フジサワ　キミヤ   FUJISAWA, Kimiya   藤澤　公也    所属   メディア学部 メディア学科    職種   准教授
言語種別	英語
発行・発表の年月	2021/04
形態種別	学術論文
査読	査読あり
標題	Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts
執筆形態	共著
掲載誌名	International Journal of Intelligent Engineering and Systems
掲載区分	国外
出版社・発行元	INASS
巻・号・頁	14(2),pp.521-535
著者・共著者	Esther Irawati Setiawan, Hans Juwiantho, Joan Santoso, Surya Sumpeno, Kimiya Fujisawa, Mauridhi Hery Purnomo
概要	Social media development makes it possible for everyone to express their opinions and information through text, speech, video, or images. Multiview sentiment analysis in current studies generally combines two modalities, text and image. It seeks to classify social media posts into two or more polarities, such as positive, neutral, or negative. To improve the performance of multiview Sentiment Analysis, we added another modality, which is concepts derived from text and image. Our proposed model integrates three views into a fusion with an ensemble approach by a metaclassifier. We performed text classification with Deep Convolutional Neural Networks. The input feature is Word2Vec for text representation in order to preserve semantic meaning. Additionally, we analyzed concepts from texts with SenticNet 5 as a knowledge base model and extracted concepts from images using the DeepSentiBank model. We obtained 2089 Adjective Noun Pairs and classified it with Multi-Layer Perceptron. Then we combined predicted probabilities from each classifier for Image, Text, and Concept by Ensemble Learning. A meta-classifier was implemented to predict the final sentiment from a fusion of Image-Text-Concept features. The fusion for multiview sentiment analysis works well and could achieve the best accuracy of 70% by applying the ensemble approach with Logistic Regression as the meta-classifier.