Klasifikasi Video Clickbait pada YouTube Berdasarkan Analisis Sentimen Komentar Menggunakan Learning Vector Quantization (LVQ) dan Lexicon-Based Features - Dwi Wahyu Puji Lestari; Rizal Setya Perdana; Putra Pandu Adikara (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Clickbait is social media content that aims to attract website visitors in order to visit their content by creating clickbait in form of appealing or provoking title but with irrelevant content. It makes the visitor decieved and disappointed, so they usually vent their frustation by writing their positive or negative opinion on the comment section. The document that is used in the research comes from YouTube comments that is related with Indonesian clickbait and non-clickbait content. This research used Learning Vector Quantization (LVQ) method and Lexicon-Based Features as word weighting other than using TF-IDF. This research uses 300 data consisting 2 type of data, training and testing data with the ratio of 70% training data and 30% testing data. The accuracy of the system that is obtained by classification using LVQ without Lexicon-Based Features is 54.54%, 1 precission, 0.1667 recall and 0.2858 f-measure. The result of the accuracy of the system using LVQ and Lexicon-Based Features is 90.91%, 0.8571 precission, 1 recall, and 0.9231 f-measure. The conclution is that LVQ method and Lexicon-Based Features can be used for sentiment classification.