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UNSUPERVISED NEURAL BASED HYBRID MODEL FOR SENTIMENT ANALYSIS OF WEB/MOBILE APPLICATION USING PUBLIC DATA SOURCES

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

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.
机译:用于确定社交网络通信中的情感的机器培训。从网站上提取文本文档,并将其标记为令牌。将令牌输入到单词到矢量的转换模型以生成单词矢量。词频逆文档频率(TF-IDF)算法将单词向量转换为句子向量。句子向量的随机选择子集被标记并用于训练分类器。分类器采用句子向量并预测与句子向量相关联的情感。与每个句子矢量相关联的预测情感可以被组合以生成与文本文档相关联的情感。

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