Deep learning and sub-tree mining for document level sentiment classification
Recently, with the development of the online social network, sentiment classification (SC) which determines opinions of people is a significant task in natural language processing. In this research, we propose a model which combines deep learning and sub-tree mining to resolve sentiment classification problem. Stanford Parser is used to extract the relation from the beginning to the end of the sentences and each sentence is represented as a tree. Afterwards, FindBestSub-tree algorithm with sub-tree mining technique eliminates outliers in the dataset. Then, the order of the words in a sentence changes according to DFS (Depth First Search) from a tree after outlier removal phase. Finally, the association between all words in a sentence and all sentences in a document is captured by LSTM and GRNN, respectively. Document sentiment classification experiment is conducted on multi-domain sentiment dataset. The elimination of outliers leads to higher performance in this model1. In our experiment, the proposed method achieves improvements in term of accuracy in a range of 0.14% - 6.93% over LSTM + GRNN model.