综述:情感分析的预处理技术和数据增强

Q1 Mathematics
Huu-Thanh Duong, Tram-Anh Nguyen-Thi
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引用次数: 43

摘要

在文献中,基于机器学习的情感分析研究通常是监督学习,在某些领域必须有足够大的预标记数据集。显然,这个任务冗长、昂贵且耗时,而且很难处理看不见的数据。本文探讨了数据集有限的越南语情感分析的半监督学习。本文总结了用于数据清理和归一化、否定处理、强化处理等方面的预处理技术。此外,还提出了数据增强技术,即在不需要用户干预的情况下从原始数据中生成新数据以丰富训练数据。在实验中,我们已经完成了各个方面,并获得了有竞争力的结果,这可能会激发下一个命题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A review: preprocessing techniques and data augmentation for sentiment analysis
In literature, the machine learning-based studies of sentiment analysis are usually supervised learning which must have pre-labeled datasets to be large enough in certain domains. Obviously, this task is tedious, expensive and time-consuming to build, and hard to handle unseen data. This paper has approached semi-supervised learning for Vietnamese sentiment analysis which has limited datasets. We have summarized many preprocessing techniques which were performed to clean and normalize data, negation handling, intensification handling to improve the performances. Moreover, data augmentation techniques, which generate new data from the original data to enrich training data without user intervention, have also been presented. In experiments, we have performed various aspects and obtained competitive results which may motivate the next propositions.
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来源期刊
Computational Social Networks
Computational Social Networks Mathematics-Modeling and Simulation
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.
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