Application of Word2Vec and LSTM Models in Sentiment Analysis of Mobile Legends User Reviews

Abstract

Sentiment analysis has become an important aspect of understanding user opinions regarding a product or service, including in the gaming industry. This study implements a combination of Word2Vec and Long Short-Term Memory (LSTM) models to analyze the sentiment of user reviews for the game Mobile Legends, obtained from the Google Play Store. The dataset used comprises 100,000 reviews that have undergone preprocessing stages such as text cleaning, tokenization, and stopword removal. The Word2Vec model is employed to represent the text in the form of numerical vectors, while LSTM is used to predict the sentiment of the reviews. Evaluation results indicate that this model achieves an accuracy of 87.88%, demonstrating the effectiveness of this method in classifying user sentiment. Further analysis reveals that the majority of user reviews are positive, with words such as "good," "exciting," and "awesome" frequently appearing in the word cloud. This research can provide insights for game developers in understanding user opinions and serve as a reference for the application of deep learning in sentiment analysis within the gaming industry.