Aug 2026· bit-Tech· Vol 9, pp. 1082-1093· 0 citations
TL;DR
GovBERTic (Government BERTopic), a reproducible computational pipeline that operationalizes public opinion analysis by linking semantic topic discovery, multi-algorithm sentiment model selection, and topic-level sentiment aggregation within a single workflow, is developed.
Abstract
Social media has become a dynamic arena through which citizens express evaluations of government performance, yet the scale, heterogeneity, and informality of online discourse complicate systematic issue detection and sentiment interpretation. This study aims to develop GovBERTic (Government BERTopic), a reproducible computational pipeline that operationalizes public opinion analysis by linking semantic topic discovery, multi-algorithm sentiment model selection, and topic-level sentiment aggregation within a single workflow. The analysis used 7,877 tweets collected from X concerning the Prabowo–Gibran administration, which were cleaned and filtered to produce 6,569 valid tweets. BERTopic, supported by multilingual sentence embeddings, UMAP, HDBSCAN, and class-based TF-IDF, was applied to identify dominant discourse themes, while CatBoost, Multi-Layer Perceptron, and Complement Naïve Bayes were compared through a model selector for three-class sentiment classification. BERTopic identified eight main topics with a Topic Diversity score of 0.9125, covering government policy, development, food security, education, drug eradication, Papua, and Palestine. Complement Naïve Bayes achieved the best relative performance, with 64.61% accuracy and a 59.21% F1-score, indicating a moderate but computationally efficient baseline rather than definitive high-accuracy sentiment monitoring. Topic-level sentiment analysis showed that negative sentiment dominated four topics, neutral sentiment dominated three topics, and positive sentiment appeared in one topic. These findings suggest that GovBERTic can support exploratory, data-informed government communication analysis by connecting salient policy issues with their associated public sentiment patterns.
In the context of digital governance, how to scientifically and accurately assess the public attitudes towards grassroots government new media has become a key issue for enhancing governance efficiency. Traditional sentiment analysis methods based on dictionaries or shallow models are unable to cope with the complex semantics and dynamic evolution characteristics of public expressions. Therefore, this study proposes and constructs a dynamic map quantitative assessment algorithm for government public attitudes based on deep semantic perception. This algorithm integrates pre-trained language models, neural topic modeling, and time series analysis, and achieves the integrated decomposition of sentiment tendencies, intensities, related issues, and evolution trajectories in comment texts through end-to-end joint learning. The algorithm can not only output fine-grained sentiment quantification values and topic attributions, but also generate multidimensional assessment vectors such as polarization indices that depict attitude differences and sensitivity indicators that reflect the impact of events. Through systematic designed comparative experiments, this algorithm demonstrates superior performance in the accuracy of sentiment and topic analysis, the explainability of association attribution, the sensitivity to public opinion dynamics, and the practicality of cross-case comparison. The research shows that this algorithm can extract a structured and interpretable "attitude-issue-time" three-dimensional assessment map from massive and unstructured interactive data, providing a data-driven, hierarchical, and highly systematic analytical tool for understanding public emotions, identifying core issues, and evaluating communication effects. It has significant methodological innovation value and practical application potential.
An-Guo Wu, Zhang Yu, Bo Yang· International Conference on...· 0 citations
The social media platform X has become an important channel for the public to express opinions and share experiences regarding public services, including Trans Jogja. User-generated content from this platform provides valuable insights into public perceptions of service quality. However, because these data consist of unstructured text, sentiment classification techniques based on machine learning are required to analyze them effectively. This study aims to compare the performance of several machine learning algorithms for sentiment classification, including Naïve Bayes, Support Vector Machine (SVM), Random Forest, Neural Network, Logistic Regression, and Decision Tree, in classifying user sentiment toward Trans Jogja on the X platform. Data were collected through a web crawling process using Tweet Harvest with keywords related to Trans Jogja, covering the period from January 1, 2025, to June 10, 2026, resulting in a dataset of 3,035 tweets. The preprocessing stage included data cleaning, case folding, tokenization, normalization, stopword removal, and stemming. Text representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The dataset was then divided into training and testing sets using five train–test split ratios: 90:10, 85:15, 80:20, 75:25, and 70:30. Model performance was evaluated using a confusion matrix and the corresponding accuracy, precision, recall, and F1-score metrics. The experimental results demonstrate that the Support Vector Machine (SVM) consistently outperformed the other algorithms across different data split ratios. At the 85:15 train–test split, the SVM achieved an accuracy of 91%, precision of 91%, recall of 91%, and an F1-score of 91%, indicating that it is the most effective algorithm for sentiment classification of Trans Jogja users on the X platform.
Putri Muryanti Setyowati, Y. Pristyanto, Arif Nur Rohman· SISTEMASI· 0 citations
The high flow of information from online media in Indonesia makes it difficult for manual analysis to identify emerging themes and sentiments. News headlines, as the first element seen by the public, play a crucial role in shaping opinion, but their massive volume and diverse themes make it difficult for manual analysis to identify topics and their underlying sentiments. To address this challenge, this study analyzed 30,329 news headlines from the online news portal detik.com for the entire year 2024. A quantitative Natural Language Processing (NLP) framework was applied, consisting of data collection through web scraping, text preprocessing, transformer-based topic modeling using BERTopic, sentiment classification using IndoBERT, and a topic sentiment intersection analysis. Preprocessing included case folding, text cleaning, normalization of informal words, and tokenization. For lexicon-based labeling, stopword removal and stemming were applied, while transformer-based models utilized minimally processed text to preserve contextual information. Topic modeling was performed using BERTopic, while sentiment classification (positive, negative, and neutral) used the IndoBERT model. The main objective of this study was to evaluate the combined performance of the two models in mapping dominant issues and the sentiments contained in media reports. The results showed that BERTopic successfully identified 366 topics. An evaluation of the 10 most dominant topics yielded a coherence score of 0.5145, indicating a relevant topic clustering. The IndoBERT demonstrated high agreement with lexicon-generated sentiment labels, with an accuracy of 94.78%, a precision of 95.04%, a recall of 94.79%, and an F1-score of 94.81%. These findings confirm that the combination of transformer-based models is effective for in-depth analysis of discourse in Indonesian-language political news headlines from a major Indonesian online news portal (detik.com).
Bagas Yana Prayoga, Qurrotul Aini, Fitroh Fitroh· International Journal of Int...· 0 citations
Although the impact of digital news media on public opinion has grown significantly, there are currently no computational frameworks that empirically connect news mood to election results. This research suggests an artificial intelligence (AI)-based solution that uses deep learning and natural language processing to analyze how political news sentiment affects election outcomes. A dataset of 17,000 political news stories gathered during the general elections in Pakistan in 2024 was manually classified into three categories: good, negative, and neutral. In order to capture contextual and temporal relationships in political speech, the texts underwent normalization, tokenization, lemmatization, and modeling using a Bidirectional Long Short-Term Memory (BiLSTM) network. To evaluate media influence, party-level mood indices were calculated using model predictions and statistically associated with official vote shares. According to experimental results, the suggested BiLSTM model outperforms conventional machine learning baselines and reaches an accuracy of 89%. Additionally, sentiment indices and electoral results show a statistically significant link, suggesting a quantifiable relationship between media mood and voter behavior. The suggested paradigm offers empirical insights into the function of news sentiment in election dynamics and a repeatable technique for extensive political media study.
Yawar Abbas Abid, Muhammad Kashif, Javed Ferzund et al.· PeerJ Computer Science· 0 citations
. Social media sentiment analysis faces a persistent aggregation problem: lexicon-based and transformer-based models often produce inconsistent outputs for the same short, informal, and stylistically heterogeneous texts. This paper introduces ADRTW (Adaptive Dynamic Reliability-Trig-gered Weighting), an interpretable sentiment fusion framework that combines heterogeneous sentiment estimators using rule-guided reliability weights derived from textual cues, inter-model disagreement, and consistency patterns [5, 8]. The framework is evaluated on a Reddit dataset containing 1,577 posts, 354,050 comments, and 187,666 authors collected between 2017 and 2025, together with a controlled synthetic benchmark for aggregation comparison. The results show that ADRTW remains competitive with static averaging in controlled settings while preserving context-sensitive local variation in large-scale discourse analysis. Beyond sentiment fusion, the ADRTW-derived signal supports complementary analyses of online discussions, including temporal trend inspection, toxicity-aware interpretation, and participation-based clustering. Overall, the proposed framework provides a transparent and reusable basis for examining emotional dynamics in social media discourse.
Aniko Apro, L. Sasi· Annales Mathematicae et Info...· 0 citations
Aspect-Based Sentiment Analysis (ABSA) is a vital method for extracting detailed opinions from customer feedback, but current approaches often overlook important factors such as menu variety and struggle with class imbalance in real-world data. This research propose a hybrid lexical-probabilistic ensemble system that combines SentiWordNet lexical resources with a dual-branch Naïve Bayes ensemble, using Multinomial Naïve Bayes for text features and Gaussian Naïve Bayes for continuous lexical sentiment features, to enhance accuracy across five restaurant aspects: food quality, service, physical environment, price fairness, and menu variety. The study was evaluated on 10,000 Kaggle restaurant reviews. After excluding neutral reviews, the remaining binary dataset was imbalanced, and evaluation was performed using a stratified 80:20 train–test split. Dataset preprocessing through tokenization, lemmatization, and stop-word removal was applied, and aspect extraction was also performed using domain-specific keyword dictionaries enriched with WordNet synonyms, Part-of-Speech tagging, and dependency parsing, while SentiWordNet assigned aspect-level sentiment scores. A threshold optimization strategy was applied to the posterior probabilities of the positive class to improve minority-class recall while maintaining overall precision without harming majority class performance. Results show that the ensemble achieves 86% precision, 80% recall, and 82% F1-score, significantly outperforming basic classifiers such as standalone Naïve Bayes, SentiWordNet-augmented Naïve Bayes, and a hybrid ELMo-Wikipedia method. Statistical tests with the Wilcoxon signed-rank test confirm these improvements (p < 1.91e-06). This framework provides fine-grained aspect-level sentiment analytics for restaurant decision support and lays a solid groundwork for handling imbalanced sentiment analysis. Future work discusses integrating transformer-based models and multilingual support.
Keywords: Aspect-Based Sentiment Analysis, Class Imbalance, Customer Satisfaction, Ensemble Learning, Naïve Bayes Classifier, Restaurant Reviews, SentiWordNet, Threshold Optimization
Hamza Abdullahi Kwazo, M. Karatu, Sirajo Abduulahi Bakura· Bulletins of Natural and App...· 0 citations
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