Abstract | ||
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Topic and sentiment detection has been considered as an effective method to reveal the facts and sentiments in a massive volume of information. Existing works mainly focus on separate topic and sentiment extraction or static topic-sentiment associations, neglecting topic-sentiment dynamics and missing the opportunity to provide a in-depth analysis of online news. Actually, sentiment orientations are highly dependent on topic content and thus detecting topic-sentiment associations and their evolution over time is very important. This paper proposes a manifold learning-based model to explore the topic-sentiment associations and their evolution over time in the online news domain. The proposed model can visualize the hidden sentiment dynamics of topics in a low-dimensional space. Extensive experiments are conducted on online news crawled from the American Cable News Networks (CNN) website. The experimental results show that the proposed model outperforms the KL distance-based and the Similarity-based methods and improves the accuracy of topic classification by 12%. |
Year | DOI | Venue |
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2020 | 10.1007/s10844-019-00586-5 | Journal of Intelligent Information Systems |
Keywords | DocType | Volume |
Topic-sentiment association, Sentiment evolution, Manifold learning | Journal | 55 |
Issue | ISSN | Citations |
1 | 0925-9902 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
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Yuemei Xu | 1 | 68 | 7.00 |
Yang Li | 2 | 208 | 15.35 |
Ye Liang | 3 | 1 | 1.64 |
Lianqiao Cai | 4 | 0 | 0.34 |