Sentimen Analysis Social Media for Disaster using Naïve Bayes and IndoBERT

The rapid advancement of information and communication technology has resulted in a significant surge in data, especially text data from social media platforms. This paper presents a sentiment analysis approach using IndoBERT and Naïve Bayes algorithms to classify sentiment related to natural disasters, specifically from a dataset of tweets derived from social media platform X. The focus of this research is to categorize tweets as positive and negative sentiment to provide useful insights in improving disaster response and management, with a focus on tweets related to earthquakes, floods, and the eruption of Mount Merapi. The goal is to assist the government in allocating aid more efficiently and understanding public sentiment during disasters. The methodology used includes data collection, data preparation, labeling, categorization, word weighting using tf-idf, data separation, and classification using Naïve Bayes and IndoBERT algorithms. The results showed that IndoBERT achieved 91% accuracy, while Naïve Bayes achieved 74% accuracy. The study highlights the potential of sentiment analysis in improving disaster preparedness and more effective response strategies

Analyzing Public Sentiments on Disaster Relief Efforts Through Social Media Data

Social media has become a source of quick but not necessarily accurate information. Especially in social media X, which is often used to share information. This research aims to conduct sentiment analysis on posts related to natural disasters that aim to maximize assistance to victims of natural disasters. This research takes datasets from tweets on social media X, the data will be labeled into positive and negative. And then the preprocessing process will be carried out, in this study, categorization will be carried out on each tweet related to the category, then the data will be divided into training and testing. Then the Term Frequency-Inverse Document Frequency (TF-IDF) feature is used to assist in reducing the weight of words that often appear in the dataset, The next step involves designing a system with a focus on applying the Support Vector Machine (SVM) Polynomial Kernel algorithm which becomes a classifier which will later be used to find the best hyperline or decision boundary that divides each review into two classes, namely positive tweets and negative tweets. Then obtained with a value of Precision of 86.49%, Recall 99.21%, F1-Score 92.42%, and Accuracy of 87.01%. This research is expected to provide involvement in making a fast and effective decision for victims of natural disasters

Jaringan Komunitas Berbasis Similaritas Topik Bahasan dan Emosi untuk Mengidentifikasi Perilaku Pengguna Twitter

Pandemi COVID-19 menyebabkan situasi krisis yang berdampak pada perubahan perilaku pengguna Twitter terkait pengalaman distres publik. Perubahan perilaku positif bisa berdampak positif. Namun, perubahan perilaku negatif bisa menjadi masalah jika terjadi secara masif, seperti meningkatnya kecemasan pengguna. Oleh karena itu, mengeksplorasi hubungan antara perilaku dan jaringan komunitas pengguna sangat penting untuk menemukan implikasi pandemi COVID-19 terhadap perubahan perilaku pengguna Twitter. Penelitian ini berkontribusi dalam mengidentifikasi perubahan perilaku pengguna berdasarkan model ekstraksi perilaku kolektif pada aktivitas tweet temporal. Mekanisme ini menggunakan topik bahasan dan emosi sebagai variabel ekstraksi untuk menghasilkan jaringan perilaku pengguna. Kemudian, jaringan perilaku tersebut dimodelkan dengan algoritma DeepWalk Network Embeddings untuk memetakan hubungan kedekatan perilaku antar pengguna dan Density Peak Clustering Algorithm untuk mengelompokkan komunitas pengguna berdasarkan kesamaan perilaku yang kuat. Dari analisis 121 pengguna aktif, periode sebelum COVID-19 memiliki 98 pengguna representatif yang didominasi oleh 33% perilaku komunitas terkait aktivitas pribadi dengan emosi senang. Di sisi lain, periode setelah COVID-19 memiliki 54 pengguna representatif yang didominasi oleh 65% perilaku komunitas terkait kesehatan dengan emosi marah. Perubahan perilaku kedua periode tersebut dipengaruhi oleh transisi pola jaringan terdistribusi ke pola jaringan clique graph, sehingga sentralisasi penyebaran informasi mempengaruhi potensi peningkatan perubahan perilaku pengguna pada jaringan komunitas. Hasil ini dapat digunakan untuk mengurangi potensi penyebaran perilaku negatif dengan memanfaatkan komunitas yang memiliki pengaruh perilaku positif dikalangan pengguna Twitter. AbstractThe COVID-19 pandemic caused a crisis that impacted behavior changes of Twitter users related to public distress experiences. Positive behavior changes could have a positive impact. However, negative behavior changes could have problems if it occur massively, such as increased user anxiety. Therefore, exploring the relationship between behavior and user community in the social networks is very important to find the implication of the COVID-19 pandemic on behavior changes of Twitter users. This study contributes to identify user behavior changes based on the collective behavior extraction model on temporal tweet activities. This mechanism used discussion topics and emotions as extraction variables to generate user behavior network. Then, the behavioral network was modeled by the DeepWalk Network Embeddings algorithm to map the behavioral closeness relationship between users and the Density Peak Clustering Algorithm to group user communities with strong behavioral similarities. Based on the analysis of 121 active users, before the COVID-19 period had 98 representative users, who were dominated by 33% of community behavior related to personal activities with happy emotions. On the other hand, after the COVID-19 period, 54 representative users were dominated by 65% of community behavior related to health with anger. Behavior changes in both periods are influenced by the transition from a distributed network pattern to a clique graph network pattern, so the centralization of information dissemination could affect the potential for increasing user behavioral changes in the community network. These findings could be used to reduce the potential for spreading negative behavior by leveraging communities with positive behavior influence among Twitter users

Genetic Algorithm and GloVe for Information Credibility Detection Using Recurrent Neural Networks on Social Media Twitter (X)

Social media, especially X, has become a key source of information for many individuals, but the level of trust in the information spread on these platforms is a critical issue. To overcome this problem, this research proposed an information credibility detection system using a Recurrent Neural Network (RNN) with the utilization of TF-IDF feature extraction, GloVe feature expansion, BERT word embedding, and Genetic Algorithm (GA) optimization. This research contributes to assessing the credibility of tweets related to the 2024 Indonesian election by integrating TF-IDF to identify important words, GloVe to enhance word context, BERT for deeper understanding, and GA is present to optimize RNN performance. The main focus is to provide maximum accuracy by integrating these methods. In this research, the dataset used consists of 54,766 tweets relating to the 2024 Indonesia election and includes relatively equal numbers of credible and non-credible labels. The corpus construction utilized source X with a total of 40,466 data, IndoNews with a total of 131,580, and a combination of both with a total of 150,943. This research conducted six experimental scenarios, namely optimal data split, max features, N-grams, Top-N rank similarity corpus, BERT and GA application. Through these scenarios, the model achieved a significant accuracy improvement of 1.81% over the baseline, reaching an accuracy of 90.60%. This result demonstrates the effectiveness of the proposed system by presenting a higher quality of accuracy compared to the baseline model. Moreover, this research underscores the significant contribution of increasing the accuracy of information credibility detection