Text Mining and Analytics（1）

最新推荐文章于 2024-10-08 06:40:01 发布

原创最新推荐文章于 2024-10-08 06:40:01 发布 · 533 阅读

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本文探讨了数据挖掘中语义网络与文本挖掘的关系，强调了文本数据的时间和空间上下文分析的重要性，并讨论了如何利用非文本数据为文本分析提供更丰富的视角。

Coursera上的视频做笔记学习

前言：
其实，semantic network与text mining是紧密相关的。
Semantic network
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1、so this means the data mining problem is basically taking a lot of data as input and giving actionale knowledge as output
2、在data mining 内部，又有针对不同data类型的mining

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The main goal of test mining is actually to revert this process of generating text data

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the context can provide intersting angles for analyzing text data
For example, we might partion text data into different time periods
because of the availability of the time.Now we can analyze text data in each time period and then make a comparison.
Similarly we can partion text data based on locations or any meta data that’s associated to form interesting comparisons in areas.
So in this sense, non-text data can actually provide interesting angles or perspectives for text data analysis.And it can help us make context-sensitive
analysis of content or the language usage or the opinions about the observer or the authors of text data.We could analyze the sentiment in different contexts