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With the advent and rapid advancement of text mining technology, a computer-based approach used to capture sentiment standpoints from data in textual form is increasingly becoming a promising field. Detailed information about sentiment can be provided using aspect-based sentiment analysis, which can be used in better decision-making. This study aims to study, observe, and classify previous methods used in aspect-based sentiment analysis. A systematic review is adopted as the method used to collect and review papers to achieve this research's aim. Papers focused on sentiment analysis, aspect extraction, and aspect aggregation from different academic databases such as Scopus, ScienceDirect, IEEE Explore, and Web of Science were gathered based on the inclusion and exclusion criteria of the study. The gathered papers were further reviewed to answer the stated research questions. The findings from the research show the most used methods for aspect extraction, sentiment analysis, and aspect aggregation in aspect-based sentiment analysis. This research offers a robust synthesis of evidence to guide further academic exploration in sentiment analysis.

This study explores the role of sentiment analysis as a predictive tool for understanding and forecasting product launch success in the digital market. Sentiment analysis involves the classification of consumer sentiment expressed on social media platforms such as Twitter and Instagram, and it can significantly impact businesses by predicting consumer behavior and product performance. The research highlights the relationship between social media sentiment and product success, demonstrating that positive sentiment is strongly correlated with higher sales and consumer engagement, while negative sentiment can lead to declines. Machine learning models, including Support Vector Machines (SVM) and Random Forest, were employed to classify sentiment from large volumes of social media data and correlate it with product performance indicators such as sales volume and consumer interaction. The study found that sentiment analysis models were highly effective in predicting product success, with positive sentiment generally driving product profitability and negative sentiment posing a potential threat to brand reputation. Moreover, the analysis showed that social media sentiment provides real-time insights into consumer perceptions, enabling businesses to quickly adjust marketing strategies and product development plans. These findings underscore the importance of integrating sentiment analysis into product launch evaluations and strategic decision-making. Future research should explore the integration of sentiment analysis with other predictive market models and investigate the effects of fake reviews and post-purchase consumer behaviors on product success.

In facing the increasing awareness of environmental impact, electric vehicles have become a primary focus in the global automotive industry. With the advancement of technology and the growing need for eco-friendly solutions, the evaluation of public sentiment towards electric vehicles becomes highly relevant. This research aims to analyze opinions expressed on Twitter regarding the use of electric vehicles using the Long Short Term Memory (LSTM) classification method. Utilizing a dataset of 30,000 entries, this study applies the LSTM algorithm to classify sentiment in tweets. Four different scenarios are tested, involving combinations of Continuous Bag of Words (CBOW) and Skip-Gram feature extraction methods, as well as data split percentages of 80:20 and 70:30. The research results demonstrate high accuracy levels across all scenarios, ranging from 85.16% to 85.9%. These findings indicate the effectiveness of sentiment analysis in gauging public perspectives on the use of electric vehicles. This study makes a significant contribution to understanding public sentiment related to electric vehicles based on Twitter data while highlighting the application of sentiment analysis techniques in the context of electric vehicle usage.

The global economic recession is a global economic downturn that affects the domestic economies of countries in the world. The stronger the economic dependence of one country on the global economy, the faster a recession will occur in that country. In 2020 the country of Indonesia and even the world are exposed to the COVID-19 virus which has an impact on the country's economic growth, even the world economy. This is the trigger for an economic recession. This has led to many different public perspectives on the occurrence of a global economic recession whose opinions or reactions are expressed on social media Youtube. The data was obtained by crawling techniques from social media Youtube with a total of 500 comments used. The data is then labeled (class) with a lexicon-based method with an Indonesian language dictionary. From the labeling results, it was obtained 185 positive labeled data (37%) and 315 negative opinions (63%). The data preprocessing stage is carried out in preparation for the data to be processed for sentiment analysis. Of the many opinions obtained, an analysis of public sentiment regarding the 2023 global economic recession will be carried out using the Naïve Bayes classification algorithm. This study also applied the TF-IDF word weighting method with the n-gram feature used, namely bigram (n=1). The system will be evaluated using a confusion matrix. The implementation results show a prediction model with a total of 500 opinion data with a comparison of training data and test data of 9:1, producing an accuracy value of 84.00%, a precision value of 75.00%, a recall of 30.00%, and an f1-score of 42.86%. The performance of the system model built in this study can be said to be good.

Digital Population Identity (IKD) is a digital-based location data innovation through a mobile application with a photo or QR Code. The government's objective is to reduce the physical prints of KTP as well as the use of blank KTP-el in the hope of administrative efficiency. ICT is integrated with health services, education, banking, and taxation, facilitating public access in the era of technological development. However, in remote areas, limited internet access and minimal socialization raise concerns about the security of digital identity data being considered. Social media, especially YouTube, is a channel platform used by the public to convey opinions, opinions and comments about ICTs. So that's why sentimental analysis is needed using the Naive Bayes algorithm to help understand public opinion. The tests were conducted using Orange on 1,561 data showing accuracy, precision, recall, and F1 above 90%. The results of this analysis can serve as a guide for staff in interacting with the community for the implementation of Digital KTP through IKD, as well as improving services regarding the applications provided.

Early childhood plays an important role in forming the basis of development, which involves stimulation of various aspects such as moral religious values, social emotional, language, cognitive, and physical motor skills. The concept of early childhood learning is focused on play, where every activity is designed to be play, so that learning becomes more effective. Parents also need to understand today's children's education to interact with children positively. This research focuses on sentiment analysis of children's education-based app reviews on the Google Play Store, using Random Forest and Logistic Regression methods. The review data is taken from three apps with the theme of child development, namely "About Kids", "PrimaKu", and "Teman Bumil", with a range of review years between 2018 and 2023. The test results show that Logistic Regression has higher accuracy compared to Random Forest, especially in the "About Kids" and "PrimaKu" applications with accuracy above 90%. The conclusion of this research highlights the importance of sentiment analysis in improving understanding of user responses to children's education applications, with suggestions for future research to increase the number of datasets and variations in testing schemes by tuning hyperparameters to improve prediction accuracy and more optimal results.

The conflict between Israel and Palestine has been a prominent topic of international discourse for several decades. This dispute spans a century, commencing in 1917 and persisting to the present day. This research delves into sentiment analysis of the Indonesian community concerning the Israel-Palestine conflict through the Twitter social media platform, with a specific focus on boycotting Israeli products. Utilizing Orange and Naive Bayes classification, the study analyzes over 300 datasets of tweets acquired through the scraping process. The objective is to comprehend the nuances, trends, and variations in sentiment among Twitter users regarding the issue of boycotting Israeli products. The results reveal that the majority of the population tends to support the boycott, with a Naive Bayes classification accuracy of 95%, Precission of 96%, Recall of 95%, and F1 Score of 95%. The data preprocessing process, encompassing transformation, tokenization, and filtering, effectively eliminates noise and prepares the data for a more in-depth sentiment analysis.

The significant changes in daily life patterns, driven by technological advancements, particularly in the transportation sector, are evident through the emergence of on-demand services such as Gojek. This research aims to explore users' perspectives and opinions regarding service quality, focusing on aspects like driver behavior, responsiveness, and reliability within the Gojek platform. The Naive Bayes method is employed to analyze user sentiments toward the driver services, supported by the Orange software to comprehend the complex patterns in user reviews. Evaluation is conducted on reviews from the Play Store, resulting in an accuracy of 87.4%, F1 score of 87.6%, precision of 87.9%, and recall of 87.4%. These findings indicate the success of the model in identifying and predicting predefined variables. Through the combination of methods and software, the study concludes that sentiment analysis of Gojek's driver services can be performed efficiently and reliably, providing valuable insights for online motorcycle taxi service providers.

This research explores the use of Text Mining Methods as an innovative approach to extract important information from research report text. With a strong conceptual foundation from the literature review, this research details the key concepts of Text Mining, such as tokenization techniques, sentiment analysis, and entity extraction. The steps of applying Text Mining Methods to research reports are explained in depth, with a focus on using such techniques to improve the efficiency and accuracy of information extraction. Through the evaluation of the method's performance, the research demonstrates a significant improvement in analysis speed and information extraction accuracy compared to conventional methods. The research conclusions provide a holistic picture of the potential of Text Mining Methods in improving the effectiveness of the research report text analysis process. The implications of this research stimulate thoughts on the applicability of this technology in various disciplines that rely on research reports as a primary source of information. Thus, this research makes a positive contribution to the understanding and development of text analysis techniques to support more efficient decision making.

The development of digital technology and social media has driven a major transformation in the study of linguistics, particularly in understanding the evolution of global languages and the emergence of digital dialects. Interactions on platforms such as Twitter, Instagram, and TikTok accelerate the formation of new vocabulary, the use of slang, and the spread of cross-cultural expression. Antony and Tramboo (2023) highlight the digital metamorphosis in language, while Friedrich and De Figueiredo (2016) explain the birth of digital Englishes in a sociolinguistic perspective. Language research challenges on social media have also emerged, such as the limitations of dialect processing (Jørgensen et al., 2015) and the influence of digital linguistic ecology (Klushina, 2022). Advances in natural language processing (NLP) have also strengthened this study. Cambria and White (2014) and Skaria et al. (2024) show the development of NLP from sentiment analysis to its application in education (Khensous et al., 2023) and multilingual text analysis (Agüero-Torales et al., 2021). The systematic review of Sundaram et al. (2023) and Prihatini et al. (2023) reinforces the evidence that social media expands language skills and enriches contemporary discourse, while Sun et al. (2021) show an increasing trend of digital linguistic research through bibliometric analysis, including cultural gaps (Rani & Samjetsabam, 2024). Overall, language not only evolves naturally, but also through human interaction with technology and digital culture. Digital dialects, slang, and online communication patterns are part of the global linguistic evolution that opens up interdisciplinary research opportunities between linguistics, computing, and cultural studies.

PeduliLindungi is an application from the Government of Indonesia that was made in response to the COVID-19 pandemic. Since its initial release in 2020, this application has received many updates with the goal of improving its overall performance. One of the basics of updating applications is to process the reviews given by users at the Google Play Store using sentiment analysis. The methods used this time are Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). The sample data used were 300 reviews with positive feedback and 300 reviews with negative feedback, for a total of 600 user reviews. The results of the NBC algorithm calculations produce an accuracy of 76%, a precision of 76%, a recall of 82%, and an f1-score of 79%. As for the SVM algorithm, it produces an accuracy rate of 80%, a precision of 83%, a recall of 80%, and an f1-score of 81%.

The post-reform era became public and government awareness of ethnic Chinese. This is evidenced by President Abdurrahman Wahid's decree to relax ethnic Chinese activities. Because in the Old Order, there was a government policy that was anti-Chinese sentiment. There is even a ban on foreigners to trade in Indonesia, as well as ethnic Chinese who become looting and mob rage, victims of sexual violence and so on. Through this Soe Hok Gie film, it depicts the participation of ethnic Chinese in solving the problem of government authoritarianism and saving Indonesia from the economic crisis. In this studyThis type of research uses library research, with a communication approach that uses media text analysis. The data collection technique in this study uses the semiotic analysis research method of Charles Sanders Peirce. The method uses Sign, Object and Interpretant to explain the content of a film.From the film Soe Hok Gie, this research has found themeaning of Sign, Object and Interpretant of ethnic Chinese political participation in the film Soe Hok Gie based on Charles Sander Peirce's semiotic theory. Finding an analysis of Sign, Object and Interpretant of ethnic Chinese political participation in political developments in Indonesia in the film Soe Hok Gie

The MotoGP One race in West Nusa Tenggara Lombok, Mandalika which was held on March 18 2022, received many responses or reactions from the public on social media, especially Twitter. There are those who agree and disagree about the holding of MotoGP in Mandalika, to find out the responses of the people who agree or disagree is needed that can process tweets data using the sentiment analysis method. The use of BERT (Bidirectional Encoder Representations from Transformers) for sentiment analysis produces a bidirectional language model that can understand the context of all words from a sentence. The dataset used goes through preprocessing stages such as case folding, data cleaning, tokenization, normalization, and removal of stopwords before sentiment analysis is carried out. This study uses several hyperparameters, namely a batch size of 32, the optimizer uses Adam with a learning rate of 3e-6 or 0.000003, and an epoch of 25. The evaluation results of the model obtain an accuracy of 55%. Precision for positive by 56%, neutral by 59%, and negative by 44%. Recall for positive is 74%, neutral is 29%, and negative is 54%. F1-score for positive is 64%, neutral is 38%, and negative is 48%.

Technological developments today make it easy for people to use social media as a means of expressing opinions, including Twitter. The case study taken by the researcher is the sentiment towards the performance of the mayor of Medan. The case was taken because it was widely discussed by Indonesian people, especially the city of Medan on Twitter social media. One of the uses of this research is to find out the trend of Twitter user comments on the performance of the mayor of Medan by conducting a sentiment analysis. Sentiment will be classified as positive, negative and neutral. The algorithm used in sentiment analysis is Naïve Bayes. The stages in conducting sentiment analysis in this study are data preprocessing, data processing, classification, and evaluation. The results of this study are using the SMOTE method, the training and testing ratio is 80:20 because it has the highest accuracy, which is 78% compared to other ratios. The prediction results resulting from the classification turned out to be more dominant towards neutral labels. In addition to classifying for sentiment analysis, this study also measures the performance of the model created. The results showed that the Naïve Bayes algorithm has a precision value of 78%, a recall of 78%, and an f1-score of 77%.

The rapid growth of e-commerce in Indonesia has generated a massive and continuous volume of product reviews. This user-generated content is vital for business intelligence, yet its sheer scale makes manual analysis inefficient, subjective, and practically impossible. Automated sentiment analysis is therefore crucial for businesses to efficiently understand customer feedback and market perception. This research addresses this gap by implementing the Naïve Bayes Classifier (NBC) algorithm to automatically classify the sentiment of Indonesian-language e-commerce product reviews. This study utilized a dataset of 2,000 reviews collected from a major e-commerce platform's "Electronics" category. The data underwent critical text preprocessing stages (case folding, tokenizing, stopword removal, and stemming using the Sastrawi library) to handle the complexities of informal Indonesian text. The dataset was split using an 80/20 ratio, resulting in 1,600 training reviews and 400 testing reviews. Model performance was then evaluated using a Confusion Matrix, focusing on the key metrics of Accuracy, Precision, and Recall. The test results showed excellent performance, achieving an Accuracy of 90.00%, Precision of 91.93%, and Recall of 95.00%. These results demonstrate that the Naïve Bayes algorithm, when supported by robust preprocessing, is a highly effective, reliable, and computationally efficient method for this task, providing a valuable tool for e-commerce stakeholders.

Sentiment analysis of comments from visitors to tourist attractions and the public on tourist attractions in Sukabumi Regency which is one of the areas with various categories of tourist objects and is a sector of economic income for the surrounding community or for related parties such as the government and managers, in sentiment analysis research This includes using the Nave Bayes classification algorithm to examine the sentiment of tourist visitors and the performance of the classification model used. The data used in this research was taken from the website from Tripadvisor and Google Maps using a crawling technique, which then processed the data by a pre-processing process and then applied a classification to the data and got a sentiment visualization by processing word frequency on tourist visitor sentiment data. The results of the accuracy of the model used were re-tested with the k-fold cross validation method and the results of sentiment visualization got the frequency of words that most often appear on negative sentiment labels are garbage, beaches, lacking, places, roads, parking, dirty, entering, caring, clean , expensive, pay, manage, good and water.

Abstract Every new policy by Indonesian government in National Examination (NE) implementation always obtains different respond from public. Since the implementation, NE system already experienced many changes, but in recent years this system receives serious critiques. As a result, government then abolished this system as graduation determinant in 2014. This research analyzes public opinion, in the form of positive and negative sentiment toward NE policy, and factors that drive the opinions. Data in this research obtained from online news media from 2012 to 2015. The result shows that public sentiment fluctuating from year to year and depends on three important factors, i.e. political pressure, extreme events, and media coverage.

The Free Nutritious Meals (MBG) program launched by the Indonesian government aims to address the problem of malnutrition in children and students. However, the acceptance of this program in the community still requires in-depth evaluation because there are many negative sentiments that dominate on social media. This study aims to analyze the sentiment of the Indonesian community regarding the Free Nutritious Meals program on social media X (Twitter) using the Bidirectional Gated Recurrent Unit (BiGRU) model. Of the 1,405 tweet data obtained, 57% were negative opinions and 43% were positive opinions. The evaluation results show that the BiGRU model with FastText support to handle potential overfitting, is able to classify sentiment effectively, with an accuracy of 80%. Sentiment analysis shows that the majority of public responses to the Free Nutritious Meals (MBG) program tend to be negative, with 798 negative tweets and 607 positive. This reflects public dissatisfaction with the implementation of the program and highlights the need for evaluation and improvements so that the benefits can be more widely felt by the community.

Organisasi Kesehatan Dunia (WHO) menyatakan COVID-19 pandemi di awal 2020, dan itu tiba di Indonesia pada Maret 2020. Tidak semua orang di dunia, termasuk Indonesia, memandang pandemi dalam cara yang sama. Menganalisis postingan Facebook Covid-19 adalah cara yang baik untuk mengukur opini publik tentang pandemi. Tujuan dari penelitian ini adalah untuk mengkaji sentimen publik di Indonesia terkait wabah penyakit Covid19 dengan menganalisis reaksi terhadap Postingan Facebook, terutama yang berasal dari yang terverifikasi rekening pemerintah, yang akan dibandingkan dengan akun dari portal berita. Dengan bantuan CrowdTangle, 1211 postingan Facebook yang berisi kata "wabah covid-19" dari 10 pemerintah pejabat dan 10 portal berita dikumpulkan antara 21 Januari 2020, dan 21 Januari 2021, untuk ini belajar. Boxplot dan visualisasi cloud kata, sebagai serta uji statistik, digunakan untuk mengkonfirmasi sentimen yang berbeda dalam posting oleh berbagai jenis akun, serta reaksi publik yang berbeda. Postingan dari pejabat pemerintah, di sisi lain, cenderung menjadi lebih positif, sedangkan posting dari portal berita cenderung lebih negatif. Selanjutnya, posting oleh pejabat pemerintah cenderung menerima lebih positif reaksi, terlepas dari sentimen mereka, dibandingkan dengan posting oleh portal berita, yang menerima berbagai reaksi publik tergantung pada sentimen.

Main Objective: The objective of this research is to complete the exciting and fictive effects of each color and lighting type when practiced in 3D animation setting. In addition, this research also proposes several lighting design recommendations in 3D animation. Background problem: The main sense for the advance of animated films is cinematic lighting effects. Various techniques and approaches have been planned to create lighting effects, but how these effects affect the viewer’s sentimental sense and especially the storytelling practice is not understood. Novelty: The results of the emotional effects of lighting styles show that these effects are not shown in an imposing aspect, which means, not all lighting style affects the feelings on each scale. In this study, several differences were found in several color scales with different cinematic meanings. Research Method: The research method used is quantitative and subjective data analysis adopting a qualitative grounded theory coding with several animated video scenes developed with various colors and low and high-key statistical lighting designs to analyze and measure the effects of different lighting designs. Finding/Result: The finding of this study indicate that cinematic lighting influences the emotional impact of scenes and stories in plan. Conclusion: This study also confirms the current lighting approach, according to the result of this study, it brings guidance on how certain lighting and color techniques can be adopted to influence the audience in 3D animation about feelings and story perception Keywords: Cinematic Lighting, 3D animation, Emotional Storytelling