Improving sentiment analysis on PeduliLindungi comments: a comparative study with CNN-Word2Vec and integrated negation handling

(1) * Herlina Jayadianti Mail (Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)
(2) Berliana Andra Arianti Mail (Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)
(3) Nur Heri Cahyana Mail (Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)
(4) Shoffan Saifullah Mail (Universitas Pembangunan Nasional Veteran Yogyakarta; AGH University of Krakow, Poland)
(5) Rafał Dreżewski Mail (AGH University of Krakow, Poland)
*corresponding author


This study investigates sentiment analysis in Google Play reviews of the PeduliLindungi application, focusing on the integration of negation handling into text preprocessing and comparing the effectiveness of two prominent methods: CNN-Word2Vec CBOW and CNN-Word2Vec SkipGram. Through a meticulous methodology, negation handling is incorporated into the preprocessing phase to enhance sentiment analysis. The results demonstrate a noteworthy improvement in accuracy for both methods with the inclusion of negation handling, with CNN-Word2Vec SkipGram emerging as the superior performer, achieving an impressive 76.2% accuracy rate. Leveraging a dataset comprising 13,567 comments, this research introduces a novel approach by emphasizing the significance of negation handling in sentiment analysis. The study not only contributes valuable insights into the optimization of sentiment analysis processes but also provides practical considerations for refining methodologies, particularly in the context of mobile application reviews.


analysis sentiment; word2vec; negation handling; opinion mining; cbow; skipgram; text classification



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