测试训练>> Sentiment-Analysis---Binary-Classification>> 返回
项目作者： Anmol-Kale

项目描述 ：
The most common use of Sentiment Analysis is this of classifying a text to a class. Depending on the dataset and the reason, Sentiment Classification can be binary (positive or negative) or multi-class (3 or more classes) problem.
高级语言： Jupyter Notebook
项目地址: git://github.com/Anmol-Kale/Sentiment-Analysis---Binary-Classification.git


Sentiment-Analysis—-Binary-Classification

The most common use of Sentiment Analysis is this of classifying a text to a class. Depending on the dataset and the reason, Sentiment Classification can be binary (positive or negative) or multi-class (3 or more classes) problem.

Pre-processing

An initial step in text and sentiment classification is pre-processing. A significant amount of techniques is applied to data in order to reduce the noise of text, reduce dimensionality, and assist in the improvement of classification effectiveness. The most popular techniques include:

  1. Remove numbers
  2. Stemming
  3. Part of speech tagging
  4. Remove punctuation
  5. Lowercase
  6. Remove stopwords