Kun Ma  Kun Ma


Sentiment classification is a hot research direction at present, but most research is based on balanced data sets. In real life, the sample is impossible to balance. For sentiment analysis of unbalanced data, we not only need to pay attention to the overall classification performance, but also need to care about the classification performance of a few classes. How to improve the recognition rate of a few types of samples while improving the overall recognition rate has become a research hotspot. Aiming at this problem, this paper proposes a model based on ensemble learning, extracts features by TF-IDF+SVD, and integrates five base classifiers by stacking to sentiment classification. The experimental results show that it can be more effective in emotional classification on unbalanced data sets than other methods.

Code & Data

Data for traing and testing

Description: label ,text
1:positive emotion
0:negative emotion

[1] https://github.com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/simplifyweibo_4_moods/intro.ipynb
[2] http://www.datatang.com/data /11936

Download: https://pan.baidu.com/s/1pL1-zaUgeVOT5k5BoHWLSw code: hham



Jidong Duan, Kun Ma, and Runyuan Sun, Unbalanced data sentiment classification method based on ensemble learning, Proceedings of 2019 2nd International Conference on Big Data Technologies (ICBDT2019),


  title={Unbalanced data sentiment classification method based on ensemble learning},
  author={Duan, Jidong and Ma, Kun and Sun, Runyuan},
  booktitle={Proceedings of the 2nd International Conference on Big Data Technologies},