An improved semi-supervised dimensionality reduction using feature weighting: Application to sentiment analysis
作者：
Highlights：
• A semi-supervised feature extraction combines with feature weighting is proposed.
• Feature weighting considers both co-occurrence of terms and label of documents.
• The polarity scores defined in SentiWordNet are reflected in the feature weights.
• Six datasets are used to validate the enhanced performance of the proposed method.
摘要
•A semi-supervised feature extraction combines with feature weighting is proposed.•Feature weighting considers both co-occurrence of terms and label of documents.•The polarity scores defined in SentiWordNet are reflected in the feature weights.•Six datasets are used to validate the enhanced performance of the proposed method.
论文关键词：Semi-supervised dimensionality reduction,Feature weighting,Feature extraction,Sentiment analysis,Natural language processing (NLP)
论文评审过程：Received 28 February 2017, Revised 18 May 2018, Accepted 19 May 2018, Available online 20 May 2018, Version of Record 25 May 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.05.023
