当前位置: X-MOL 学术Artif. Intell. Rev. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Systematic reviews in sentiment analysis: a tertiary study
Artificial Intelligence Review ( IF 10.7 ) Pub Date : 2021-03-03 , DOI: 10.1007/s10462-021-09973-3
Alexander Ligthart , Cagatay Catal , Bedir Tekinerdogan

With advanced digitalisation, we can observe a massive increase of user-generated content on the web that provides opinions of people on different subjects. Sentiment analysis is the computational study of analysing people's feelings and opinions for an entity. The field of sentiment analysis has been the topic of extensive research in the past decades. In this paper, we present the results of a tertiary study, which aims to investigate the current state of the research in this field by synthesizing the results of published secondary studies (i.e., systematic literature review and systematic mapping study) on sentiment analysis. This tertiary study follows the guidelines of systematic literature reviews (SLR) and covers only secondary studies. The outcome of this tertiary study provides a comprehensive overview of the key topics and the different approaches for a variety of tasks in sentiment analysis. Different features, algorithms, and datasets used in sentiment analysis models are mapped. Challenges and open problems are identified that can help to identify points that require research efforts in sentiment analysis. In addition to the tertiary study, we also identified recent 112 deep learning-based sentiment analysis papers and categorized them based on the applied deep learning algorithms. According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis.



中文翻译:

情绪分析中的系统评价:第三级研究

借助先进的数字化技术,我们可以观察到网络上用户生成的内容大量增加,从而提供了人们对不同主题的看法。情感分析是分析人们对实体的感觉和观点的计算研究。在过去的几十年中,情感分析领域一直是广泛研究的主题。在本文中,我们介绍了第三次研究的结果,旨在通过综合已发表的关于情感分析的第二次研究(即系统的文献综述和系统的作图研究)的结果来调查该领域的研究现状。这项第三级研究遵循系统文献综述(SLR)的指南,仅涵盖第二级研究。这项第三级研究的结果提供了对情感分析中关键主题和各种任务的不同方法的全面概述。映射了情感分析模型中使用的不同功能,算法和数据集。识别出挑战和未解决的问题,可以帮助识别需要在情感分析中进行研究的方面。除第三级研究外,我们还确定了112篇基于深度学习的情感分析论文,并根据应用的深度学习算法对它们进行了分类。根据此分析,LSTM和CNN算法是用于情感分析的最常用的深度学习算法。识别出挑战和未解决的问题,可以帮助识别需要在情感分析中进行研究的方面。除第三级研究外,我们还确定了112篇基于深度学习的情感分析论文,并根据应用的深度学习算法对它们进行了分类。根据此分析,LSTM和CNN算法是用于情感分析的最常用的深度学习算法。识别出挑战和未解决的问题,可以帮助识别需要在情感分析中进行研究的方面。除第三级研究外,我们还确定了112篇基于深度学习的情感分析论文,并根据应用的深度学习算法对它们进行了分类。根据此分析,LSTM和CNN算法是用于情感分析的最常用的深度学习算法。

更新日期:2021-03-03
down
wechat
bug