Hierarchical Sentiment Estimation Model for Potential Topics of Individual Tweets
| Autor: | Gongshen Liu, Quanhai Zhang, Yinghua Ma, Yilin Dai, Xiang Lin, Qian Ji |
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| Rok vydání: | 2020 |
| Předmět: |
User information
business.industry Computer science Deep learning InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Sentiment analysis computer.software_genre Convolutional neural network Binary classification Leverage (statistics) Artificial intelligence InformationSystems_MISCELLANEOUS business computer Natural language processing Transformer (machine learning model) |
| Zdroj: | Communications in Computer and Information Science ISBN: 9783030638191 ICONIP (4) |
| DOI: | 10.1007/978-3-030-63820-7_75 |
| Popis: | Twitter has gradually become a valuable source of people’s opinions and sentiments. Although tremendous progress has been made in sentiment analysis, mainstream methods hardly leverage user information. Besides, most methods strongly rely on sentiment lexicons in tweets, thus ignoring other non-sentiment words that imply rich topic information. This paper aims to predict individuals’ sentiment towards potential topics on a two-point scale: positive or negative. The analysis is conducted based on their past tweets for the precise topic recommendation. We propose a hierarchical model of individuals’ tweets (HMIT) to explore the relationship between individual sentiments and different topics. HMIT extracts token representations from fine-tuned Bidirectional Encoder Representations from Transformer (BERT). Then it incorporates topic information in context-aware token representations through a topic-level attention mechanism. The Convolutional Neural Network (CNN) serves as a final binary classifier. Unlike conventional sentiment classification in the Twitter task, HMIT extracts topic phrases through Single-Pass and feeds tweets without sentiment words into the whole model. We build six user models from one benchmark and our collected datasets. Experimental results demonstrate the superior performance of the proposed method against multiple baselines on both classification and quantification tasks. |
| Databáze: | OpenAIRE |
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