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The question about possibilities to use Twitter users’ moods to increase accuracy of stock price movement prediction draws attention of many researchers. In this paper we examine the possibility of analyzing Twitter users’ mood to improve accuracy of predictions for Gold and Silver stock market prices. We used a lexicon-based approach to categorize the mood of users expressed in Twitter posts and to analyze 755 million tweets downloaded from February 13, 2013 to September 29, 2013. As forecasting technique, we select Support Vector Machines (SVM), which have shown the best performance. Results of SVM application to prediction the stock market prices for Gold and Silver are discussed.
This paper argues for a general DP-shell analysis of clausal complements in Russian. It is proposed that clausal complements are licensed by a null P in Caseless positions. The argument is based on an agentivity restriction on čto- and čtoby-clauses. Experimental evidence is presented that makes use of the factorial definition of the agentivity restriction. Two alternative accounts – in terms of a partial DP-shell and semantic coercion – are discussed. It is shown that the experimental results favor the null P account over the alternatives.