@inproceedings{1776e27651d04f0d8857b7830dcd7bae,
title = "Article Citation Sentiment Analysis Using Deep Learning",
abstract = "We performed sentiment analysis on article citation sentences corpora bearing three polarities viz. positive, negative, and neutral. Due to scarcity of negative citation sentences, the dataset suffers from a huge class imbalance issue. To tackle this, we proposed an ensemble feature engineering method for deep learning, which uses embedding of text and its dependency relationships. The performance of deep learning models was compared with a support vector machine and logistic regression approach using bag of words. Experimental results show that deep learning can be used effectively for an imbalanced dataset by applying the proposed ensemble features. Statistical significance test indicates that one-hot supervised LSTM is statistically not different from the baseline methods for two datasets, one developed by us and the other taken from literature.",
keywords = "Citation Sentiment Analysis, Deep Learning, Machine Learning",
author = "Kumar Ravi and Srirangaraj Setlur and Vadlamani Ravi and Venu Govindaraju",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018 ; Conference date: 16-07-2018 Through 18-07-2018",
year = "2018",
month = oct,
day = "4",
doi = "10.1109/ICCI-CC.2018.8482054",
language = "English",
series = "Proceedings of 2018 IEEE 17th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "78--85",
editor = "Newton Howard and Sam Kwong and Yingxu Wang and Jerome Feldman and Bernard Widrow and Phillip Sheu",
booktitle = "Proceedings of 2018 IEEE 17th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018",
address = "United States",
}