预测自由职业者平台上自由职业者交易金额的改进方法:基于情感分析

Hongbin Zhang
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摘要

自由职业是一种劳动安排,独立的自由职业者利用自己的自由支配时间完成临时任务,通常是单一性质的任务,目的是获得报酬。本研究提出了一种利用文本情感分析预测自由职业者交易量的增强方法。首先,我们选择了一个独特的标记为 "自由职业者情感"(Freelancer Sentiment)的特征来概括自由职业者的积极或消极情感取向。随后,应用 Naive Bayes 算法处理来自自由职业者平台的文本数据,最终开发出一个计算单词情感值的模型。该模型有助于准确计算与情感词相关的情感值。最后,利用词频-反向文档频率(TF-IDF)算法构建文本情感值计算模型,从而准确计算出自由职业者的情感值。五种常用预测模型的对比实验结果表明,与不包含自由职业者情感特征的模型相比,包含自由职业者情感特征的模型的均方误差(MSE)显著降低了 6%-11%。本研究包含理论探索和实践意义。首先,本文提出的从文本数据中提取特征建立预测模型的方法,为今后加强自由职业者平台,尤其是依赖非结构化数据的自由职业者平台的预测建模提供了有价值的参考。其次,结合与自由职业者相关的文本情感价值特征,可以显著提高预测交易金额的准确性。第三,单词和文本情感值的计算采用了一系列针对自由职业者平台文本数据特定特征的算法。这种方法对于提高特征值计算的准确性非常重要。
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An enhancement methodology for predicting transaction amounts for freelancers on freelance platforms: Based on sentiment analysis
Freelancing is a type of labor arrangement in which independent freelancers use their discretionary time to perform ad hoc tasks, usually of a single nature, with the aim of getting paid. This study proposes an augmented method for predicting the amount of freelance transactions using textual sentiment analysis. First, a unique feature labeled Freelancer Sentiment was selected to summarize the positive or negative sentiment orientation of freelancers. Subsequently, Naive Bayes algorithm is applied to process the text data from the freelancers platform to finally develop a Model for Computing Word Sentiment Values. The model helps to accurately calculate the sentiment values associated with emotional words. Finally, the Word Frequency-Inverse Document Frequency (TF-IDF) algorithm is used to construct the Text Sentiment Value Calculation Model, so as to accurately calculate the sentiment values of freelancers. The results of the comparison experiments of the five commonly used prediction models show that the mean squared error (MSE) of the model that includes the freelancer sentiment feature is significantly reduced by 6%-11% compared with the model that does not include the freelancer sentiment feature. This study contains theoretical explorations and practical implications. First, the proposed approach of extracting features from textual data to build predictive models provides a valuable reference for future enhancement of predictive modeling on freelance platforms, especially those that rely on unstructured data. Second, incorporating textual sentiment value features relevant to freelancers can significantly improve the accuracy of predicting transaction amounts. Third, the calculation of word and text sentiment values employs a series of algorithms that target specific features of the freelancer platforms text data. This approach is important for improving the accuracy of feature value calculation.
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