MATEC Web Conf.
Volume 189, 20182018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018)
|Number of page(s)||6|
|Section||Bio & Human Engineering|
|Published online||10 August 2018|
Sentiments classification in stock network public opinion space based on long-short memory convolution neural network
Computer Sciences, Shanghai University, 200444, China
2 East Sea Information Centre, SOA China, 200444, China
* Corresponding author: email@example.com
Deep learning is used to deal with natural language processing problems. Some are based on phrases and some are based on words. This article is inspired by the pixel level in the CV world and therefore retrains the neural network from a character perspective. Neural networks do not need to know about word lookup table or word2vec in advance, and the knowledge of these words is often high-dimensional and it is difficult to apply to convolutional neural networks. In addition, our long-short term memory convolutional neural networks no longer need to know the syntax and semantics in advance. The purpose of this paper is to analyse the investor's psychological characteristics and investment decision-making behaviour characteristics, to study the investor sentiment in the network public opinion space.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.