.png){kind=icon}
返回
Performance analysis of aspect-level sentiment classification task based on different deep learning models
.png){kind=icon}
OA
AI
DOI：10.7717/peerj-cs.1578.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Aspect-level sentiment classification task (ASCT) is a natural language processing task that aims to correctly identify specific aspects and determine their sentiment polarity from a given target sentence. Deep learning models have been proven to be effective in aspect-based sentiment classification tasks, and the mainstream Aspect level sentiment classification (ASC) models currently constructed generally assume that the training and test datasets are Gaussian distribution (e.g., the same language). Once the data distribution changes, the ASC model must be retrained on the new distribution data to achieve good performance. However, acquiring a large amount of labeled data again typically requires a lot of manpower and money, which seems unlikely, especially for the ASC task, as it requires aspect-level annotation. This article analyzes the performance of sequence-based models, graph-based convolutional neural networks, and pre-training language models on the aspect-level sentiment classification task using two sets of comment datasets in Chinese and English, from four perspectives: classification performance, performance with different aspect numbers, specific case performance, and computational cost. In this article, we design a state-of-the-art ASCbased classification method and conduct a systematic study on eight public standard English and Chinese datasets with various commonly used assessment measures that provide directions for cross-language migration. Finally, we discuss the limitations of the study as well as future research directions.
Keyword:
Deep learning
Aspect-based sentiment classification
Comment datasets
Perfor-mance analysis
Neural network models
Pre-trained language models
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.5
论文数:
3.4K
被引数:
6.9K
机构
暂无机构信息
引用论文
Femtosecond lasers in fluid-inclusion analysis: overcoming metastable phase states流体包裹体分析中的飞秒激光: 克服亚稳态相态
.png){kind=icon}
PREAI
分享收藏Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research在冰山一角之下: 情感分析研究的当前挑战和新方向OAAI
OAAI分享收藏
Cobalt and Copper Composite Oxides as Efficient Catalysts for Preferential Oxidation of CO in H2-Rich Stream钴和铜复合氧化物作为H2-Rich流中CO优先氧化的有效催化剂
PREAI
分享收藏Issues and Challenges of Aspect-based Sentiment Analysis: A Comprehensive Survey基于方面的情感分析的问题与挑战: 综合综述
PREAI
分享收藏
查看更多内容