Network text sentiment analysis method combining LDA text representation and GRU-CNN

In order to improve the performance of internet public sentiment analysis, a text sentiment analysis method combining Latent Dirichlet Allocation (LDA) text representation and convolutional neural network (CNN) is proposed. First, the review texts are collected from the network for preprocessing. Then, using the LDA topic model to train the latent semantic space representation (topic distribution) of the short text, and the short text feature vector representation based on the topic distribution is constructed. Finally, the CNN with gated recurrent unit (GRU) is used as a classifier. According to the input feature matrix, the GRU-CNN strengthens the relationship between words and words, text and text, so as to achieve high accurate text classification. The simulation results show that this method can effectively improve the accuracy of text sentiment classification.

[1]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[2]  M. Pasquier,et al.  Key issues in conducting sentiment analysis on Arabic social media text , 2013, 2013 9th International Conference on Innovations in Information Technology (IIT).

[3]  Hongfei Lin,et al.  Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus , 2017, Algorithms.

[4]  Tajinder Singh,et al.  Role of Text Pre-processing in Twitter Sentiment Analysis , 2016 .

[5]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[6]  Yashwant Prasad Singh,et al.  Sentiment Classification of Financial News Using Statistical Features , 2017, Int. J. Pattern Recognit. Artif. Intell..

[7]  Yanfei Liu,et al.  SatCNN: satellite image dataset classification using agile convolutional neural networks , 2017 .

[8]  Yong Shi,et al.  The Role of Text Pre-processing in Sentiment Analysis , 2013, ITQM.

[9]  Craig MacDonald,et al.  Using word embeddings in Twitter election classification , 2016, Information Retrieval Journal.

[10]  Haitao Huang,et al.  Abstractive text summarization using LSTM-CNN based deep learning , 2018, Multimedia Tools and Applications.

[11]  Maozhen Li,et al.  Performance evaluation of Latent Dirichlet Allocation in text mining , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[12]  Fathi M. Salem,et al.  Gate-variants of Gated Recurrent Unit (GRU) neural networks , 2017, 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).

[13]  Jack W. Stokes,et al.  Malware classification with LSTM and GRU language models and a character-level CNN , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Jiaru Lin,et al.  Text Sentiment Analysis Algorithm Optimization and Platform Development in Social Network , 2013, 2013 Sixth International Symposium on Computational Intelligence and Design.

[15]  Xianghan Zheng,et al.  Deep Sentiment Representation Based on CNN and LSTM , 2017, 2017 International Conference on Green Informatics (ICGI).

[16]  K SoumyaGeorge,et al.  Text Classification by Augmenting Bag of Words (BOW) Representation with Co-occurrence Feature , 2014 .

[17]  Bassam Al-Salemi,et al.  LDA-AdaBoost.MH: Accelerated AdaBoost.MH based on latent Dirichlet allocation for text categorization , 2015, J. Inf. Sci..

[18]  Yue Lu,et al.  Enriching text representation with frequent pattern mining for probabilistic topic modeling , 2012, ASIST.

[19]  Ruifeng Xu,et al.  A convolutional attentional neural network for sentiment classification , 2017, 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC).