iSA: A fast, scalable and accurate algorithm for sentiment analysis of social media content
iSA: A fast, scalable and accurate algorithm for sentiment analysis of social media content
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DOI：
10.1016/j.ins.2016.05.052
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发表时间：
2016-11-01
影响因子：
8.1
通讯作者：
Iacus, Stefano Maria
中科院分区：
文献类型：
--
作者：
Ceron, Andrea;Curini, Luigi;Iacus, Stefano Maria
我们提出了伊萨（集成情感分析），一种新的算法，专为社交网络和Web 2.0领域（Twitter，博客等）。意见分析，即针对数字环境开发的，其特征在于与信息量相比噪声丰富。伊萨不是先进行单独分类，然后再汇总预测值，而是直接估计意见的汇总分布。基于监督手工编码而不是NLP技术或本体词典，伊萨是一种语言不可知的算法（基于人类编码者的能力）。伊萨利用降维方法，使其可扩展，快速，内存效率，稳定和统计准确。由于伊萨的稳定性，意见的交叉制表是可能的。通过实证分析，它将显示当伊萨优于机器学习技术的个人分类（如SVM，随机森林等），以及唯一的其他替代聚合情感分析称为自述。(C)2016 Elsevier Inc. All rights reserved.
We present iSA (integrated sentiment analysis), a novel algorithm designed for social networks and Web 2.0 sphere (Twitter, blogs, etc.) opinion analysis, i.e. developed for the digital environments characterized by abundance of noise compared to the amount of information. Instead of performing an individual classification and then aggregate the predicted values, iSA directly estimates the aggregated distribution of opinions. Based on supervised hand-coding rather than NLP techniques or ontological dictionaries, iSA is a language-agnostic algorithm (based on human coders' abilities). iSA exploits a dimensionality reduction approach which makes it scalable, fast, memory efficient, stable and statistically accurate. The cross-tabulation of opinions is possible with iSA thanks to its stability. Through empirical analysis it will be shown when iSA outperforms machine learning techniques of individual classification (e.g. SVM, Random Forests, etc) as well as the only other alternative for aggregated sentiment analysis known as ReadMe. (C) 2016 Elsevier Inc. All rights reserved.