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

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.

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

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.

<sec> <title>BACKGROUND</title> The COVID-19 pandemic caused many changes in primary health care systems in Europe. The fast adoption of telemedicine, the shift of health care resources to COVID-19–related tasks, and the tendency of patients to cancel their nonurgent appointments are some examples of these changes. Patient satisfaction is an important outcome of health care services, and the changes caused by COVID-19 in the system may have affected it. Google Maps reviews provide an important channel for patients to communicate about their experiences regarding the primary health care system. </sec> <sec> <title>OBJECTIVE</title> Drawing from research on social media data analytics and text mining, this study set out to investigate the changes in public sentiment regarding primary health care in Finland and Andalusia (Spain) before, during, and after the COVID-19 pandemic. </sec> <sec> <title>METHODS</title> We collected 55,043 Google Maps reviews from primary health care locations in Finland and Andalusia from January 1, 2013, to May 15, 2024. There are 604 primary health care locations in Finland and 1016 in Andalusia. The total number of Google Maps reviews collected was 12,247 for Finland and 42,796 for Andalusia. First, lexicon-based sentiment analysis using the open-source software AFINN was conducted for the Finnish- and Spanish-language datasets. Thereafter, transformer-based deep learning models for sentiment analysis were applied for both languages. The numeric user ratings and the results of the sentiment analysis were then analyzed. In addition, we conducted a word frequency analysis of the reviews. </sec> <sec> <title>RESULTS</title> There were important changes in the ratings and sentiment in the data for Andalusia. The ratings shifted from median 4 (IQR 3) before the COVID-19 pandemic to median 1 (IQR 2) during and median 1 (IQR 3) after the COVID-19 pandemic, on a scale from 1 to 5. The median of the sentiment values of the review texts shifted from neutral before the COVID-19 pandemic to –2 (IQR 0.055) or –1 (IQR 1) during and after the COVID-19 pandemic, depending on which sentiment analysis method was used. Interestingly, changes in numeric ratings and sentiment of the review texts in Finland were only minor, and the median values were the same during all 3 periods. Lexical analysis revealed changes in word frequencies across the periods, reflecting shifts in primary health care experiences during the pandemic, especially among the Spanish-language reviews. </sec> <sec> <title>CONCLUSIONS</title> The change toward a more negative public discussion on primary health care in Andalusia during the COVID-19 pandemic was considerable. This can be observed both in the numeric user ratings and in the sentiment analysis of the review texts. However, the data for Finland show that the public discourse stayed mostly neutral or slightly positive. The findings have implications on the quality management procedures in primary health care and on the use of user-generated content as an additional information source. </sec>

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.

Today, sentiment analysis is in high use to understand user reactions. In this paper, the authors have discussed this topic using news and Twitter texts as sources of data. They use TextBlob, VADER, and IBM Watson NLU as sentiment analysis tools. The news sentiment analysis data is from January to July 2020, classified under each tool. The authors get almost the same result from all of them. February shows having the maximum negative polarity news, followed by June. While Twitter data of each month when classified under each sentiment analysis tool shows the same kind of result for all the months, March has the maximum negative polarity and maximum positive polarity is seen in January. The aim of this paper is to show that sentiment analysis on newspaper content can help common people to know the bias in newspapers to prevent more negative impact on readers especially during a pandemic like COVID-19. The comparison drawn between the news data sentiment analysis and the same with Twitter data has a good correlation but still shows a difference in sentiment.

A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data

This paper explores the role of social media in tourist sentiment analysis. To do this, it describes previous studies that have carried out tourist sentiment analysis using social media data, before analyzing changes in tourists’ sentiments and behaviors during the COVID-19 pandemic. In the case study, which focuses on Andalusia, the changes experienced by the tourism sector in the southern Spanish region as a result of the COVID-19 pandemic are assessed using the Andalusian Tourism Situation Survey (ECTA). This information is then compared with data obtained from a sentiment analysis based on the social network Twitter. On the basis of this comparative analysis, the paper concludes that it is possible to identify and classify tourists’ perceptions using sentiment analysis on a mass scale with the help of statistical software (RStudio and Knime). The sentiment analysis using Twitter data correlates with and is supplemented by information from the ECTA survey, with both analyses showing that tourists placed greater value on safety and preferred to travel individually to nearby, less crowded destinations since the pandemic began. Of the two analytical tools, sentiment analysis can be carried out on social media on a continuous basis and offers cost savings.

Public engagement is a cornerstone of effective waste management, yet regional attitudes toward environmental practices remain underexplored. Social media provides critical insights into evolving public discourse, particularly for sustainability initiatives where community perception drives policy success. This research examines pro-environmental behavior through sentiment analysis of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> (Twitter) data, focusing on waste management discussions in Spain and Macedonia from May 2024 to October 2024. We employ BERT-based models Multilingual BERT and BERTweet to analyze sentiment and perform topic modeling using BERTopic. For Spanish tweets, native-language analysis with Multilingual BERT outperforms BERTweet when tested on translated data, revealing a predominantly positive sentiment (80%). In contrast, Macedonian tweets, translated into English and evaluated on both models, show consistent performance but reflect an opposite trend, with 70% of tweets expressing negative sentiment. Sentiment analysis categorizes tweets into positive, neutral, and negative classes, enabling a comparative evaluation of model effectiveness across translations and languages. Additionally, BERTopic identifies prominent themes across the dataset and within sentiment categories, offering insights into public attitudes and discussions. These findings reveal regional differences in waste management perceptions and their importance for sentiment and topic analysis, aiding environmental communication insights.

The sentiment analysis process that gives this work its name is the main theme of the work. Since the beginning of 2000, sentiment analysis has become one of the most active research areas by researchers working on natural language processing and social networking analysis. In addition, data mining, web mining, and text mining are also studied extensively. Moreover, the method of sentiment analysis has spread over many fields, from computer science to management science, from social science to economics, due to the importance given to the business world as a whole and the collectivity. In this study, Konstanz Information Miner (KNIME), which is a powerful data mining tool with its richest features and many visualization tools, was used on twitter data. Ten thousand Twitter data were used in this study. The sentiment analysis study, which is in fact a classification study, was conducted using machine learning algorithms on Twitter data. The results of the study were interpreted by carrying out an accuracy analysis. It is anticipated that the use of the KNIME, which has rich visualization tools, will be widespread in sentiment analysis studies to make these works both easier and more reliable.

In these days people tend to check reviews and ratings of video games before spending money and time for a game. This paper proposes a new model of sentiment analysis for video game’s reviews. In the proposed model, video game’s rating will be generated by doing sentiment analysis on public opinion data from Micro-blog site, Twitter. To segregate users’ sentiment Naive Bayes, Support Vector Machine, Logistic Regression and Stochastic Gradient Descent machine learning algorithms were used. This algorithms themselves were trained and tested on the Amazon game review dataset before doing sentiment analysis on Twitter Data. Furthermore, customized classifiers model was implemented which acted as voting classifiers to determine contextual polarity. This voting classifier takes results of other algorithms into account and select the best one which can obtain the most number of votes. Before implementing the classifiers, data pre pro-processing had been performed for accurate sentiment analysis. This process ensures higher accuracy for generating review ratings and distinguishing users’ sentiment. Finally, algorithm’s accuracy results are analyzed in vivid details. Analysis showed that our proposed technique outperforms others.

Objectives: This study aims to prepare a corpus and explore sentiment analysis in the Amharic language, which is increasingly used due to the growth of both the language and the Internet. Methods: The study acquired 23,646 Amharic tweets from Twitter using the Twitter API, cleaned and normalized the text through preprocessing, and manually annotated the data as positive, negative, or neutral by three annotators. The study utilized a multi-scale sentiment analysis approach to experimentally evaluate the classifier\'s performance and compare different ML and DL classifiers. Findings: The study found that sentiment analysis in the Amharic language in this dataset showed that the KNN classifier could classify texts with an accuracy of 76% and 90% accuracy using the CNN deep learning classifier. Novelty: This study contributes to the field of sentiment analysis by addressing the scarcity of an Amharic-language dataset specifically tailored for sentiment analysis purposes. Our approach involves filling this critical research gap by developing a new dataset. Subsequently, we employ machine learning and deep learning classifiers to assess the viability of this dataset for performing multi-class sentiment analysis tasks in the Amharic language. Keywords: Amharic, Sentiment Analysis, Multi­class, Machine Learning, Deep Learning Classifier

Amid the ever-growing social media content, sentiment analysis is a technique that is necessary for analyzing public opinion. This paper aims to discover what sentiment prevailed and what reaction took place preceding the election. This paper also presents the results of a study that applied sentiment analysis to Twitter data relating to the 15th General Elections of Malaysia. This research reviews several sentiment analysis techniques based on Twitter data. Using the method of analyzing 1566 tweets, including re-tweets and replies, gathered between November 12th and November 18th, 2022, the findings give us an understanding of the level of emotions that were tweeted by the users of Twitter towards the 15th general election in Malaysia. The results indicate that the sentiments expressed in the analysed tweets are distributed as follows: slightly positive (41%), positive (31%), neutral (24%), and slightly negative (4%).

The computational process of identifying and categorizing opinions that are expressed in the piece of text could be employed to determine information insight and the writer's opinion toward a particular topic. Most sentiment analysis employed for English text. Contrarily, a plethora method for sentiment analysis has been reported that the task stayed an interesting question for Indonesian text. The invention of machine learning models and broad accessibility of Twitter data on previous years have derived many researchers to take a machine learning model to resolve the sentiment analysis problem. The objective of this study is to build a sentiment analysis model using Sentiwordnet and machine learning for Indonesia general election opinion in Indonesian text from the twitter content. The data of the tweet was taken namely, the username, and the tweet itself. The theme of the tweet was the topic related to the 2019 general election figures, namely Joko Widodo and Prabowo Subianto. The period of data collection was November 13, 2018, to January 11, 2019, during the campaign period. The tweet was in Indonesia language. The result revealed sentiment analysis with the Naïve Bayes classification method showed 74.94% accuracy for Joko Widodo topic, while 71.37% accuracy for Prabowo topic.