Polarity detection of online news articles based on sentence structure and dynamic dictionary
| dc.contributor.author | Islam, M.U., | |
| dc.contributor.author | Ashraf, F.B., | |
| dc.contributor.author | Abir, A.I., | |
| dc.contributor.author | Mottalib, M.A. | |
| dc.date.accessioned | 2025-04-28T05:45:10Z | |
| dc.date.issued | 2017-07-02 | |
| dc.description.abstract | The importance of online news article has evolved notably with the advancement of information and technology. However, some of the news are violent as well as obnoxious. So, identifying and categorizing online news article automatically is important as well as remains challenging. Using opinion mining and sentiment analysis, we propose an intuitive approach of detecting positive or negative news from an online news article. Our approach consists of a sentence identification phase, followed by a dynamic library of predefined negative and positive strings and at last marking whether the paragraph is positive, negative or neutral. Our approach detects the polarity of online news articles with around 91% accuracy rate. Sentence type identification before using dynamic dictionary of positive and negative words is the key factor which resolves the issue of finding out the part of the sentence which holds the polarity of the sentence. | |
| dc.identifier.citation | Islam, M. U., Ashraf, F. B., Abir, A. I., & Mottalib, M. A. (2017, December). Polarity detection of online news articles based on sentence structure and dynamic dictionary. In 2017 20th International Conference of Computer and Information Technology (ICCIT) (pp. 1-5). IEEE. | |
| dc.identifier.isbn | 978-153861150-0 | |
| dc.identifier.uri | http://dspace.uttarauniversity.edu.bd:4000/handle/123456789/421 | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject | Data mining | |
| dc.subject | Opinion mining | |
| dc.subject | Phrase-level Sentiment analysis | |
| dc.subject | Sentence analysis | |
| dc.title | Polarity detection of online news articles based on sentence structure and dynamic dictionary | |
| dc.type | Other |