Pendekatan Text Mining dalam Menilai Sentimen Publik pada Baterai Kendaraan Listrik - Trisna Yuniarti; Juli Astuti; Firdhani Faujiyah; M. Zaiyar (Jurnal Serambi Engineering, 2024)
The use of technology and information in the Industry 4.0 era has been widely used to extract information from textual data patterns. The development of technology and the availability of big data from various platforms can provide valuable insights and facilitate better decision making. In this study, we aim to extract information and obtain the main topics of public sentiment on electric vehicle batteries. The data source is obtained from the results of comments or tweets on X social media. A total of 1,302 texts were processed using text mining techniques in the orange data mining application. This technique consists of several stages of text processing, namely transformation, filtering and tokenisation. The results of the text processing are extracted by word cloud to find the characteristics of words that are often discussed by the community. Next, sentiment analysis is performed to find out people's opinions about electric vehicle batteries based on positive, negative and neutral categories. Finally, topic modelling is performed using LDA to identify the topics discussed in each sentiment category. The results showed that public opinion was divided into three categories: 37% positive, 42% neutral and 21% negative. A frequently discussed topic in relation to electric vehicle batteries is the word feature nickel. Topic modelling produces five main topic categories that are frequently discussed by the public.