BeeAE: effective aspect term extraction with artificial bee colony

• Published in: The Journal of supercomputing Vol. 78; no. 16; pp. 17969 - 17991
• Main Authors: Shi, Jingli, Li, Weihua, Bai, Quan, Ito, Takayuki
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2022; Springer Nature B.V
• Subjects: Ablation; Bees; Compilers; Computer Science; Context; Data mining; Datasets; Feature extraction; Feature selection; Interpreters; Linguistics; Processor Architectures; Programming Languages; Sentiment analysis; Swarm intelligence; Aspect term extraction; Feature selection; Artificial bee colony; Linguistic feature
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-022-04579-0

Aspect terms are opinion targets for people to express and understand opinions in reviews. Aspect terms extraction is an essential subtask in aspect-level sentiment analysis. To extract aspect terms from a sentence, existing methods mainly focus on context features generated by pre-trained models. However, these models either neglect the crucial implicit linguistic features, e.g., post-of-tag, head, and head dependency, or fail to explore sufficient valuable features for aspect term extraction, which lead to the deficiency in aspect term extraction task. To address the challenges, in this paper, we propose a novel and effective framework for aspect term extraction by integrating both contextual and linguistic features with the artificial bee colony-based feature selection method. Firstly, a novel variant of artificial bee colony is designed to identify the most valuable linguistic features to reduce the high sparsity and dimensionality of the raw dataset. Next, the selected features and context embeddings are integrated to improve the performance of aspect extraction. Finally, extensive experiments are conducted on real-world datasets, and the results exhibit that our proposed framework can outperform the competitive baselines. Compared with the latest baselines, the proposed framework achieves the comparatively higher F 1 scores of 80.7%, 84.7%, 72.2%, and 74.8% on the four groups of datasets. Furthermore, the ablation study shows that the proposed method with the designed feature selection module significantly outperforms the method with the original artificial bee colony, having 4.15%, 4.4%, 4.4%, and 3.2% improvements in F 1 score on all the four datasets, respectively.