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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Hybrid sentiment classification on twitter aspect-based sentiment analysis
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Hybrid sentiment classification on twitter aspect-based sentiment analysis

机译：杂交情绪分类对Twitter宽边的情感分析

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摘要

Social media sites and applications, including Facebook, YouTube, Twitter and blogs, have become major social media attractions today. The huge amount of information from this medium has become an attractive resource for organisations to monitor the opinions of users, and therefore, it is receiving a lot of attention in the field of sentiment analysis. Early work on sentiment analysis approached this problem at a document-level, where the overall sentiment was identified, rather than the details of the sentiment. This research took into account the use of an aspect-based sentiment analysis on Twitter in order to perform a finer-grained analysis. A new hybrid sentiment classification for Twitter is proposed by embedding a feature selection method. A comparison of the accuracy of the classification by the principal component analysis (PCA), latent semantic analysis (LSA), and random projection (RP) feature selection methods are presented in this paper. Furthermore, the hybrid sentiment classification was validated using Twitter datasets to represent different domains, and the evaluation with different classification algorithms also demonstrated that the new hybrid approach produced meaningful results. The implementations showed that the new hybrid sentiment classification was able to improve the accuracy performance from the existing baseline sentiment classification methods by 76.55, 71.62 and 74.24%, respectively.
机译：社交媒体网站和应用程序，包括Facebook，YouTube，Twitter和博客，已成为今天的主要社交媒体景点。来自此媒体的大量信息已成为组织监控用户意见的有吸引力的资源，因此，它在情感分析领域接受了很多关注。早期的情感分析工作在文件级接近了这个问题，其中确定了整体情绪，而不是情感的细节。本研究考虑了在Twitter上使用基于方面的情感分析，以进行更精细的粒度分析。通过嵌入特征选择方法提出了对Twitter进行新的混合情感分类。本文介绍了主成分分析（PCA），潜在语义分析（LSA）和随机投影（RP）特征选择方法的分类精度的比较。此外，使用Twitter数据集验证了混合情绪分类，以表示不同的域，并使用不同分类算法的评估还证明了新的混合方法产生了有意义的结果。实施表明，新的混合情感分类分别能够将现有基线情绪分类方法的准确性绩效分别提高76.55,71.62和74.24％。

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