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Background: Public opinion analysis has become increasingly important in the digital era, where social media platforms generate large-scale textual data reflecting public perceptions toward environmental policies. Advances in Natural language processing (NLP) and machine learning enable systematic sentiment classification to support data-driven decision-making. Objective: This study aims to evaluate the effectiveness of several sentiment classification models in analyzing Indonesian-language social media data related to environmental policies. Method: The research employed a text mining pipeline including data crawling, preprocessing (case folding, tokenization, stopword removal, and stemming), and vectorization using TF-IDF. Three classification models Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) were trained and evaluated using accuracy and F1-score metrics. Results: Experimental findings indicate that LSTM achieved the highest performance with 91.7% accuracy and 91.2% F1-score, outperforming SVM (88.5%) and Logistic Regression (84.2%). Sentiment distribution analysis shows that public opinion is dominated by positive sentiment (47.5%), followed by neutral (32.0%) and negative (20.5%). Overall: The results demonstrate that deep learning-based models provide more robust contextual understanding and more reliable sentiment mapping for environmental policy 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.

Social media (social media) has now become a part of the life of Indonesian people who are very phenomenal. Various kinds of advantages and conveniences are offered to interact with everyone both in terms of business even from various circles. Not only that, with the development of internet use and communication technology devices such as smartphones that are increasingly advanced, it has become one of the drivers of the growth of new networking sites that offer friends and information online. Social media has also become the backbone as a means of communication in this digital century. This study aims to determine students' perceptions about the extent of the use of social media in helping them learn English. English that is expected to be understood and learned is English in hospitality and also in advertising language. English that is expected to be understood and learned is English in hospitality and also in advertising language. This research is important to find out how students perceive the effectiveness of using social media to support their learning and understanding and creativity in the field of English for Hospitality and in compiling advertising language. A total of 50 students from the English Literature study program of Udayana University will be used as data sources in conducting this research. This research will use qualitative methods with a case study design. The use of questionnaires as well as semi-structured interviews will be carried out in collecting data. In analyzing the data will use a basic statistical model of the results of questionnaires on students. Meanwhile, the results of the interview data will be analyzed with the Flow model from Miles and Huberman.