Papers › Review highlights: opinion mining on reviews: a hybrid model for rule selection in...
Review highlights: opinion mining on reviews: a hybrid model for rule selection in aspect extraction
Amit Kushwaha, Shubham Chaudhary
This paper proposes a methodology to extract key insights from user generated reviews. This work is based on Aspect Based Sentiment Analysis (ABSA) which predicts the sentiment of aspects mentioned in the text documents. The extracted aspects are fine-grained for the presentation form known as Review Highlights. The syntactic approach for extraction process suffers from the overlapping chunking rules which result in noise extraction. We introduce a hybrid technique which combines machine learning and rule based model. A multi-label classifier identifies the effective rules which efficiently parse aspects and opinions from texts. This selection of rules reduce the amount of noise in extraction tasks. This is a novel attempt to learn syntactic rule fitness from a corpus using machine learning for accurate aspect extraction. As the model learns the syntactic rule prediction from the corpus, it makes the extraction method domain independent. It also allows studying the quality of syntactic rules in a different corpus.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Extract Aspect | SemEval 2015 Task 12 | EliXa | F1 score | 0.70 | #1 of 2 | Archive leaderboard | report |
| Extract Aspect | SemEval 2015 Task 12 | Syntactic Grammar Model | F1 score | 0.63 | #2 of 2 | Archive leaderboard | report |
| Extract aspect-polarity tuple | SemEval 2015 Task 12 | Syntactic Grammer Model | F1 score | 0.51 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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