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Key sentiment sentence prediction using SVM and RNN
- LIU Ming, ZAN Hong-ying, YUAN Hui-bin
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JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2014, 49(11):
68-73.
doi:10.6040/j.issn.1671-9352.3.2014.025
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Key sentiment sentences play an important role in predicting the sentiment distribution in texts, and therefore it improves the performance after correctly judging these key sentences. After analyzing the advantages and disadvantages of the state-of-the-art approaches which are mainly based on rules and statistics, it is found that rule-based methods achieve high accuracy but with low coverage, the statistic method is quite the opposite. In this paper, a novel deep learning framework to predict sentiment distributions based on Recursive Neural Network as well as Support Vector Machine was introduced. There are sentiment features including not only grammar information such as sentiment and negative words, but also statistical information like word vector in deep learning. Meanwhile, text features like sentence pattern and position were also involved. This method combines SVM and RNN in deep learning to predict sentiment distributions in texts, which outperforms other traditional approaches. The result from COAE2014 Task 1 shows that our method achieves a MicroF1 value of 0.388, higher than the average level.
