PERBANDINGAN TEKNIK PEMBOBOTAN FAST TEXT DAN WORD2VEC UNTUK DETEKSI MULTICLASS EMOSI MENGGUNAKAN METODE CONV-LSTM - Salwa Ziada Salsabiila (2023)
Emotions are one aspect of human psychology which includes the ability to control a feeling and behavior that creates a way of self-expression that is in line with the environment. The source of data collection in processing the emotion detection process with text comes from social media Twitter. Solving the problem of how to compare the embedding weighting techniques of Fast Text and Word2Vec with the combination of Conv-LSTM to the emotion detection text classification process and how the results of the performance analysis are. This study aims to implement and obtain the results of performance analysis in a comparison of the Fast Text and Word2Vec algorithms. The method used is a combination of the Convolutional Neural Network CNN and Long Short Term Memory (LSTM) methods, which are then referred to as Conv-LSTM for the text classification process for detecting labels on sentiment analysis research objects regarding Mr. Anies Rasyid Baswedan's keywords based on 98,664 data tweets from social Twitter media with 6 multiclass classifications, namely happy, sad, angry, disgusted, surprised, and afraid. The highest accuracy results using the Word2Vec Skipgram method with a Max Pooling of 0.8499.