期刊
IEEE ACCESS
卷 9, 期 -, 页码 68609-68618出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3076789
关键词
Feature extraction; Deep learning; Natural language processing; Task analysis; Social networking (online); Detectors; Licenses; Sarcasm detection; natural language processing; deep learning
资金
- Universiti Putra Malaysia through the Geran Putra Inisiatif Siswazah (Research Title: Sarcasm Detection Using Machine Learning to Enhance Sentiment Analysis)
The study focuses on detecting sarcasm in tweets by combining deep learning features with contextual handcrafted features. Logistic Regression is identified as the best classification algorithm for this task, based on positive results in terms of Accuracy, Precision, Recall, and F1-measure.
Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted feature sets are created based on their respective contextual explanations. Each feature sets are specifically designed for the sole task of sarcasm detection. The objective is to find the most optimal features. Some sets are good to go even when it is used in independence. Other sets are not really significant without any combination. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features are classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task. Furthermore, result comparison to recent works and the performance of each feature set are also shown as additional information.
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