Much attention in recent years has been attracted by the process exploring useful information from a large amount of textual data produced by microblogging services such as Twitter. A very important preprocessing step is to convert natural language texts of microblog text mining into proper numerical representations. The short-length characteristics of microblog texts result in using the term frequency vectors to represent microblog texts and it will cause “sparse data” problem. Finding proper representations for microblog texts is a challenging issue.In the previous paper, they applied deep networks so that they can map the high-dimensional representations to low-dimensional representations.The retweet and hashtags have been used as the semantic similarity. They used two types of approaches which includes modifying the training data and modifying the training objective. They have also shown that deep models perform better than traditional methods such as latent Dirichlet allocation topic model and latent semantic analysis.

Microbloging, Accessibility, Sentiment Classfication, Latent Semantic Analysis

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