Stop Word Removal Research Articles

Sentiment Analysis of Hotel Reviews in Anyer Beach Tourism Area: A Lexicon-Based Text Mining Approach

This study aims to analyze tourist sentiment towards hotels in the Anyer beach tourism area through online reviews. Sentiments and emotions expressed in these reviews were analyzed using a lexicon-based approach from the National Research Council (NRC) lexicon, which includes emotion categories such as anger, fear, sadness, joy, and disgust. Hotel review data were collected from TripAdvisor, and the analysis involved text preprocessing steps such as cleaning, case folding, stemming, and stopword removal. The sentiment analysis results reveal that most tourists provided positive reviews, with only 14.06% of the reviews displaying negative sentiment. Word cloud visualization identified the words "bad" and "dirty" as frequently appearing in negative reviews, likely pointing to cleanliness and service quality issues as the primary reasons for dissatisfaction. Emotional analysis showed dominant feelings of sadness, anger, disgust, and fear, likely triggered by unmet expectations regarding cleanliness and comfort during their stay. Based on these findings, it is recommended that hotel management in the Anyer beach tourism area address these concerns by implementing regular cleaning routines and staff training to enhance hotel service quality.

Fake News Detection Using Machine Learning

Abstract: The identification of fake news has grown in importance as false information circulates quickly via digital channels. The goal of this project is to combine deep learning and machine learning to create an automated system that can identify news stories as legitimate or fraudulent. The preprocessed text data in the dataset, which comprises of tagged news items, was done using methods such tokenization, stop word removal, and padding. A Convolutional Neural Network (Conv1D), which is based on deep learning, was combined with more conventional machine learning models, such as Naive Bayes, in a hybrid approach. After converting the text input into a series of tokens, semantic meanings were captured by using word embeddings. Important patterns from the word sequences were intended to be captured by the Conv1D model with global max pooling, whereas the Naive Bayes also used in the project.

An Efficient Deep Learning Mechanism for Predicting Fake News/Reviews in Twitter Data

Recently, social media platforms have been widely utilized as information sources due to their effortless accessibility and reduced costs. However, online platforms like Instagram, Twitter and Facebook get influenced by their users via fake news/reviews. The main intention of spreading fake news is to mislead other network users, which highly affects businesses, political parties, etc. Thus, an effective methodology is needed to predict fake news from social media automatically. The major objective of this proposed study is to identify and classify the given Twitter input data as real or fake through deep learning mechanisms. The proposed study involves four stages: pre-processing, embedded word analysis, feature extraction, and fake news/reviews prediction. Initially, pre-processing is performed to enhance the quality of data with the help of tokenization, stemming and stop word removal. Embedded word analysis is done using Advanced Word2Vec and GloVe modeling to enhance the performance of a proposed prediction model. Then, the hybrid deep learning model named Dense Convolutional assisted Gannet Optimal Bi-directional Network (DC_GO_BiNet) is introduced for feature extraction and prediction. A Dense Convolutional Neural Network (DCNN) is hybridized with a bi-directional long-short-term memory (Bi-LSTM) model to extract the essential features and predict fake news from the given input text. Also, the proposed model’s parameters are fine-tuned by adopting a gannet optimization (GO) algorithm. The proposed study used three different datasets and obtained higher classification accuracy as 99.5% in Fake News Detection on Twitter EDA, 99.59% in FakeNewsNet and 99.51% in ISOT. The analysis proves that the proposed model attains higher prediction results for each dataset than others.

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.

Analisis Sentimen Opini Publik Terhadap Cyberbullying Pada Komentar Instagram Menggunakan Multinomial Naive Bayes

This study aims to evaluate the effectiveness of a sentiment classification model using the Naïve Bayes algorithm on Instagram comment data. The main focus of this research is to measure the performance of the model in terms of accuracy, precision, recall, and F1-score. The data used in this study includes 400 Instagram comments that have been labeled with negative and positive sentiments. Data pre-processing involved case folding, tokenization, stopword removal, and stemming, followed by TF-IDF weighting to measure the importance of each word. The data was divided into 80% for training and 20% for testing. The Naïve Bayes model was then applied to the test data to predict sentiment. The evaluation results show that the model achieved an accuracy of 86.25%, with a precision of 85.56%, recall of 86.46%, and F1-score of 85.88%. For the negative class, the precision reached 91%, recall 85%, and F1-score 88%, while for the positive class, the precision was 80%, recall 88%, and F1-score 84%. These findings show that the Naïve Bayes model is effective in classifying the sentiment of Instagram comments and provides useful insights in understanding public sentiment towards the issue of cyberbullying

Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement.

Fake News Detection with Machine Learning Algorithms

Nowadays, with the advancement of technology, the use of news sources has also undergone a great evolution. News sources have constantly evolved from past to present, ranging from magazines to radios, from newspapers to televisions. The fact that it has become so easy to access news has caused society to pay more attention to fake news. Fake news has the ability to influence society through news sources such as social media, which can reach wider audiences with the development of technology. The difficulties of users in accessing accurate and reliable sources in this information flow that shapes their daily lives increases the potential for the spread of fake news, and it becomes increasingly difficult to distinguish between real and fake news. In this study, classification models for fake news detection were designed using machine learning algorithms. The dataset, which includes fake and real news examples, contains 42,000 examples. Each class, including fake and real samples, contains 22,000 sample data. In order to increase data quality, accuracy and usability, preprocessing methods were applied to the data set. The removal of numbers, stop words, and html tags was done in the pre-processing step to remove unnecessary information from the text. Models were created for fake news detection with singular and ensemble classification algorithms. Performance evaluation of the models was performed using 5-fold cross-validation. In the performance comparisons of the models, values such as accuracy, sensitivity, specificity, tp rate and fp rate were calculated. The highest performance results were observed in the random forest classification algorithm with an accuracy rate of 76%.

Feature Engineering for Agile Requirement Management Using Semantic Analysis

Efficient management and prioritization of software requirements are critical challenges in agile projects, where requirements constantly evolve due to changing user needs, business goals, and regulatory updates. This paper explores the role of semantic feature extraction in enabling adaptive management strategies. Using the PROMISE Expanded Dataset and the Coquina Dataset, we employed TF-IDF weighted Word2Vec for advanced tokenization and feature extraction. Latent Dirichlet Allocation (LDA) was used to analyze how preprocessing steps like stop word removal impact topic representation, revealing that removing stop words improved topic specificity and coherence. To address class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was applied, enhancing the model's ability to handle underrepresented classes effectively. Principal Component Analysis (PCA) reduced the dimensionality of TF-IDF weighted Word2Vec embeddings from 100 features to 30, while Analysis of Variance (ANOVA) identified the most significant features for classification. The results obtained identified three features to have p-values below 0.05 as statistically significant, p-value = 0.0000605, p-value = 0.00000000469, and p-value = 0.0024. These extracted features could be used as input to training machine learning models for predicting and managing software requirements adaptively during agile development. With the reduction of ambiguities and sentiments of the user at the requirement phase, the development phase could be undertaken seamlessly with ease.

Smart Hostel Allocation System Using Naïve Bayes Algorithm

This research explores the potential of automated personality classification using supervised machine learning techniques, with a specific focus on the Myers-Briggs Type Indicator (MBTI) for hostel allocation. The problem is the manual and resource-intensive allocation of hostel accommodations in educational institutions, leading to personality mismatches among roommates, which negatively impacts students' academic engagement and well-being. The aim of this research is to improve hostel room allocation by predicting student personality traits, thus minimizing personality mismatches among roommates. A Naïve Bayes classifier was applied to the MBTI personality dataset acquired from Kaggle. Various preprocessing techniques, such as tokenization, stop-word removal, and word stemming, were used to prepare the dataset for feature selection. CountVectorizer and TF-IDF methods were employed to convert text into numerical vectors for machine learning analysis. Additionally, SMOTE was implemented to address class imbalance within the dataset, ensuring better model performance. The Naïve Bayes classifier showed promising results, with scores achieved across all personality traits as 73% precision, 70% accuracy, F1 Score 70% and Recall 70% in predicting MBTI personality traits from input text. Visualizations and performance metrics such as accuracy, precision, recall, and F1-score were used to evaluate the model's efficiency. The research findings suggest that supervised machine learning techniques can effectively predict personality traits from social media data, aiding in improved hostel room allocation and potentially benefiting broader applications in education and computational psychology. This system is recommended for educational institutions and residential complexes seeking efficient roommate pairing.

Sentiment Analysis of Sirekap Tweets Using CNN Algorithm

Background: The research investigates the application of deep learning models for sentiment analysis on Twitter data related to Indonesia's Sirekap system. Sentiment analysis is crucial for understanding public opinion and enhancing the transparency and reliability of election result recapitulation processes. Objective: The objective of this study is to compare the performance of Convolutional Neural Networks (CNN) and CNN-LSTM models in analyzing sentiments from tweets about the Sirekap system. The study aims to identify the most effective model and preprocessing techniques to improve sentiment classification accuracy. Methods: A comprehensive data preprocessing pipeline was implemented, including cleansing, case folding, tokenizing, normalization, stopword removal, and stemming. To address class imbalance, the SMOTE technique was applied. The models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. Pre-trained word embeddings were used to enhance model performance. Results: The CNN model achieved an accuracy of 85.90%, outperforming the CNN-LSTM model, which achieved 79.91% accuracy. Additionally, the CNN model demonstrated superior precision, recall, and F1-score metrics compared to the CNN-LSTM model. The thorough preprocessing and handling of class imbalance significantly contributed to the enhanced performance of the CNN model. Conclusion: The research emphasizes the effectiveness of deep learning approaches, particularly CNNs, in sentiment analysis tasks. The findings highlight the importance of comprehensive preprocessing and class imbalance handling. The use of pre-trained word embeddings and various evaluation metrics ensures robust model performance. These insights contribute to improving the accuracy and efficiency of sentiment classification, thereby enhancing the reliability and transparency of election result recapitulation processes.

Sentiment Analysis of Visitor Reviews on Baturaden Tourist Attraction Using Machine Learning Methods

This study evaluates the performance of four machine learning models: Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes in analyzing visitor reviews of the Lokawisata Baturaden tourist attraction. Using 5-fold cross-validation, the study aims to determine which machine learning model best suits sentiment analysis on the Baturaden review data. This study was conducted through several stages, including data preprocessing, feature extraction, and the data training process. Case folding, text cleaning, tokenization, stopword removal, and stemming were performed during the data preprocessing stage. The feature extraction method used was TF-IDF. SMOTE was applied to increase data variation and address the data imbalance in the dataset. The results show that SVM provides the best performance with an accuracy of 0.937, an F1-score of 0.937, a precision of 0.943, and a recall of 0.937. Random Forest also performs well with an accuracy of 0.918 and an F1-score of 0.918, though slightly below SVM. KNN shows the lowest performance with an accuracy of 0.651 and an F1-score of 0.544, while Naive Bayes performs adequately with an accuracy of 0.845 and an F1-score of 0.841. Based on this evaluation, SVM is recommended as the best model for sentiment analysis of reviews, followed by Random Forest as a good alternative. The KNN model is not recommended due to its lower performance, while Naive Bayes can be considered for its speed and simplicity, although its results are not as good as SVM and Random Forest. These conclusions guide the selection of the optimal model to enhance understanding and visitor experience at the Baturaden tourist attraction.

PROCESSAMENTO DE LINGUAGEM NATURAL PARA RECONHECIMENTO DE ENTIDADES NOMEADAS EXTRAÍDAS DE CORPUS

This article presents a code implementation to automate the reading, analysis and generation of summaries of unstructured web texts, highlighting the crucial role of Natural Language Processing (NLP). The process was divided into two stages: data collection and pre-processing. In the first stage, a scientific article was selected and relevant data was extracted via web scraping, organized into an HTML page hosted online. In pre-processing, tokenization, normalization, stopword removal and word counting were performed. Using NLTK, the most important sentences were identified and ranked based on keyword frequency, allowing the selection of the most relevant sentences from the introduction, methodology, results/dicussion and conclusion sections. Discrepancies such as links and figure descriptions have been removed to improve the clarity of the abstract. The “most_common()” method was used to select the most relevant words in each section. After additional processing to delete unnecessary words, the final summary was clear and understandable, despite minor flaws. This automated approach provides an efficient way to synthesize information from scientific articles, optimizing the textual analysis process.

K-Means text clustering method based on Decision Grey Wolf Optimization

Aiming at the problem that the traditional algorithm is easy to fall into local optimum in the process of text clustering, which leads to inaccurate text clustering results, a text clustering method based on decision grey wolf optimization K-Means is proposed to cluster the text data set and the standard UCI data set respectively. Afterword segmentation, stop words removal, feature extraction, and text vectorization of text data, the powerful optimization ability of the Decision Gray Wolf Optimization (DGWO) algorithm is used for global optimization, and the clustering center in K-Means algorithm is replaced by the location of wolves. The position of the wolf group is updated by iterative optimization to obtain the optimal clustering center, to perform text clustering. The experimental results show that compared with the traditional method, the precision, recall, and F-Measure of the text data clustering are improved by 49.22%, 51.15%, and 48.98% respectively. The precision, recall, and F-Measure of UCI data clustering are increased by 23.92%, 25.40%, and 24.70% respectively, and the text clustering results are more reliable.

Sentiment Analysis of Joglo Wifi UMKM Service with Naive Bayes Method

In today's digital era, many businesses face challenges in managing and responding to customer feedback and suggestions contained in comments. Joglo Wifi Corner UMKM Business Center & Coffee experiences similar difficulties, where the lack of customer feedback management negatively impacts service quality and customer satisfaction. In this study, we aim to develop and implement a sentiment analysis system that is able to classify customer sentiment into positive, negative, or neutral. The process begins with data collection. customer comments from the official Joglo Wifi Corner website, which are then processed through a preprocessing stage including tokenization, stop word removal, and lemmatization. Then, a Logistic Regression Naive Bayes model is drilled using the processed data to classify sentiment. The evaluation results show that this system achieves an accuracy of 91.67%, with an average precision of 0.646, an average recall of 0.805, and an average F1-Score of 0.701. The implementation of this system provides valuable insights for Joglo Wifi Corner managers in making strategic decisions to improve service quality. By responding more effectively to customer feedback, Joglo Wifi Corner promises that this research can improve customer satisfaction and expand the company's market coverage and identify areas for improvement based on customer feedback, thereby strengthening customer relationships. This approach also uses Agile Development system development methodology to ensure iterative and responsive development to changing user needs.

IMPLEMENTASI TEKNIK SMOTE UNTUK MENGATASI IMBALANCE CLASS DALAM KLASIFIKASI SENTIMEN MENGENAI CHATGPT

ChatGPT is a chatbot or computer program in the form of a virtual robot that can simulate human-like conversations. ChatGPT is widely used in various fields in academia. The impact of the use of ChatGPT on academia and public perception of this technology is significant. Sentiment analysis can be used to determine the polarity of a text or opinion that is positive or negative. In this research, social media is used as a data source to collect public opinion regarding ChatGPT instantly. The methods used in this reserach are the KNN algorithm and Naive Bayes algorithm. The aim of this research is to find the best algorithm model for sentiment classification in terms of public opinion for ChatGPT which contains English text. Before testing the algorithm model, a text processing stage was carried out which included the processes of case folding, tokenizing, stopword removal, and stemming. Word weighting using TF-IDF was carried out before the data was ready to be processed. Splitting data used in this research includes 80% of the dataset as training data and 20% of the dataset as testing data. The application of the SMOTE technique to the KNN and Naive Bayes algorithms to overcome the imbalance class of the public opinion dataset regarding ChatGPT. The research results show that combining SMOTE and Naive Bayes algorithm gives the best results with an accuracy value of 85.00%, a precision value of 87.64%, a recall value of 84.78% and an f1-score of 86.18%.

Topic Modeling of the 2024 Election Using the BERTopic Method on Detik.com News Articles

In 2024, Indonesia will hold simultaneous general elections dominated by the participation of young people, especially Generation Z and Millennials, who seek political information primarily through the Internet, highlighting the crucial role of digital media in shaping public opinion. Detik.com is actively reporting on the 2024 elections, evidenced by a special election subchannel. However, the lack of topic categorization in this subchannel makes it difficult for readers to find in-depth information, and tracking and analyzing the large volume of news articles published daily is a significant challenge. This study employs Topic Modelling techniques, specifically the BERTopic method, to analyze topics related to the 2024 elections from Kompas.com news articles. The dataset, sourced from the detik.com election sub-channel, was collected via scraping from September 1, 2023, to February 14, 2024, totalling 15,019 articles. The text preprocessing involves case folding, cleaning, tokenizing, and stopword removal. Topic modelling using BERTopic includes embeddings with sentence-transformers "distiluse-base-multilingual-cased-v1," dimensionality reduction with UMAP, clustering with K-Means using optimal k=5 value evaluated by Elbow, tokenizer with CountVectorizer, and weighting scheme using c-TF-IDF. Based on the Silhouette Score of 0.566 and Silhouette plot results, the clustering results using the K-Means model with a value of k equal to 5 produce good clustering with clear inter-cluster distances. For other evaluations, the SSE value of 70223.257 provides an overview of the cluster distribution, the Davies-Bouldin Index of 0.758 shows that the cluster has a relatively good level of inter-cluster separation with good closeness within the cluster, the Calinski-Harabasz Index of 20083.489 shows good and compact inter-cluster separation, and the Dunn Index of 0.003 shows outliers that cause overlapping clusters and lack of clear separation. The evaluation results show that implementing the K-Means model with a value of k equal to 5 again emphasizes that the clustering results are good. The modelling results show an average topic coherence value of 0.0902 and produce five main topics in the 2024 election news on Detik.com topic 0: about presidential and vice presidential candidates (5,215 articles) with the representation of the words 'ganjar', 'prabowo', 'anies' and 'imin', topic 1: about general elections and related surveys (3,191 articles) with the representation of the words '2024', 'pemilu', 'pilpres' and 'suara', topic 2: news about Joko Widodo President (2,604 articles), topic 3: news about presidential and vice presidential debates (2046 articles) with representations of the words 'presiden', 'jokowi', 'demokrat' and 'politik' and topic 4: news about the figure Gibran Rakabuming Raka and related issues (1963 articles) with representations of the words 'raka', 'rakabuming', 'nomor' and 'urut'. Using the results of this research, readers can gain insights into the most discussed issues and the attention given to key figures in the 2024 election news on the detik.com news portal.

Text document clustering using mayfly optimization algorithm with k-means technique

Text clustering is a subfield of machine learning (ML) and natural language processing (NLP) that consists of grouping similar sentences or documents based on their content. However, insignificant features in the documents minimize the accuracy of information retrieval which makes it challenging for the clustering approach to efficiently cluster similar documents. In this research, the mayfly optimization algorithm (MOA) with a k-means approach is proposed for text document clustering (TDC) to effectively cluster similar documents. Initially, the data is obtained from Reuters-21678, 20-Newsgroup, and BBC sports datasets, and then pre-processing is established by stemming and stop word removal to remove unwanted phrases or words. The data imbalance approach is established using an adaptive synthetic sampling algorithm (ADASYN), then term frequency-inverse document frequency (TD-IDF) and WordNet features are employed for extracting features. Finally, MOA with the K-means technique is utilized for TDC. The proposed approach achieves better accuracy of 99.75%, 99.54%, and 98.24% when compared to the existing techniques like fuzzy rough set-based robust nearest neighbor-convolutional neural network (FRS-RNN-CNN), TopicStriker, Modsup-based frequent itemset, and rider optimization-based moth search algorithm (Modsup-Rn-MSA), hierarchical dirichlet-multinomial mixture, and multi-view clustering via consistent and specific non-negative matrix (MCCS).

AI-generated text detection and classification based on BERT deep learning algorithm

With the rapid development and wide application of deep learning technology, AI-generated text detection plays an increasingly important role in various fields. In this study, we developed an efficient AI-generated text detection model based on the BERT algorithm, which provides new ideas and methods for solving related problems. In the data preprocessing stage, a series of steps were taken to process the text, including operations such as converting to lowercase, word splitting, removing stop words, stemming extraction, removing digits, and eliminating redundant spaces, to ensure data quality and accuracy. By dividing the dataset into a training set and a test set in the ratio of 60% and 40%, and observing the changes in the accuracy and loss values during the training process, we found that the model performed well during the training process. The accuracy increases steadily from the initial 94.78% to 99.72%, while the loss value decreases from 0.261 to 0.021 and converges gradually, which indicates that the BERT model is able to detect AI-generated text with high accuracy and the prediction results are gradually approaching the real classification results. Further analysis of the results of the training and test sets reveals that in terms of loss value, the average loss of the training set is 0.0565, while the average loss of the test set is 0.0917, showing a slightly higher loss value. As for the accuracy, the average accuracy of the training set reaches 98.1%, while the average accuracy of the test set is 97.71%, which is not much different from each other, indicating that the model has good generalisation ability. In conclusion, the AI-generated text detection model based on the BERT algorithm proposed in this study shows high accuracy and stability in experiments, providing an effective solution for related fields. In the future, the model performance can be further optimised and its potential for application in a wider range of fields can be explored to promote the development and application of AI technology in the field of text detection.

Analysis of Vina Film Sentiment on Social Media X Using The Naïve Bayes Method

The increasingly rapid development of technology and information, one of which is the internet. Where users can share opinions and discuss various topics or problems around them, namely social media One of the news items that frequently appears as a trending topic on X is the Vina film controversy. However, with the large amount of review data available, it will be difficult to process manually. Therefore, sentiment analysis is needed to see whether people's tendencies toward the Vina film case are positive or negative. The stages carried out were data collection taken via web scrapping with an initial amount of data of 833 and processed through the preprocessing stage, including cleaning, case folding, normalization, stopword removal, tokenization, and stemming, the data became 830. The application of the Naïve Bayes algorithm in this research uses the probability method to classify and predict 664 training data and 166 test data, with the help of the Python library. The accuracy calculation results show quite good performance with TF-IDF weighting producing an accuracy of 78%, precision of 80%, and recall of 90%, f1-score of 84%. Analysis from this research shows that the dominance of negative sentiment is 517 while positive sentiment is 313. The amount and quality of training data play an important role in system quality, where high data quality provides better accuracy in predicting sentiment classes.

Analysis of Indonesian Netizen Sentiment on Platform X Regarding the Arrival of Refugees in Indonesia Using the Multinominal Naive Bayes Method

This research aims to analyze the sentiments of Indonesian netizens regarding the arrival of Rohingya refugees in Indonesia using the Multinomial Naive Bayes method. Sentiment analysis was carried out on comments obtained from platform X. The data collection technique used the crawling method to extract comments from platform X users regarding the issue of the arrival of Rohingya refugees. The tool used for crawling is Google Collab. The data analysis process includes sentiment labeling, data preprocessing (case folding, stopword removal, tokenizing, stemming), and classification using the Multinomial Naive Bayes method. The research results show that the majority of Indonesian netizens' sentiments regarding the arrival of Rohingya refugees in Indonesia are negative, with a percentage of 81%. Positive sentiment reached 8%, while neutral sentiment was 11%. The Multinomial Naive Bayes method produces an accuracy of 82.5% in classifying netizen sentiment. The tools used to process the data are the Orange Data Mining application version 3.36.2 It is hoped that this research can contribute to the development of computer science, especially in the fields of Text Mining, Natural Language Processing, Machine Learning and Artificial Intelligence (AI). It is also hoped that this research will provide benefits to parties related to handling the Rohingya refugee problem in Indonesia, such as the government, humanitarian organizations, mass media, academics, the general public, and other researchers.