Multi-Aspect Sentiment Analysis on Tiktok Using Random Forest Classifier and Word2Vec
ADIV HARJADINATA
Informasi Umum
Kode
22.04.3443
Klasifikasi
621.367 - Technological photography and photo-optics, Spectrography, Stroboscopic photography, Image processing, Optical data processing
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Machine Learning, Image Processing - Computer Image,
Informasi Lainnya
Abstraksi
<p>The number of people using the internet today is directly proportionate to the number of people using social media. Compared to other social media platforms, Tiktok is one of the most downloaded social media platforms on Google Play. However, not among Tiktok’s reviews are positive. Based on reviews on Google Play, these reviews can be used as data in sentiment analysis to determine which aspects are reviewed by users and whether the sentiment is positive or negative. The aspects used in this study include features, business, and content. Word2Vec was used for data modeling, and Random Forest Classifier was used for classification. Using the Skip-gram model, without Stemming and CBOW model without stopwords the best parameter testing on Random Forest achieved the accuracy an average of all aspects of 78.33%.</p>
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Koleksi & Sirkulasi
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Pengarang
| Nama | ADIV HARJADINATA |
| Jenis | Perorangan |
| Penyunting | Yuliant Sibaroni |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2022 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
