Sentiment Analysis on the Use of Electric Vehicles With Long Short-Term Memory

Electric vehicles, or EVs, have drawn a lot of attention lately as an eco-friendly way to cut carbon emissions and lessen reliance on fossil fuels. This work uses a neural network model called Long Short-Term Memory (LSTM), which is effective at processing sequential input, to analyze user attitudes toward electric vehicles. Opinion data were gathered from the social media network Twitter between March 1st and March 31st, 2023, using the keyword “electric vehicles”. Based on the obtained opinion data, the LSTM approach is utilized to categorize sentiments as positive or negative. Implementation of the random oversampling technique is important to be applied in increasing the accuracy of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{n}$</tex> recall model for the specified class. The study's conclusions show that the LSTM model performs admirably when it comes to sentiment analysis using the test dataset. The model evaluation yields a precision of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{99.65 \%}$</tex> and an accuracy rating of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{97.79 \%}$</tex>. The modeling process is carried out with variance splitting data where the highest accuracy is obtained from a comparison of training testing data of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{90 \%}$</tex>: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{10 \%}$</tex>.