An approach for bibliographic citation sentiment analysis using deep learning
| Autor: | Satish Muppidi, B. Kishore, Satya Keerthi Gorripati |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Information retrieval
Computer science business.industry Deep learning InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL 05 social sciences Sentiment analysis 050301 education 02 engineering and technology Bibliographic Citation Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business 0503 education Software |
| Zdroj: | International Journal of Knowledge-based and Intelligent Engineering Systems. 24:353-362 |
| ISSN: | 1875-8827 1327-2314 |
| DOI: | 10.3233/kes-200087 |
| Popis: | Sentiment analysis of scientific citations is a novel and remarkable research area. Most of the work on opinion or sentiment analysis has been suggested on social platforms such as Blogs, Twitter, and Facebook. Nevertheless, when it comes to recognizing sentiments from scientific citation papers, investigators used to face difficulties due to the implied and unseen natures of sentiments or opinions. As the citation references are reflected implicitly positive in opinion, famous ranking and indexing prototypes frequently disregard the sentiment existence while citing. Hence, in the proposed framework the paper emphasizes the issue of classifying positive and negative polarity of reference sentiments in scientific research papers. First, the paper scraps the PDF articles from arxiv.org under the computer science group consisting of articles that are comprised of ‘autism’ in their title, then the paper extracted cited references and assigns polarity scores to each cited reference. The paper uses a supervised classifier with a combination of significant feature sets and compared the performance of the models. Experimental results show that a combined CNN-LSTM deep neural network model results in 85% of accuracy while traditional models result in less accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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