| LI Shengwang,YANG Yi,XU Yunfeng,ZHANG Yan.A survey of text aspect-based sentiment classification[J].Journal of Hebei University of Science and Technology,2020,41(6):518-527 |
| 文本方面級情感分類方法綜述 |
| A survey of text aspect-based sentiment classification |
| Received:October 02, 2020 Revised:November 06, 2020 |
| DOI：10.7535/hbkd.2020yx06006 |
| 中文關鍵詞: 自然語言處理 情感分類 方面級別 文本分類 深度學習 圖神經網絡 圖卷積網絡 |
| 英文關鍵詞:natural language processing sentiment classification aspect-based text classification deep learning graph neural network graph convolutional network |
| 基金項目:中國留學基金委地方合作項目（201808130283）; 中國教育部人工智能協同育人項目（201801003011）; 河北科技大學校立課題（82/1182108） |
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| 中文摘要: |
| 隨著深度學習的發展，方面級情感分類已經在單領域和單一語言中取得了大量的研究成果，但是在多領域的研究還有提升的空間。通過對近年來文本方面級情感分類方法進行歸納總結，介紹了情感分類的具體應用場景，整理了方面級情感分類常用的數據集，并對方面級情感分類的發展進行了總結與展望，提出未來可在以下領域開展深入研究：1）探索基于圖神經網絡的方法，彌補深度學習方法存在的局限性；2）學習融合多模態數據，豐富單一文本的情感信息；3）開展更多針對多語言文本和低資源語言的研究。 |
| 英文摘要: |
| With the development of deep learning, aspect-based sentiment classification has achieved a lot of results in a single field and a single language, but there is room for improvement in multi-fields. By summarizing up the methods of text aspect-based sentiment classification in recent years, the specific application scenarios of sentiment classification were introduced, and the commonly used data sets of aspect-based sentiment classification were categorized. The development of aspect-based sentiment classification were summarized and prospected, and further research can be carried out in the following areas: exploring methods based on graph neural networks to make up for the limitations of deep learning methods; learning to fuse multi-modal data to enrich the emotional information of a single text; developing more targeted research work on multilingual texts and low-resource languages. |
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