[vc_empty_space][vc_empty_space]
Emotion classification on youtube comments using word embedding
Savigny J.a, Purwarianti A.a
a School of Electrical and Informatics Engineering, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2017 IEEE.Youtube is one of the most popular video sharing platform in Indonesia. A person can react to a video by commenting on the video. A comment may contain an emotion that can be identified automatically. In this study, we conducted experiments on emotion classification on Indonesian Youtube comments. A corpus containing 8,115 Youtube comments is collected and manually labelled using 6 basic emotion label (happy, sad, angry, surprised, disgust, fear) and one neutral label. Word embedding is a popular technique in NLP, and have been used in many classification tasks. Word embedding is a representation of a word, not a document, and there are many methods to use word embedding in a text classification task. Here, we compared many methods for using word embedding in a classification task, namely average word vector, average word vector with TF-IDF, paragraph vector, and by using Convolutional Neural Network (CNN) algorithm. We also study the effect of the parameters used to train the word embedding. We compare the performance of the classification with a baseline, which was previously state-of-the-art, SVM with Unigram TF-IDF. The experiments showed that the best performance is achieved by using word embedding with CNN method with accuracy of 76.2%, which is an improvement from the baseline.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Classification tasks,comments,Convolutional neural network,emotion,Emotion classification,State of the art,Text classification,word embedding[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]classification,CNN,comments,emotion,word embedding[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICAICTA.2017.8090986[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]