Multi-sentiment Modeling with Scalable Systematic Labeled Data Generation via Word2Vec Clustering

Dhruv Mayank, Kanchana Padmanabhan, K. Pal
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引用次数: 4

Abstract

Social networks are now a primary source for news and opinions on topics ranging from sports to politics. Analyzing opinions with an associated sentiment is crucial to the success of any campaign (product, marketing, or political). However, there are two significant challenges that need to be overcome. First, social networks produce large volumes of data at high velocities. Using traditional (semi-) manual methods to gather training data is, therefore, impractical and expensive. Second, humans express more than two emotions, therefore, the typical binary good/bad or positive/negative classifiers are no longer sufficient to address the complex needs of the social marketing domain. This paper introduces a hugely scalable approach to gathering training data by using emojis as proxy for user sentiments. This paper also introduces a systematic Word2Vec based clustering method to generate emoji clusters that arguably represent different human emotions (multi-sentiment). Finally, this paper also introduces a threshold-based formulation to predicting one or two class labels (multi-label) for a given document. Our scalable multi-sentiment multi-label model produces a cross-validation accuracy of 71.55% (± 0.22%). To compare against other models in the literature, we also trained a binary (positive vs. negative) classifier. It produces a cross-validation accuracy of 84.95% (± 0.17%), which is arguably better than several results reported in literature thus far.
基于Word2Vec聚类的可扩展系统标记数据生成的多情感建模
从体育到政治,社交网络现在是新闻和观点的主要来源。分析带有相关情绪的观点对于任何活动(产品、营销或政治活动)的成功都至关重要。然而,有两个重大挑战需要克服。首先,社交网络以高速产生大量数据。因此,使用传统的(半)手工方法来收集训练数据是不切实际和昂贵的。其次,人类表达两种以上的情绪,因此,典型的二元好/坏或积极/消极分类器不再足以解决社会营销领域的复杂需求。本文介绍了一种通过使用表情符号作为用户情感的代理来收集训练数据的巨大可扩展方法。本文还介绍了一种系统的基于Word2Vec的聚类方法来生成可以代表不同人类情感(多情感)的表情符号聚类。最后,本文还介绍了一个基于阈值的公式来预测给定文档的一个或两个类标签(多标签)。我们的可扩展多情感多标签模型产生了71.55%(±0.22%)的交叉验证精度。为了与文献中的其他模型进行比较,我们还训练了一个二元(正与负)分类器。它产生了84.95%(±0.17%)的交叉验证精度,可以说比迄今为止文献报道的几个结果要好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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