<![CDATA[Elections are a crucial democratic procedure as they offer citizens the privilege of exercising their rights. Consequently, there is a wide range of contrasting responses on social media, particularly on Twitter. Twitter is a platform that facilitates sentiment analysis, a valuable tool for comprehending the public's perception of leaders and the topics addressed in a campaign. This analysis encompasses both positive and negative opinions, which are of particular importance for study. Sentiment analysis can be used to determine the propensity of Twitter users to publish material. This study commenced with gathering data from Twitter, followed by data modeling utilizing the LSTM technique and real-time implementation, resulting in the classification of individuals into supporters, non-supporters, and neutrals. LSTM, short for Long Short-Term Memory, is a sophisticated deep learning technique that serves as the foundational framework for the research project titled "Sentiment Analysis of Twitter Users towards the 2024 Election using the LSTM Method". LSTM has the benefit of being capable of retaining and manipulating long-term knowledge, as well as accessing and modifying past information. The objective of this study is to ascertain the sentiment analysis of Twitter users on the 2024 election, categorizing it as favorable, negative, or neutral. The study yielded an accuracy rate of 78% utilizing the LSTM approach.]]>
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