基于机器学习的文本情感倾向性分析

为实现对网络上电影评论的情感倾向性分析，通过对电影影评数据进行爬取，获取热门的电影评论，利用文本预处理和机器学习算法，完成对数据的训练以及测试，最终构建最优情感分类模型。实验结果表明，在所有词与双词结合并进行jieba的TF-IDF及卡方统计的特征提取下，得到的效果较好，BP神经网络以及多项式贝叶斯算法比较适用于这类文本的分析，尤其以BP神经网络的效果最佳，准确率达到86.2%。

Analysis of Text Sentiment Orientation Based on Machine Learning

In order to realize the emotional orientation analysis of movie reviews on the Internet, the film review data is crawled to obtain popular movie reviews, and text preprocessing and machine learning algorithms are used to complete the training and testing of the data, and finally the most superior sentiment classification model is constructed. The experimental results show that under the combination of all words and double words and the feature extraction of jieba’s TF-IDF and Chi-square statistics, the BP neural network and polynomial Bayesian algorithm are more suitable for the analysis of this kind of text, especially BP neural network is the best, the accuracy rate reaches 86.2%.