Abstract
Sentiment analysis is widely used in product reviews, movie reviews, and micro-blog reviews. Micro-blog review is different from general commodity or movie reviews, which often contains the user’s randomness and lots of network terms. So the micro-blog reviews emotional analysis is not a small challenge. Network terms generally express strong emotions or the user’s point of view, the traditional bag words model and machine learning method do not use the network terms features. In the face of ever-changing micro-blog reviews manifestations, forecast accuracy may be affected. Therefore, in this paper our study focuses on the micro-blog emotional analysis through the extended network terms features and integration with other features. We are taking experiments to compare prediction performance under the different feature fusions, to find out which feature fusion can get the best results. Our results show that by the extended network term feature integration with other features ways to improve the accuracy of predictions, especially some of the most popular micro-blog reviews.
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Ye, F. (2018). Sentiment Classification for Chinese Micro-blog Based on the Extension of Network Terms Feature. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 554. Springer, Singapore. https://doi.org/10.1007/978-981-10-3773-3_22
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DOI: https://doi.org/10.1007/978-981-10-3773-3_22
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