Abstract
Opinion mining plays an important role in public opinion monitoring, commodity evaluation, government governance, and other areas. One of the basic tasks of opinion mining is to extract the expression elements, which can be further divided into direct subjective expression and expressive subjective expression. For the task of subjective expression extraction, the methods based on neural network can learn features automatically without exhaustive feature engineering and have been proved to be efficient for opinion mining. Constructing adequate input vector which can encode sufficient information is a challenge of neural network-based approach. To cope with this problem, a novel representation method that combines the different features with word vectors is proposed. Then, we use neural network and conditional random field to train and predict the expressions and carry out comparative experiments on different methods and features combinations. Experimental results show the performance of the proposed model, and the F value outperforms other methods in comparative experimental dataset. Our work can provide hint for further research on opinion expression extraction.
Highlights
Information retrieved from social network includes the objective facts of events and contains the opinions expressed by the people, organizations, or the media
Word level methods tend to focus on the attitudes, opinions, and sentiment polarity contained in the words themselves. e common mechanisms are to search approximate words heuristically according to the existing opinion word seeds, expand the original opinion lexicon, and judge view sentences according to the vocabulary
The combination of fine-tuning pretraining word vectors and part-of-speech features is adequate in the recognition of direct subjective expressions (DSE) subjective expression elements. e combination of lexical and entity features is similar to that of individual lexical features, while named entity features have little impact on DSE recognition
Summary
Mingyong Yin ,1,2 Haizhou Wang ,3,4 Xingshu Chen ,3,4 Hong Yan, and Rui Tang. Opinion mining plays an important role in public opinion monitoring, commodity evaluation, government governance, and other areas. One of the basic tasks of opinion mining is to extract the expression elements, which can be further divided into direct subjective expression and expressive subjective expression. For the task of subjective expression extraction, the methods based on neural network can learn features automatically without exhaustive feature engineering and have been proved to be efficient for opinion mining. Constructing adequate input vector which can encode sufficient information is a challenge of neural networkbased approach. To cope with this problem, a novel representation method that combines the different features with word vectors is proposed. En, we use neural network and conditional random field to train and predict the expressions and carry out comparative experiments on different methods and features combinations. Our work can provide hint for further research on opinion expression extraction
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