<![CDATA[After introducing electric vehicle products to the market, manufacturers need to find out public opinion/sentiment towards the products that have been introduced. This information can be used by manufacturers as a basis for determining the next business strategy. One technique that can be used is sentiment analysis which is part of Natural Language Processing in Data Mining / Artificial Intelligence. This study aims to determine public sentiment towards an electric vehicle product using a lexicon and rule-based sentiment analysis method approach called VADER (Valence Aware Dictionary and Sentiment Reasoner). A total of 3707 tweets (955 unique data) were taken using the tweepy library in Python and analyzed using the VADER submodule in the nltk library (Natural Language Toolkit) and visualization was made using the wordcloud library. The results showed that the majority (95%) of public sentiment was positive, and 5% negative. The positive sentiment conveyed by the public is related to product advantages such as features, design, sophistication, and environmental friendliness. Meanwhile, negative sentiments are related to the absence of fast charging and prices that are still considered uneconomical.]]>
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