Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews
Aspect and opinion terms extraction from review texts is one of the key tasks in aspect-based sentiment analysis. In order to extract aspect and opinion terms for Indonesian hotel reviews, we adapt double embeddings feature and attention mechanism that outperform the best system at SemEval 2015 and...
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| creator | Fernando, Jordhy Khodra, Masayu Leylia Septiandri, Ali Akbar |
| description | Aspect and opinion terms extraction from review texts is one of the key tasks
in aspect-based sentiment analysis. In order to extract aspect and opinion
terms for Indonesian hotel reviews, we adapt double embeddings feature and
attention mechanism that outperform the best system at SemEval 2015 and 2016.
We conduct experiments using 4000 reviews to find the best configuration and
show the influences of double embeddings and attention mechanism toward model
performance. Using 1000 reviews for evaluation, we achieved F1-measure of 0.914
and 0.90 for aspect and opinion terms extraction in token and entity (term)
level respectively. |
| doi_str_mv | 10.48550/arxiv.1908.04899 |
| format | Article |
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in aspect-based sentiment analysis. In order to extract aspect and opinion
terms for Indonesian hotel reviews, we adapt double embeddings feature and
attention mechanism that outperform the best system at SemEval 2015 and 2016.
We conduct experiments using 4000 reviews to find the best configuration and
show the influences of double embeddings and attention mechanism toward model
performance. Using 1000 reviews for evaluation, we achieved F1-measure of 0.914
and 0.90 for aspect and opinion terms extraction in token and entity (term)
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in aspect-based sentiment analysis. In order to extract aspect and opinion
terms for Indonesian hotel reviews, we adapt double embeddings feature and
attention mechanism that outperform the best system at SemEval 2015 and 2016.
We conduct experiments using 4000 reviews to find the best configuration and
show the influences of double embeddings and attention mechanism toward model
performance. Using 1000 reviews for evaluation, we achieved F1-measure of 0.914
and 0.90 for aspect and opinion terms extraction in token and entity (term)
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in aspect-based sentiment analysis. In order to extract aspect and opinion
terms for Indonesian hotel reviews, we adapt double embeddings feature and
attention mechanism that outperform the best system at SemEval 2015 and 2016.
We conduct experiments using 4000 reviews to find the best configuration and
show the influences of double embeddings and attention mechanism toward model
performance. Using 1000 reviews for evaluation, we achieved F1-measure of 0.914
and 0.90 for aspect and opinion terms extraction in token and entity (term)
level respectively.</abstract><doi>10.48550/arxiv.1908.04899</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computation and Language |
| title | Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews |
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