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Make aspect-based sentiment classification go further: step into the long-document-level
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AI
DOI：10.1007/s10489-021-02836-y.png)
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摘要
En 中文
Aspect-based sentiment classification (ABSC) is a fine-grained analysis task that obtains different sentiment polarities contained in a single text from the views of different aspects. Its practicability draws so much attention from researchers that the number of related works grows explosively. However, existing works mainly aim to obtain polarities from short texts (shorter than 100 words), only a few works analyze documents (shorter than 500 words), but almost no work analyzes long documents (LD, longer than 500 words). This situation makes ABSC powerless when dealing with some texts like in-depth analysis articles. In this paper, we make ABSC step into the LD level by proposing the Hierarchical Aspect-Oriented Framework for Long Document (HAOFL). HAOFL solves two challenges that rarely appear in short texts and normal documents. The first is the too-long input sequence that can cause the model to forget previously learned information or ignore the tailed unlearned information. The second is the unstable sentiment information of the target aspect contained in LD, which increases the difficulty for a model to draw a proper result. HAOFL constructs the data transformation module, dependency processing module, and sentiment aggregation module to solve these two challenges. Numerical experiments prove HAOFL can solve the aforementioned challenges and achieve superior performance in an effective and resource-saving way. With HAOFL, the performances of popular ABSC models on LD are improved at most 8.69% of accuracy and 11.37% of F1-score. In terms of resource-consuming, up to 82.10% of training time and 71.03% of GPU memory are saved.
Keyword:
Aspect-based sentiment analysis
Hierarchical architecture
Data transformation
Long document
