Text sentiment analysis based on CBOW model and deep learning in big data environment

For the issues that the accurate and rapid sentiment analysis of comment texts in the network big data environment, a text sentiment analysis method combining Bag of Words (CBOW) language model and deep learning is proposed. First, a vector representation of text is constructed by a CBOW language model based on feedforward neural networks. Then, the Convolutional Neural Network (CNN) is trained through the labeled training set to capture the semantic features of the text. Finally, the Dropout strategy is introduced in the Softmax classifier of traditional CNN, which can effectively prevent the model from over-fitting and has better classification ability. Experimental results on COAE2014 and IMDB datasets show that this method can accurately determine the emotional category of the text and is robust, the accuracy on the two datasets reached 90.5% and 87.2%, respectively.