Sentiment Analysis of Public Opinion Regarding Papuan Local Languages Condition Using Data Science Approach - Hasan, Nur Fitrianingsih; Aisyah, Aisyah; Rahman, Rahman; Wonda, Herlin (Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning, 2022)
Regional languages ​​can support economic empowerment and improvement through the tourism sector. Opinions from people's expressions in social media and online news collections in reporting the condition of regional languages ​​often become headlines in cyberspace that number in the thousands, which can be used as new knowledge as a basis for making decisions through the mining method. This study aims to explore public opinion sentiment related to the condition of the Papuan language, sourced from text data in cyberspace using a data science approach, namely the classification method with text mining techniques using the naïve bayes algorithm. Public opinion sentiments are processed and the results are presented using word cloud visualization through 4 stages of data science, namely data collection, data preprocessing, modeling exploration and visualization analysis. The result of 778 opinions, 92% tend to have a positive sentiment. The analysis of public opinion sentiment is carried out by the naïve bayes algorithm which has an algorithm model accuracy of 78% and a precision of 88%. The machine learning model that was built and the word cloud visualization analysis succeeded in providing new insights regarding the condition of the Papuan language.