. A high color complexity is visually appealing, but can lead to fatigue, discomfort, and boredom; a moderate complexity stimulates vitality and pleasure; high levels of regional harmony and aesthetics can increase perceptions of beauty and security; and low levels of coordination can increase feelings of depression. The environmental characteristics of different areas and differences in the daily activities of residents resulted in regional differences regarding the impacts of color features on emotional perception. This study corroborates the assertion that environmental color coordination has the capacity to enhance residents’ emotions, thereby providing an important reference point for urban planning. Planning should be based on the functional characteristics of the region, and color complexity and coordination should be reasonably regulated to optimize the emotional experiences of residents. Differentiated color management enhances urban aesthetics, livability, and residents’ happiness and promotes sustainable development. In the future, the influences of color and environmental factors on emotions can be explored in depth, with a view to assist in the formulation of fine urban design.

Ethical Concerns Upon Artificial Intelligence Empowered Human Resource Management: A Qualitative Study among Middle-level Managers from Beijing Technology Companies

The evolution of artificial intelligence (AI) in organizational management has significantly enhanced operational efficiency. However, it has introduced ethical challenges in human resource management. Between January to March 2024, this study conducted 21 semi-structured interviews with middle-level managers from high-tech companies in Beijing. Through word frequency analysis, the study found that key topics among the managers were “company,” “data,” “system,” and “problem,” with “AI” frequently recurring in the discussions. Sentiment analysis revealed generally favorable attitudes toward AI in human resource management (AI-HRM), along with nuanced emotional expressions such as inquiry, introspection, recommendation, challenge, adaptation, resistance, and indifference. The sentiment distribution of keywords aligned with topic trends. Thematic analysis identified key ethical concerns in AI-HRM, including issues related to data collection and utilization, human versus machine decision-making, quantitative versus qualitative assessment methodologies, the balance between fairness and efficiency, the need for trustworthy, explainable, and transparent AI, and the oversight of AI-HRM. This study contributes to the ethical investigation of AI-HRM from the perspective of middle-level managers, highlighting themes that are critical for understanding the theory, application, and future development of AI-HRM.

Reconstructing representations using diffusion models for multimodal sentiment analysis through reading comprehension

The primary challenge in multimodal sentiment analysis (MSA), which utilizes textual, audio, and visual information to analyze speakers' emotions, lies in constructing representation vectors that incorporate both unimodal semantic and multimodal interaction information. While existing research has extensively focused on multimodal fusion strategies, there remains insufficient exploration of the intrinsic potential within concurrently enhancing unimodal and multimodal representations. To address this gap, we introduce two additional steps to traditional three-step MSA: text modality enhancement through the machine reading comprehension (MRC) framework and multimodal representation reconstruction via proposing the diverse diffusion denoising autoencoder (D3AE) module. The MRC queries are integrated to locate sentiment-related prior knowledge, thereby deepening textual semantic understanding from a pretrained language model. Meanwhile, D3AE employs a single-step denoising strategy along with diffusion models across multiple time intervals, enabling efficient reconstruction and enhancement of multimodal representations. Extensive experiments conducted on two benchmark datasets, CMU-MOSI and CMU-MOSEI, validate that our model, MRC-D3AE, achieves state-of-the-art performance. The superiority of our model over existing baselines is primarily attributed to integrating MRC for enhancing text modality and D3AE for reconstructing multimodal representations.

Enhancing XGBoost Classification with SVM-SMOTE & EasyEnsemble for Imbalanced Telemedicine Sentiment Data

Telemedicine is the practice of health through applications using audio, visual and data communication, including care, diagnosis, consultation and treatment as well as remote medical data exchange. Based on the results of sentiment analysis on telemedicine applications, imbalance data is often found. The purpose of this research is to identify the use of SVM-SMOTE and EasyEnsemble in improving the performance of XGBoost classification on sentiment data imbalance in Telemedicine. Identification is done by including SVM-SMOTE and EasyEnsemble methods in improving XGBoost Classification Performance using data obtained from the Halodoc application, then validation techniques will be carried out using AUC and GMeans. The results showed that the use of SVM SMOTE and EasyEnsamble for data imbalance in XGBoost obtained the best model that is feasible to use in improving the performance of imbalance classification of sentiment data in health applications.

Analysis Sentiment & Topic Modelling using Latent Semantic Indexing

The proliferation of digital news platforms has transformed information consumption, making user experience (UX) a key success factor for Detik.com, a leading news application in Indonesia. This research aims to explore the relationship between UX and news through sentiment analysis and topic modelling. The methods include web scraping for data collection, descriptive analysis for sentiment, and Latent Semantic Indexing (LSI) for topic modelling. Out of 14,320 reviews analyzed, 45.6% were positive, focusing on the speed of updates and news quality. Conversely, 41.18% of reviews were negative, highlighting disruptive ads and login issues, while 13.76% were neutral. LSI identified eight main topics, including user experience and ad complaints. Visualizations through heatmaps, distance maps, and word clouds emphasized terms like "news" and "ads." This research recommends that Detik.com developers reduce ad disruptions, fix technical problems, and understand frequently discussed topics to enhance user satisfaction.

Leveraging deep learning with sentiment analysis for Online Book reviews polarity classification model

Leveraging deep learning with sentiment analysis for Online Book reviews polarity classification model

Is there innovation management of emotions or just the commodification of happiness? A sentiment analysis of happiness apps

PurposeThe aim of this research is to gain insight into the emotional impact and cognitive evaluations that these applications have on users from their own perspective.Design/methodology/approachSentiment analysis methodology is used to analyze users’ comments and ratings, assess perceived benefits and determine the emotional impact these applications have on them.FindingsThe results suggest a benefit for happiness scholars, professionals, users and organizations focused on employee well-being and provide valuable insights into the evolving landscape of happiness applications. While the study relies on publicly available user data, it complements, but does not replace, market research or product evaluation conducted by app developers and companies.Originality/valueAmong the myriad channels disseminating the assumption that the good life is attainable through personal means and psychological know-how, smartphone applications have emerged as accessible, cost-effective and attractive tools to help users become happier and alleviate feelings of distress and discomfort.

Product ranking considering differences across online review platforms: a method based on intuitionistic fuzzy soft sets

Numerous online review platforms have become flooded with a large number of textual reviews, allowing consumers to compare and select products based on online reviews from multiple platforms. Existing studies have found that there may be certain differences in the content focus of online reviews from different platforms and that the evaluation indicators of different platforms may be different. Ranking products by considering differences in platforms and integrating online reviews from multiple platforms is of great significance in supporting consumer purchase decisions; however, relevant studies are relatively scarce. Therefore, this study proposes a product ranking method using intuitionistic fuzzy soft sets, considering the differences in platforms. First, online reviews were preprocessed to obtain product indicator parameters on different platforms. Second, a sentiment analysis algorithm was used to determine the sentiment strengths of the indicator parameters. Subsequently, an intuitionistic fuzzy soft set for each platform concerning the indicator parameters was constructed. Furthermore, a comprehensive intuitionistic fuzzy soft set was obtained by the AND operation, and the ranking of the products was determined. Finally, a case study was conducted to illustrate the use of the proposed method. Through a comparative analysis, the characteristics and advantages of the proposed method were further illustrated.

A New Contrastive Learning based Vision Transformer for Sentiment Analysis using Scene Text Images

A New Contrastive Learning based Vision Transformer for Sentiment Analysis using Scene Text Images

Sentiment analysis using graph-based Quickprop method for product quality enhancement

ABSTRACT The degree to which customers express satisfaction with a product on Twitter and other social media platforms is increasingly used to evaluate product quality. However, the volume and variety of textual data make traditional sentiment analysis methods challenging. The nuanced and context-dependent nature of product-related opinions presents a challenge for existing tools. This research addresses this gap by utilizing complex graph-based modelling strategies to capture the intricacies of real-world data. The Graph-based Quickprop Method constructs a graph model using the Sentiment140 dataset with 1.6 million tweets, where individuals are nodes and interactions are edges. Experimental results show a significant increase in sentiment classification accuracy, demonstrating the method’s efficacy. This contribution underscores the importance of relational structures in sentiment analysis and provides a robust framework for extracting actionable insights from user-generated content, leading to improved product quality evaluations. The GQP-PQE method advances sentiment analysis and offers practical implications for businesses seeking to enhance product quality through a better understanding of consumer feedback on social media.

Application of the Naïve Bayes Classifier Algorithm to Analyze Sentiment for the Covid-19 Vaccine on Twitter in Jakarta

The epidemic of a new disease caused by the coronavirus (2019-nCoV), commonly referred to asCOVID-19, has been declared a global virus epidemic by the World Health Organization (WHO).President Joko Widodo has officially ratified Presidential Decree No. 99 of 2020 concerning theprovision of vaccines and the implementation of vaccination activities. Twitter is a social mediaplatform that allows users to share information and opinions directly with fellow users. Tweets givencan be in any form, either positively or negatively, so one of the methods used is sentiment analysis.Sentiment analysis helps determine an opinion or comment on an issue, whether the response ispositive or negative. The Naïve Bayes algorithm is used in sentiment analysis because it is suitablefor tweets or text data that is not too long or short text. The initial stage of sentiment analysis is textpre-processing which consists of Cleaning, case folding, tokenizing, and stopword removal. Then thedata is labeled manually. The analysis results are visualized as bar charts, pie charts, and word clouds.Then the word weighting is carried out using the term frequency-inverse document (TF-IDF), andclassification is carried out using the Naïve Bayes classifier. From the test results, the accuracy valueof the confusion matrix is 82% from 2600 tweet data with 80% training data composition and 20%test data.