Safrizal Ahmad
Ahmad Maulid Ridwan
Gilang Dwi Setyawan


Sentiment analysis is gaining popularity as the number of internet users increases. Internet users often express their opinions through reviews on websites. The customer opinions expressed have a huge impact on sellers and customer numbers, as many consumers rely on online reviews as a reference when purchasing products. In order to quickly understand sentimental views and tendencies towards a product or event, a text sentiment analysis is performed on the opinions expressed by users. Sentiment analysis focuses on understanding the sentiments contained in the text. One common approach in sentiment analysis is to use Deep Learning (DL) models. This study aims to analyze product sentiment in the Tools & Home category from Amazon using models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The CNN model is used to extract features from words that reflect short-term sentiment dependencies, while LSTM is used to establish long-term sentiment relationships between words. CNN and LSTM are sophisticated DL models, capable of efficiently processing text data and recognizing relationships and patterns that exist at various levels of abstraction. The purpose of this study is to understand the differences in the performance of the DL model in conducting sentiment analysis, it is hoped that it can also be a reference for those who plan to apply other DL models.


How to Cite
Ahmad, S., Ridwan, A. M. ., & Setyawan, G. D. . (2023). ANALISIS SENTIMEN PRODUCT TOOLS & HOME MENGGUNAKAN METODE CNN DAN LSTM. TEKNOKOM, 6(2), 133–140.

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