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一种自然语言处理的算法 用于情感分析 将一篇文章以关键字的角度来区别正面负面 已经添加了中文词典，可以对中文使用（请对语料先分词）-A natural language processing algorithm for sentiment analysis will be an article with keywords to distinguish between the positive and negative perspective has been added Chinese dict
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对企业的新闻动态，通过情感分析的方法，利用matlab编程实现，确定新闻的好坏程度-News of the companies, by the method of sentiment analysis, the use of matlab programming, to determine the extent of bad news
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介绍了情感分析与数据挖掘的相关算法，大数据智能-Introduces the sentiment analysis algorithms and data mining
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最近提出的利用互信息算法来计算词语权重，对细粒度情感分析是十分重要的一步。-Recently proposed mutual information algorithm to calculate the weight of the words, fine-grained sentiment analysis is a very important step.
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图片情感分析模型，基于卷积神经网络，以颜色特征为依据进行情感分类，图片情感极性分为积极和消极两类。(The model can extract the hue, brightness, contrast and other information from a picture to represent the emotional polarity of the image. The image sentiment analysis model is using convolution neura
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卷积神经网络的源代码，用于微博博文文本情感分析的三分类。(Convolutional neural network source code for micro-blog Bowen text sentiment analysis of the three categories.)
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卷积神经网络的源代码，用于微博博文情感分析的三分类。(Convolutional neural network source code, for micro-blog Bowen sentiment analysis of the three categories.)
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07python语言用于情感分析：基于电商产品数据的情感分析(Sentiment analysis based on 07python language: sentiment analysis based on electricity supplier product data)
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对文本的情感分析的划分和测试，相似度进行判断(The classification and test of text sentiment analysis, and the judgement of similarity.)
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实现LSTM情感分析，中文，用到结巴分词，LSTM模型，环境python3.0(Achieve LSTM sentiment analysis, Chinese, use stuttering participle, LSTM model, environment python3.0)
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文本情感分析，对评论进行积极向以及消极向计分，判断评论文本情感极性。(sentiment analysis,comment score.)
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该MATLAB例程是基于演讲的语音韵律分析工具，用于情感识别(The MATLAB routine is a sentiment analysis tool based on a speech prosodic analysis and used for sentiment recognition.)
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读取csv或xlsx文件后对文本信息进行NLP情感分析并计算出情感的得分，越接近1越积极，越接近-1越消极(After reading the CSV or xlsx file, NLP sentiment analysis is performed on the text information and the score of sentiment is calculated. The closer to 1, the more positive, the more close to - 1,
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