Exploring user historical semantic and sentiment preference for microblog sentiment classification
| Autor: | Ran Yu, Xiaofei Zhu, Jiafeng Guo, Stefan Dietze, Jie Wu, Ling Zhu, Katarina Boland |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
User information
Information retrieval Computer science Microblogging Cognitive Neuroscience InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Sentiment analysis Context (language use) Recommender system Preference Computer Science Applications Artificial Intelligence Component (UML) Social media Encoder |
| Zdroj: | Neurocomputing. 464:141-150 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2021.08.089 |
| Popis: | Microblog text is usually very short, thereby challenging existing sentiment classification methods by providing models with little context. Recently, historical user information has been widely used in many real-world applications, such as recommender systems. However, few research works consider user historical states in the loop of microblog sentiment analysis. In this work, we propose to involve historical user information for microblog sentiment analysis to alleviate the context sparsity problem. In particular, we propose a novel neural microblog sentiment classification method which learns informative representations of microblog posts by exploiting both a user’s contextual information and his/her historical state information. The proposed method consists of four components, i.e., a micropost encoder, a user historical sentiment encoder, a User Historical Semantic Encoder, and a micropost sentiment classification component. Extensive experiments are conducted on real-world data collected from Weibo, and experimental results show that the proposed approach achieves superior performance as compared to state-of-the-art baselines. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect