Sentiment Analysis of Mobile Legends App Reviews Using Machine Learning and LSTM-Based Deep Learning Models

TL;DR

This study compares traditional Machine Learning models with an LSTM-based deep learning approach for sentiment analysis of Mobile Legends app reviews, demonstrating the superior performance of LSTM models.

Contribution

It introduces an LSTM-based model for sentiment analysis and shows its effectiveness over classical models on app review data.

Findings

LSTM model achieves 92% accuracy and 91% F1-score.

Deep learning models better handle informal, context-dependent text.

LSTM outperforms traditional ML models in sentiment classification.

Abstract

This paper compares Machine Learning and LSTM-based Deep Learning methods for sentiment analysis of Mobile Legends app reviews. Using a dataset of 10,000 reviews labeled as positive, negative, and neutral, the study evaluates traditional models with TF-IDF and PyCaret AutoML and compares them against an LSTM model designed to capture sequential text dependencies. The results show that the LSTM model outperforms the classical Machine Learning baselines, achieving 92% accuracy and a weighted F1-score of 91%. The findings indicate that deep learning is more effective for handling informal and context-dependent user review text.