Research on Machine Learning Method-Based Combination Forecasting Model and its Application
A novel combination forecasting model is presented in this paper, which combines single ones based on machine learning. The model has been applied to the prediction of five cities election in Taiwan with combining the exposure rate and the approval rate, which obtains good results. The exposure rate is the frequency of a candidates appearances in the news and approval rate is the proportion of the positive information of a candidate. And the polarity of a review is predicted by sentiment classification based on machine learning techniques. A novel method of feature extraction is used in sentiment classification, which makes the classifier effectively assign the review a type of polarity. Meanwhile, this paper proposes a method of feature clustering and extending based on the synonym dictionary, which obviously reduces the dimension of feature vector and improve the F-score of sentiment classification.
sentiment classification feature extraction feature clustering combination forecasting model
Zhenlong Sun Conghui Zhu Bing Xu Sheng Li
MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin, China
国际会议
上海
英文
1274-1279
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）