Abstract
With the advanced tendency of big data, there are billions of text messages posted on the Internet to express peoples’ views and opinions all the time. And with the explosive growth of text information flow in social network, the topic detection and the sentiment analysis have become important researches in the field of NLP (natural language processing). There are two ways to analyze the sentiment tendency: traditional statistics method and machine learning method. At present, with the extensive application of machine learning method in NLP, various neural network models have achieved commendable results in the research of sentiment classification. However, such methods require a long period in training and learning process, and the training effect may be over fitting depended on the training set. In addition, such methods usually do not consider the vector space characteristics and lacks of the use of the word sentiment label. Standing around problem, this paper creates a sentiment classification method which combined with the feature of text vector space and the convolutional neural network method. Firstly, the words are ranked and selected according to the spatial distribution features in text information. Then the processed words are mapped into abstract vectors based on the existing dictionary resources. And the convolutional neural network is utilized to extract features of abstract vectors for the sentiment classification. This paper puts forward the relevant methods obtained superb performance in the Chinese tendency analysis evaluation (COAE 2014) data sets.
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Acknowledgement
This work is supported by the National Cryptography Development Fund of China Under Grants No. MMJJ20170112, National Key Research and Development Program of China Under Grants No. 2017YFB0802000, National Nature Science Foundation of China (Grant Nos. 61772550, 61572521, U1636114), the Natural Science Basic Research Plan in Shaanxi Province of china (Grant Nos. 2016JQ6037) and Guangxi Key Laboratory of Cryptography and Information Security (No. GCIS201610).
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Yun, W., An, W.X., Jindan, Z., Yu, C. (2019). Combining Vector Space Features and Convolution Neural Network for Text Sentiment Analysis. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_71
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