Papers › Improving BERT Performance for Aspect-Based Sentiment Analysis
Improving BERT Performance for Aspect-Based Sentiment Analysis
Akbar Karimi, Leonardo Rossi, Andrea Prati
Aspect-Based Sentiment Analysis (ABSA) studies the consumer opinion on the market products. It involves examining the type of sentiments as well as sentiment targets expressed in product reviews. Analyzing the language used in a review is a difficult task that requires a deep understanding of the language. In recent years, deep language models, such as BERT \cite{devlin2019bert}, have shown great progress in this regard. In this work, we propose two simple modules called Parallel Aggregation and Hierarchical Aggregation to be utilized on top of BERT for two main ABSA tasks namely Aspect Extraction (AE) and Aspect Sentiment Classification (ASC) in order to improve the model's performance. We show that applying the proposed models eliminates the need for further training of the BERT model. The source code is available on the Web for further research and reproduction of the results.
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Code
Syntology Ran 5 of 11 code samples harvested from 1 repository linked to this paper; 6 have no recorded run. Of those that ran: 2 ran · honoured contract; 3 ran · our draft was wrong.
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Code Syntology ran Syntology
11 samples harvested; 5 ran; 2 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 0 of the 11 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Aspect Extraction | SemEval-2014 Task-4 | PH-SUM | Laptop (F1) | 86.09 | #3 of 6 | Archive leaderboard | report |
| Aspect Extraction | SemEval-2014 Task-4 | PH-SUM | Mean F1 (Laptop + Restaurant) | 84.215 | #3 of 6 | Archive leaderboard | report |
| Aspect Extraction | SemEval-2014 Task-4 | PH-SUM | Restaurant (F1) | 82.34 | #3 of 6 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | PH-SUM | Laptop (Acc) | 79.55 | #12 of 48 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | PH-SUM | Mean Acc (Restaurant + Laptop) | 82.96 | #12 of 48 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | PH-SUM | Restaurant (Acc) | 86.37 | #12 of 48 | 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.
Methods
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