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UNSUPERVISED NEURAL BASED HYBRID MODEL FOR SENTIMENT ANALYSIS OF WEB/MOBILE APPLICATION USING PUBLIC DATA SOURCES
UNSUPERVISED NEURAL BASED HYBRID MODEL FOR SENTIMENT ANALYSIS OF WEB/MOBILE APPLICATION USING PUBLIC DATA SOURCES
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机译:基于公共数据源的基于非监督神经网络的混合模型在Web /移动应用中的情感分析
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摘要
Machine training for determining sentiments in social network communications. A text document is extracted from a web site and tokenized into tokens. The tokens are input to a word to vector conversion model to generate word vectors. A term frequency inverse document frequency (TF-IDF) algorithm converts the word vectors to sentence vectors. A randomly selected subset the sentence vectors are tagged and used to train a classifier. The classifier takes a sentence vector and predicts a sentiment associated with the sentence vector. Predicted sentiment associated with each of the sentence vectors may be combined to generate a sentiment associated with the text document.
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