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Research and Realization of Internet Public Opinion Analysis Based on Improved TF - IDF Algorithm

机译：基于改进的TF-IDF算法的互联网舆情分析研究与实现。

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摘要

At present, the main methods of network public opinion analysis include data acquisition, information extraction, spam filtering, similarity clustering, emotion analysis, positive and negative judgment. The extraction of data information based on text characteristic extraction is a key step. In this paper, the traditional TF-IDF method is improved by introducing the part of speech weight coefficient and the position weight (span weight) of the characteristic word. The experimental results show that the improved method can effectively improve the clustering effect of the characteristic words, and is better able to reflect the textual characteristics. Applying it to the public opinion analysis system has achieved good results.
机译：目前，网络舆情分析的主要方法包括数据采集，信息提取，垃圾邮件过滤，相似度聚类，情感分析，正面和负面判断。基于文本特征提取的数据信息提取是关键步骤。本文通过引入语音权重系数的一部分和特征词的位置权重（跨度权重）对传统的TF-IDF方法进行了改进。实验结果表明，改进后的方法可以有效地提高特征词的聚类效果，更好地体现文字特征。将其应用于民意分析系统取得了良好的效果。

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