Sentiment Analysis of Mobile Legends App Reviews Using Machine Learning and LSTM-Based Deep Learning Models
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Sentiment AnalysisSimilar Papers 제목 키워드 기반
The use of new technologies to support Public Administration. Sentiment analysis and the case of the app IO
App IO is an app developed for the Italian PA. It is definitely useful for citizens to interact with the PA and to get services that were not digitized yet. Nevertheless, it was not perceived in a good way by the citizen…
Sentiment AnalysisBERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews
User reviews have an essential role in the success of the developed mobile apps. User reviews in the textual form are unstructured data, creating a very high complexity when processed for sentiment analysis. Previous app…
Sentiment AnalysisTransfer LearningSentiment Classification of Customer Reviews about Automobiles in Roman Urdu
Text mining is a broad field having sentiment mining as its important constituent in which we try to deduce the behavior of people towards a specific item, merchandise, politics, sports, social media comments, review sit…
AttributeClassificationGeneral ClassificationSentiment Analysis+3SCARE ― The Sentiment Corpus of App Reviews with Fine-grained Annotations in German
The automatic analysis of texts containing opinions of users about, e.g., products or political views has gained attention within the last decades. However, previous work on the task of analyzing user reviews about mobil…
Sentiment AnalysisBanking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews
The rapid growth of mobile banking (m-banking), especially after the COVID-19 pandemic, has reshaped the financial sector. This study analyzes consumer reviews of m-banking apps from five major Canadian banks, collected …
Sentiment Analysis