Multi-aspect Sentiment Analysis of Tiktok Application Usage Using FasText Feature Expansion and CNN Method

RIFKI ALFIAN ABDI MALIK

Informasi Dasar

83 kali
23.04.1046
006.312
Karya Ilmiah - Skripsi (S1) - Reference

Among the many social media platforms that have emerged, TikTok is the platform that has the most significant number of subscribers compared to other platforms. However, not all reviews given by TikTok users are good reviews. These reviews will later be analyzed according to predetermined aspects, namely feature aspects, business aspects, and content aspects based on reviews written on the Google Play Store, using data crawling and will pass the preprocessing and weighting stages. The weighting method used is Term FrequencyInverse Document Frequency (TF-IDF). Then, the sentiment analysis process using the Convolutional Neural Network (CNN) method will be carried out to determine whether each review is included in Sentiment Analysis, and feature expansion will be carried out to determine what words are interrelated with a particular word. The purpose of this research is to analyze sentiment using Convolutional Neural Network and fastText feature expansion. The highest accuracy result was 87.74%.

Subjek

DATA MINING
DATA ANALYSIS,

Katalog

Multi-aspect Sentiment Analysis of Tiktok Application Usage Using FasText Feature Expansion and CNN Method
 
 
Indonesia

Sirkulasi

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Pengarang

RIFKI ALFIAN ABDI MALIK
Perorangan
Yuliant Sibaroni
 

Penerbit

Universitas Telkom, S1 Informatika
Bandung
2023

Koleksi

Kompetensi

 

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