Abstract
This paper proposes a method for identifying an aspect highlighted in a sentence from a movie review, utilizing a generative language model. For example, the aspect “SFX Techniques” is identified for the sentence “The explosions in cosmic space were realistic.” Classically, aspects are commonly estimated in the field of opinion mining within product reviews with classification or extraction approaches. However, because the aspects of movie reviews are diverse and innumerable, they cannot be listed in advance. Thus, we propose a generation-based approach using a generative language model to identify the aspect of a review sentence. We adopt T5 (Text-to-Text Transfer Transformer), a modern generative language model, providing additional pre-training and fine-tuning to reduce the training data. To verify the effectiveness of the learning techniques thus adopted, we conducted an experiment incorporating reviews of Yahoo! movies. Manual labeling of the correctness and diversity of the aspect names generated shows that our method can generates a variety of fine-grained aspect names using little training data.
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Notes
- 1.
Hugging Face Transformer: https://huggingface.co/docs/transformers/index.
- 2.
sonoisa/t5-base-japanese: https://huggingface.co/sonoisa/t5-base-japanese.
- 3.
SentenceTransformers: https://www.sbert.net/.
- 4.
Hugging Face sentence-transformers https://huggingface.co/sentence-transformers/.
- 5.
For the sake of translation and anonymization, the reviews are fictitious, as the experiment was in Japanese and uses real review sentences prepared by an individual.
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Acknowledgements
This work was supported by JSPS KAKENHI Grants Number 21H03775, 21H03774, and 22H03905.
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Ishii, T., Shoji, Y., Yamamoto, T., Ohshima, H., Fujita, S., Dürst, M.J. (2023). Generating Fine-Grained Aspect Names from Movie Review Sentences Using Generative Language Model. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_23
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