Study the sentiment analysis for English weblog text.
文中对英文博客文本的情感分析进行了研究。
It USES so-called "sentiment analysis" software to assess text.
这个项目利用“情绪分析”软件来分析文字。
In the meantime, here's a previous blog post on the use of Sentiment Analysis for early warning.
同时，这里有一个以前的博客张贴出关于情绪分析应用于早期预警的文章。
Educators could see the data and sentiment analysis around how a community feels about its local schools.
教育者可以从数据和感情分析中了解到小区对当地的学校是怎样的感觉。
It used the word as the unit for feature selection and add the pretreatment in text sentiment analysis process.
提出了以词为单位进行特征的选取，并在文本的情感分析中加入了预处理过程。
The other major challenge, says Bollen, is that sentiment analysis only provides a scrape of potentially useful information.
Bollen教授还说，另外一个巨大的挑战就是情绪分析仅能提供一部分潜在可用的信息。
The situation is also leading Google to work on making its "sentiment analysis" search system more of a factor in results ranking.
这种情况也导致GOOGLE努力使它的“情感分析”搜索系统在搜索排名中占更多的因子。
Sentiment analysis is a branch of natural language processing (NLP) that seeks to quantify positive vs negative perceptions; akin to "tone" analysis.
情绪分析是自然语言处理的一个分支(NLP)，旨在量化积极和消极感知力；类似于“音调”分析。
Sentiment analysis can be used to determine insights into stakeholder (customers, employees, regulators etc. ) sentiment from massive, complex unstructured data.
情感分析用来从大量和复杂的非结构数据中提取股东（客户、雇员、监管者等）情感。
The influence and conformity indices of every node were then iteratively calculated according to text sentiment analysis, and the information cascades and important early participants were extracted.
通过文本情感分析和信息级联关系迭代计算节点的影响力指数和从众指数，提取微博信息早期重要参与者。
Some of the most interesting and active fields related to text analysis are scientific research and sentiment detection.
与文本分析相关的最有趣、最活跃的领域是科学研究和情感探测。
In sentiment detection, the analysis engine tries to determine how positive or negative someone feels about a given product or service.
在情感探测中，分析引擎尝试确定某人对给定产品或服务的积极和消极感觉。
Analysis: Review activity for frequency (the rate of mentions), the state of sentiment allocation, traffic, as well as the size of connections (friends, followers, fans, etc.).
分析：积极回顾活动频率(被提及的比率)，情感分配结构，浏览量，以及链接的规模(朋友，追随者，粉丝等)。
It also makes it possible to use third-party assets, free or commercial, to do sophisticated analysis such as sentiment detection on your Warehouse data.
此外，这也使得使用免费或商业的第三方资产进行高级分析成为可能，比如对Warehouse数据执行情感探测。
It also makes it possible to use third-party assets, free or commercial, to do sophisticated analysis such as sentiment detection on your Warehouse data.
此外，这也使得使用免费或商业的第三方资产进行高级分析成为可能，比如对Warehouse数据执行情感探测。
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