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LSTM deep learning model outperforms ML for Mobile Legends app review sentiment analysis

By PulseAugur Editorial · [1 sources] ·

This paper evaluates machine learning and LSTM-based deep learning models for sentiment analysis of Mobile Legends app reviews. Utilizing a dataset of 10,000 labeled reviews, the study found that the LSTM model achieved 92% accuracy and a 91% weighted F1-score, outperforming traditional machine learning baselines. The research suggests deep learning methods are more adept at processing the informal and context-dependent language found in user reviews. AI

IMPACT Demonstrates the effectiveness of deep learning for analyzing informal user-generated text, potentially improving product feedback analysis.

RANK_REASON Academic paper detailing a comparative study of machine learning models for sentiment analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LSTM deep learning model outperforms ML for Mobile Legends app review sentiment analysis

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Academic paper detailing a comparative study of machine learning models for sentiment analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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144 days old
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Vira Putri Maharani, Kharisa Harvanny, Daris Samudra, Luluk Muthoharoh, Ardika Satria, Martin Clinton Tosima Manullang ·

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

    arXiv:2605.01317v1 Announce Type: new 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 tradit…

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