Improving BERT Performance for Aspect-Based Sentiment Analysis

ICNLSP 2021  ·  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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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Aspect Extraction SemEval 2014 Task 4 Sub Task 2 PH-SUM Laptop (F1) 86.09 # 2
Mean F1 (Laptop + Restaurant) 84.215 # 1
Restaurant (F1) 82.34 # 4
Aspect-Based Sentiment Analysis SemEval 2014 Task 4 Sub Task 2 PH-SUM Restaurant (Acc) 86.37 # 8
Laptop (Acc) 79.55 # 10
Mean Acc (Restaurant + Laptop) 82.96 # 8

Methods