Sentiment Analysis on Indonesia COVID-19 Pandemic based on 2020 Twitter Data

Sentiment analysis and stance detection are interrelated problems of affective computing, and their outputs commonly complement each other. The focus of this article is to determine sentiments and stances of Twitter users about vaccination. A tweet dataset on COVID-19 vaccination is compiled and jointly annotated with sentiment and stance. This deep learning approach employs BERT, which is a model based on pre-trained transformers. The generative deep learning model, ChatGPT, is also used for stance and sentiment analysis on the dataset. ChatGPT achieves the best performance for stance detection, while BERT is the best performer for sentiment analysis. This study is the first one to observe stance and sentiment detection performance of ChatGPT on health-related tweets. This article also includes a full-fledged system proposal based on automatic sentiment and stance analysis. COVID-19 pandemic is an impactful global public health phenomenon, and hence, joint extraction of sentiments and stances from health-related tweets can profoundly contribute to health-related decision-making processes.

The General Elections in India is a grand event with over 900 million eligible voters. India has a population of around 1.3 billion and it is the world’s largest democracy. One of the features of a democratic nation is the election. In election general sentiment and opinion of people plays an important role. Social media is abuzz with opinions and discussions taking place all over India especially twitter. We tried to predict election results and general mindset using sentiment and network analysis. Previously, some work has been done in the sphere of gauging the mindset of people using social media, specifically Sentiment Analysis of Twitter posts. Using sentiment analysis, we can classify a document into a host of categories like positive, negative, neutral, angry, happy, sad, etc. and this is especially helpful when we are trying to gauge the opinion of people based on what they wrote about it. We used Twitter data for our work and tried to apply sentiment analysis techniques to find the consensus of the public about different political leaders and political parties. The results were pretty accurate when compared to actual results and this model and analysis could prove useful in gauging the mindset of the general public about a specific topic, product, or idea. Furthermore, we also carry out a detailed network analysis of dataset using the network formed by taking tweets as the edges of the graph. This gave us some really interesting insights and resulted in the formation of 4 major communities confirming the fact that online communities are close-knitted when it comes to political orientations.

Social networking sites and micro blogs provide tremendous amount of real time data every day. Sentiment analysis or opinion mining aims to automate the process of sentiment extraction from the user content available online. Twitter in recent years due to its high subscriber rate and diverse audience, has become increasingly powerful in representing and changing user opinions over an object or event. This paper focuses on research conducted within the field of twitter sentiment analysis. The objective is to comprehensively investigate the task of sentiment analysis and its sub processes and identify the different tools, techniques or other resources used or applied on twitter data during the process. A Systematic Literature Review (SLR) has been conducted to identify 40 researches, relevant to sentiment identification and analysis. The work presented covers major tools and techniques used during sentiment mining process and maybe utilized by researchers or practitioners for identifying potential research directions as well as suggest possible software development areas that need to be explored.

This study examines public discourse on the COVID-19 pandemic using sentiment analysis, topic modeling, and geolocation analysis of Twitter data. This research aims to provide a multi-dimensional perspective on how different regions and demographics perceived and reacted to pandemic events. Through sentiment analysis, over 57,000 tweets were categorized as positive, neutral, or negative, revealing public emotional responses over the pandemic’s progression. Topic modeling identified key themes, including public health measures, vaccination sentiments, and the impact of misinformation. Geolocation analysis further enhanced these insights by mapping sentiment and topic distributions across various regions, highlighting regional differences in pandemic-related discourse. Most studies primarily focused on sentiment trends, and they often lacked integration with geographic data, which is essential for understanding regional differences in public reactions. The integration of these methods with geotagging (geolocation) provided a comprehensive, user-friendly approach to large-scale data analysis, marking its importance in analysis public opinions based on geographic location for nation decision-making especially in healthcare services for such research. Hence, this study used hybrid data mining approaches and enhanced geolocation analysis to address this problem. Findings from this study contribute to understanding public responses to health crises, offering insights beneficial to policymakers, health professionals, and researchers in managing future pandemics.

Objective: To make an extensive analysis of sentiment within the discourse surrounding COVID-19 vaccines on Twitter, employing Natural Language Processing (NLP) methodologies. Method: The research methodology encompasses data collection via the Twitter API, followed by sentiment analysis facilitated by the TextBlob library. Pre-processing stages are integrated to cleanse and standardize the Twitter data. Subsequently, sentiment analysis categorizes tweets into positive, negative, and neutral sentiments based on polarity scores. Findings: The findings, grounded in a dataset spanning from March 1, 2022, to April 30, 2022, comprising 61,934 tweets, unveil that 45.0% of tweets conveyed positive sentiment, 17.3% exhibited negativity, and 37.7% maintained neutrality. Moreover, an exploration of tweet subjectivity revealed that 70.1% of the content expressed subjectivity, while 29.9% conveyed objectivity. The research is augmented with visual representations, including word clouds and subjectivity-polarity graphs, that offer a more intuitive understanding of sentiment trends. Novelty: This study contributes to the expanding landscape of sentiment analysis and its application within the context of public health crises, empowering stakeholders with valuable knowledge to enhance vaccine acceptance and effectiveness. The tool used “Tweet Downloader” in data collection makes this study different from other reviewed studies. Keywords: COVID-19 vaccine, Twitter sentiment analysis, Public perception, Natural Language Processing (NLP), Social media data

In this paper, the focus is on sentiment analysis of web 3.0 enabled twitter dataset. The objective of the project is to explore various methods for performing sentiment analysis on Twitter datasets and implementing these methods on the web3.0 Twitter platform. The project involves collecting Twitter data through blockchain-based applications, preprocessing the data to remove noise, and applying machine learning models for sentiment analysis. Sentiment analysis is simply the extraction of thoughts, ideas, opinions, and emotions from sources such as text, speech, tweets, and databases using natural language processing (NLP) This process involves text segmentation mentally makes it "good," "bad," and "neutral" groups. In addition, it is known by other terms such as objective evaluation, mindfulness mining, and rating extraction. Web 3.0, also known as Web3, represents the third contemplated iteration of the World Wide Web, which aspires to establish a connected, transparent and intelligent online environment Based on the concept of decentralization, blockchain technology and the implementation of token-based economies. The main outcome of the project is to gain insights into the sentiment of users by analyzing WEB3.0 enabled Twitter data. By implementing sentiment analysis techniques on a WEB3.0 enabled Twitter dataset, the project aims to contribute to the field of sentiment analysis and showcase the effectiveness of using WEB3.0 enabled for data collection and analysis. The project aims to provide valuable insights for various methods of sentiment analysis for researchers. Keywords – Sentiment Analysis, Blockchain- Enabled, Twitter data, WEB 3.0, Machine Learning Models, User Sentiment, Research Contribution

Microbloging Twitter is a service that is widely used because of the need for rapid communication or cheaper than blogs, email, instant messaging or web. The growth of Twitter users has increased very rapidly in recent years. Thus the need for the utilization of Twitter either in the promotion of a product or the introduction of self-governance necessary for future leaders. These researches tried to calculate the popularity by calculating the value of user influence and sentiment. The research of sentiment on Indonesian text only focuses on sentiment classification. There has been no research on scoring or calculation of the sentiment value. The calculation of sentiment value is needed to determine the magnitude of a good or bad someone assessment by the value of a product or a person. Popularity analysis using Bayesian probability is to measure the value of the influence. Measurements of sentiment consist of 3 main parts such as value of verbs, adjectives, and adverbs in Indonesian language. In this research, analyze the value of influence and sentiment of someone using the Pearson correlation method. The negative correlation on President candidate is higher than positive correlation. The low sentiments value will have a greater impact to increase the influence value or vice versa. The accuracy of the sentiment on Bahasa Indonesia text is 73% It can be increased by improving the preprocessing process on Bahasa Indonesia. This research provides two contributions, namely calculating the value of sentiment on Bahasa Indonesia and analysis of sentiment and influence patterns of relationships.

On March 11, 2020, Dr. Tedros Adhanom Ghebreyesus, Director-General of the WHO, pronounced the outbreak a pandemic. The term “pandemic” refers to a disease that spreads rapidly and engulfs an entire geographic region. Coronavirus is a brand-new viral disease named after the year it first appeared. There is a scarcity of academic research on the subject to help researchers. Social media content analysis can reveal a lot concerning the general temperament and mood of the human race. In the field of sentiment analysis, deep learning models have been widely used. Sentiment analysis is a set of techniques, tools, and methods for detecting and extracting information. People have been using social networking sites like Twitter to voice their opinions, report realities, and provide a point of view on what is happening in the world today. Folks have always used Twitter to share data about the COVID-19 pandemic. People randomly share data visualizations from news revealed by organizations and the government. The numerous studies surveyed are selected based on a similarity. Every paper which is supervised performs sentiment analysis of Twitter data. Various studies have made used a fusion of diverse word embedding’s with either machine learning classifiers or deep learning classifiers. Albeit the interpretation of single classifiers is satisfactory, the studies those proposed hybrid models have shown outstanding performance. On top of that transformer based models demonstrated quality results. It is concluded that using hybrid classifiers on Twitter data for sentiment analysis can surpass the achievements of the single classifiers.KeywordsSentiment analysisNLPTwitter dataWord embeddingsMachine learning classifiersDeep learning classifiersHybrid classifiersTransformer models

Sentiment analysis mainly supports sorting out the polarity and provides valuable information with the use of raw data in social media platforms. Many fields like health, business, and security require real-time data analysis for instant decision-making situations.Since Twitter is considered a popular social media platform to collect data easily, this paper is considering data analysis methods of Twitter data, real-time Twitter data analysis based on geo-location. Twitter data classification and analysis can be done with the use of diverse algorithms and deciding the most appropriate algorithm for data analysis, can be accomplished by implementing and testing these diverse algorithms.This paper is discussing the major description of sentiment analysis, data collection methods, data pre-processing, feature extraction, and sentiment analysis methods related to Twitter data. Real-time data analysis arises as a major method of analyzing the data available online and the real-time Twitter data analysis process is described throughout this paper. Several methods of classifying the polarized Twitter data are discussed within the paper while depicting a proposed method of Twitter data analyzing algorithm. Location-based Twitter data analysis is another crucial aspect of sentiment analyses, that enables data sorting according to geo-location, and this paper describes the way of analyzing Twitter data based on geo-location. Further, a comparison about several sentiment analysis algorithms used by previous researchers has been reported and finally, a conclusion has been provided.

Sentiment analysis has gained much attention nowadays among the researchers especially during the Covid-19 pandemic. Due to the increasing volume of data coming from the social media platforms, researchers have been using sentiment analysis to analyse topics regarding commercial products, daily issues among the society and also to detect important events from the community. Since the social media users are consisting of the community, content that are shared could also be used to detect possible situational hazard such as the outbreak of Covid-19 in advanced. The result from the sentiment analysis could be beneficial to government organizations in order to contain the outbreaks and public health crisis related to Covid-19. The objective of this research is to explore Naive Bayes algorithm for the sentiment analysis on the Covid-19 outbreak awareness based on Twitter data. In this research, the data were collected during the Malaysia's second lock down, which was between the months of April to June 2021 using the Twitter API Tweepy. After the pre-processing and feature extraction stages, the data have been divided into the training and testing dataset for the Naive Bayes sentiment classification. The result has shown that Naive Bayes has been able to generate high performance with more than 90% accuracy for this classification problem. Future work would include the improvement of data preprocessing, more balance of dataset, enhancement of the algorithm and also comparing the performance with other well-known classification algorithms.

The growth and development of social networks, blogs, forums, and e-commerce websites has produced a number of data, notably textual data, which has increased tremendously. Twitter is one of the most popular media social platforms; during the COVID-19 pandemic, people all around the world use social media to share their opinions or concerns about the pandemic that has changed their lives. It revealed a significant rise in tweets on coronavirus, including positive, negative, and neutral tweets about the virus's impact. Sentiment analysis faces challenges: sparse data limits understanding, while topic coherence and interpretability demand improvement for clearer insights. The primary goal of this paper is to improve the accuracy and effectiveness of sentiment analysis during the COVID-19 pandemic through the application of advanced techniques and classifiers. In this article, we experiment with such Support Vector Machines (SVM) and Naive Bayes (NB) on Twitter data for high-accuracy machine learning models. Using Latent Dirichlet Allocation (LDA)for feature extraction, we aim to capture comprehensive aspects and topics for sentiment analysis. Additionally, we explore Count Vectorizer and Term Frequency - Inverse Document Frequency (TF-IDF) as word embedding techniques. The main objectives are to extract topics, understand public concerns about Covid-19, and compare classifier performance in Aspect-Based Sentiment Analysis on Covid-19 tweets. This paper introduces advanced sentiment analysis techniques, such as LDA, Count Vectorizer, and SVM, enhancing nuanced sentiment analysis during the COVID-19 pandemic with notable 85% accuracy in SVM classification.

SummaryIn this manuscript, a deep neural network is proposed by integrating improved adaptive‐network‐based fuzzy inference system (IANFIS) for branding online products to overcome these issues. Here, the sentiment analysis (SA) and prediction on future branding of products that are extracted from the twitter data is carried out. After the review process classifying the products as positive, negative, and neutral assessments completely concentrated in three folds, prediction of a future brand that is carried out by IANFIS for weighting the products finally classify them. This scheme helps the respective retailers/retail brands with their digital marketing team to understand their brand perception as opposed to others. The performance of the proposed method is compared with the existing methods, such as sentiment analysis on twitter data based on particle swarm optimization and genetic algorithm, sentiment analysis on twitter data based on particle swarm optimization and convolutional neural network, sentiment analysis on twitter data based on whale optimization algorithm and support vector machine, and sentiment analysis on twitter data based on convolutional neural network and long short term memory. The simulation results show that the proposed method outperforms the state of art methods.

Multilingual sentiment analysis poses significant challenges, especially in the context of languages with low resources. The study proposes a hybrid deep learning model based on the CNN-BiLSTM architecture to classify sentiment in multiple languages, including those with limited corpus and lexical resources. This model integrates the multilingual text representation of mBERT embedding with CNN's ability to extract local features and BiLSTM's power in capturing sequential contexts. Experiments were conducted on datasets that included various languages such as Indonesian, Hausa, Swahili, and Yoruba. The results of the evaluation showed that the proposed model achieved an accuracy of 84.3% and a macro F1 score of 83.1%, outperforming basic models such as Naive Bayes and independent BiLSTM. These findings suggest that the hybrid approach is effective in improving sentiment analysis performance across languages and has promising potential for real-world multilingual applications [1] T. I. Jain and D. Nemade, “Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis,” Int. J. Comput. Appl., vol. 7, no. 5, pp. 12–21, 2010, doi: 10.5120/1160-1453. [2] A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining,” Proc. 7th Int. Conf. Lang. Resour. Eval. Lr. 2010, pp. 1320–1326, 2010, doi: 10.17148/ijarcce.2016.51274. [3] I. Iin, R. Supriatna, M. Mulyawan, and D. Rohman, "The Application of Natural Language Processing in the Sentiment Analysis of the 2024 Vice Presidential Candidate Using the Naive Bayes Algorithm," JATI (Journal of Mhs. Tek. Inform., vol. 8, no. 1, pp. 1109–1115, 2024, doi: 10.36040/jati.v8i1.8572. [4] B. Ramadhani and R. R. Suryono, "Comparison of Naïve Bayes Algorithms and Logistic Regression for Metaverse Sentiment Analysis," J. Media Inform. Budidarma, vol. 8, no. 2, p. 714, 2024, doi: 10.30865/mib.v8i2.7458. [5] F. F. Mailoa, "Sentiment analysis of twitter data using text mining method on obesity problems in Indonesia," J. Inf. Syst. Public Heal., vol. 6, no. 1, p. 44, 2021, doi: 10.22146/jisph.44455. [6] E. Lutfina, W. Andriana, S. Quamila, P. Wiratmaja, and E. Febrianti, "Science, Technology and Management Journal Methods and Algorithms in Sentiment Analysis: Systematic Literature Review Info Articles," vol. 4, no. 2, pp. 67–79, 2024, [Online]. Available: http://journal.unkartur.ac.id/index.php/stmj [7] A. Fauzi, M. F. Akbar, and Y. F. A. Asmawan, "Sentiment of Internet Analysis on Social Media Using Bayes Algorithm," J. Inform., vol. 6, no. 1, pp. 77–83, 2019, doi: 10.31311/ji.v6i1.5437. [8] D. Winoto, V. Desta Aditia, C. Sorisa, R. Priskila, and V. Handrianus Pranatawijaya, "Sentiment Analysis on User Reviews of Duolingo Language Learning Application: Using Naïve Bayes and K-Nearest Neighbor Algorithms," JATI (Journal of Mhs. Tek. Inform., vol. 8, no. 3, pp. 3230–3236, 2024, doi: 10.36040/jati.v8i3.9647. [9] T. Y. Pahtoni and H. Jati, "Analysis of Twitter Data Sentiment Related to ChatGPT Using Orange Data Mining," J. Techno. Inf. and Computing Science., vol. 11, no. 2, pp. 329–336, 2024, doi: 10.25126/jtiik.20241127276. [10] A. Ardiansyah, E. Argarini Pratama, N. Imam Fadlilah, and U. Bina Sarana Informatika, "Analysis of User Sentiment Towards the ChatGPT Application on the Google Play Store: The Application of the Support Vector Machine Algorithm," vol. 11, no. 2, pp. 247–254, 2024. [11] Normah, B. Rifai, S. Vambudi, and R. Maulana, "Sentiment Analysis of Vtuber Development Using SMOTE-Based Support Vector Machine Method," J. Tek. Computer. AMIK BSI, vol. 8, no. 2, pp. 174–180, 2022, doi: 10.31294/jtk.v4i2. [12] S. F. Intan, I. Permana, F. N. Salisah, M. Afdal, and F. Muttakin, "Comparison of KNN, NBC, and SVM Algorithms: Analysis of Public Sentiment Towards Parking in the City of Pekanbaru," JUSIFO (Journal of Sist. Information), vol. 9, no. 2, pp. 85–96, 2023, doi: 10.19109/jusifo.v9i2.21357. [13] M. A. Maulana, A. Setyanto, and M. P. Kurniawan, "Analysis of Social Media Sentiment at Amikom University Yogyakarta as a Means of Information Dissemination Using the Svm Classification Algorithm," Sem. Nas. Technology. Inf. and Multimed. 2018 Univ. AMIKOM Yogyakarta, 10 February 2018Pp. 7–12, 2018. [14] D. A. Efraim, "Sentiment Analysis on Instagram Social Media Using Naive Bayes Algorithm (Case Study: Indonesian Futsal National Team)," no. April 2012, pp. 498–509, 2023. [15] I. Maulana, W. Apriandari, and A. Pambudi, "Aspect-Based Sentiment Analysis of Mypertamina Application Reviews Using Support Vector Machine," IDEALIS Indones. J. Inf. Syst., vol. 6, no. 2, pp. 172–181, 2023, doi: 10.36080/idealis.v6i2.3022. [16] N. D. Putranti and E. Winarko, "Twitter Sentiment Analysis for Indonesian Text with Maximum Entropy and Support Vector Machine," IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 8, no. 1, p. 91, 2014, doi: 10.22146/ijccs.3499. [17] R. Maulana, A. Voutama, and T. Ridwan, "Sentiment Analysis of MyPertamina Application Reviews on Google Play Store using NBC Algorithm," J. Techno. Integrated, vol. 9, no. 1, pp. 42–48, 2023, doi: 10.54914/jtt.v9i1.609. [18] M. Y. Pratama, U. A. Putri, P. A. D. Angraini, D. Puspita, and F. Kurniawan, "Sentiment Analysis of Chat GPT as the Future of Workers on Youtube Social Media using the Naive Bayes Classification Algorithm," Explore. J. Sist. Inf. and Telemat., vol. 14, no. 2, p. 193, 2023, doi: 10.36448/jsit.v14i2.3391.

Public health surveillance has gained more importance recently due the global COVID-19 pandemic. It is important to track public opinions and positions on social media automatically, so that this information can be used to improve public health. Sentiment analysis and stance detection are two social media analysis methods that can be applied to health-related social media posts for this purpose. In this chapter, the authors perform sentiment analysis and stance detection in Turkish tweets about COVID-19 vaccination. A sentiment- and stance-annotated Turkish tweet dataset about COVID-19 vaccination is created. Different machine learning approaches (SVM and Random Forest) are applied on this dataset, and the results are compared. Widespread COVID-19 vaccination is claimed to be useful in order to cope with this pandemic. Therefore, results of automatic sentiment and stance analysis on Twitter posts on COVID-19 vaccination can help public health professionals during their decision-making processes.

Omicron variant has been massively reported on Indonesian mass media following the spread of other previous variants during Covid-19 pandemic. This research combines computer science and linguistics to analyze the news on the variant. It implemented quantitative research using computational algorithm by collecting the titles of the news from Indonesian mainstream online mass media. Sentiment Analysis (SA) was applied to obtain the sentiments, opinion, and subjectivities of the texts along with topic modeling in classifying the topics. The words in the headline news titles were used as the data and grabbed by Python programming language. A criterion-based sampling was employed in to select the relevant data and to formulate the criteria in the research methodology. The results were filtered to ‘Omicron’ keyword for SA processing by the Azure Text Sentiment Analysis tool. The results of SA, as computational research, was then confirmed with Attitude Analysis (AA) from the perspective of Systemic Functional Linguistics. AA classified the words into affect, judgment, and appreciation as the attitude construed in English text. This research provides SA as the insights of Omicron issue. The presence of AA extracts the words into bipolar senses of human’s meaning interpretation. AA is important to straighten SA findings. SA has contextual meaning problem and requires study on its words classified in ‘neutral’ which are then confidently directed into positive or negative meanings by AA. It is found that there are different dynamics by SA and AA findings as they reflect particular meanings. Besides their difference, SA is useful for managing overload data into fast policy making whereas AA makes sure the acceptable meanings to people. In this case AA corrects the bias occurring from SA.