Analisis Sentimen Review Film Avatar 2 pada Platform  IMDb Menggunakan LSTM dan GRU

Rani Saputri; Anna Baita

doi:10.62951/router.v3i1.395

Authors

Rani Saputri Universitas Amikom Yogyakarta
Anna Baita Universitas Amikom Yogyakarta

DOI:

https://doi.org/10.62951/router.v3i1.395

Keywords:

GRU, Imbalance Data, LSTM, Movie Reviews, Sentiment Analysis

Abstract

This research uses a deep learning-based sentiment analysis approach with several main stages, namely data collection, preprocessing, model preparation, and model building. In addition, this research also evaluates the impact of data splitting techniques on the model's performance during the training process.The evaluation results show that the LSTM-GRU model achieved the best performance on the character aspect, with an F1-score of 0.72 in the 90:10 splitting scenario. Meanwhile, the lowest F1-score was found in the editing aspect, with a value of 0.51 in the 80:20 splitting scenario. These findings indicate that the model is more effective in recognizing sentiment in narrative aspects compared to technical aspects. Furthermore, the data splitting technique significantly influences model performance, both in determining accuracy levels and in optimizing the model's effectiveness in identifying sentiment patterns more accurately.

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