LSTM Algorithm Analysis for Opinion Classification on the Development of Oil Palm Plantations in Indonesia

Abstract

This study aims to analyze public opinion on the development of oil palm plantations in Indonesia through sentiment classification using the Long Short-Term Memory (LSTM) algorithm. The data used in this study were taken from Twitter by collecting 750 tweets consisting of three sentiment categories: positive, negative, and neutral. The pre-processing stage includes filtering, tokenization, stemming, and word-embedding to prepare the data for further analysis. The LSTM model was applied to classify the sentiment of the processed tweets, and evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results showed that the LSTM model produced an accuracy of 70.81%, with precision, recall, and F1-score varying between classes, namely 0.92, 0.71, and 0.80 for the negative class, 0.48, 0.63, and 0.55 for the neutral class, and 0.77, 0.77, and 0.77 for the positive class. This study shows that LSTM can be used to analyze public opinion on the issue of oil palm plantations, despite challenges in classifying neutral tweets.