http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
An algorithm and method for sentiment analysis using the text and emoticon
Mohammad Aman Ullah,Syeda Maliha Marium,Shamim Ara Begum,Nibadita Saha Dipa 한국통신학회 2020 ICT Express Vol.6 No.4
People nowadays use emoticons in their text increasingly in order to express their feelings or recapitulate their words. Earlier machine learning techniques only involve the classification of text, emoticons or images solely where emoticons with text have always been neglected, thus ignored lots of emotions. This research proposed an algorithm and method for sentiment analysis using both text and emoticon. In this work, both modes of data were analyzed in combined and separately with both machine learning and deep learning algorithms for finding sentiments from twitter based airline data using several features such as TF–IDF, Bag of words, N-gram, and emoticon lexicons. This research demonstrates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual data analysis. Also, deep learning algorithms are found to be better than machine learning algorithms.
AHM Zadidul Karim,Md Abdullah Al Mahmud,Md Sazal Miah,Shikder Shafiul Bashar,Seungmin Oh,Jinsul Kim,Maliha Marium 한국디지털콘텐츠학회 2020 The Journal of Contents Computing Vol.2 No.2
Photoplethesmography (PPG) is a low cost, non-invasive heart Rate (HR) monitoring process. It contains important health information. So, based on these characteristics this paper has taken step to go with it. PPG signal recorded very easily from the surface of the skin by using wearable device. So, during exercise PPG signal is corrupted heavily by Motion Artifact (MA). The interest of this paper is to work on removing the MA and reconstruction of clean PPG signal. This paper has worked on two stages. One is the tracking of PPG signal and detection of the peak of ECG signal.