In this paper, sentiment classification techniques are incorporated into the domain of political news from columns in different Turkish news sites. We compared four supervised machine learning algorithms of Naïve Bayes, Maximum Entropy, SVM and the character based N-Gram Language Model for sentiment classification of Turkish political columns. We also discussed in detail the problem of sentiment classification in the political news domain. We observe from empirical findings that the Maximum Entropy and N-Gram Language Model outperformed the SVM and Naïve Bayes. Using different features, all the approaches reached accuracies of 65% to 77%.

, 115 pages The foreign exchange market, which is widely known as Forex or FX, is the largest financial market with a daily transactional volume of $5 trillion. Due to the huge structure of the market, price analysis on FX market draws attention of many scientists and practitioners. There are 2 main analysis approaches: Fundamental and technical analysis. Fundamental analysis focuses on the macroeconomic factors such as interest rate to explain the market movements. Technical analysis deals with past market price data to forecast the future prices. Technical analysis involves two main approaches: Chart analysis and technical indicator based price analysis. Chart analysis deals with detection of patterns in price charts. Technical indicators transform the price time series data into another time series data to explore patterns. Technical indicators are widely used in FX and other financial markets which are the building blocks of many trading systems. A trading system is based on technical indicators or pattern-based approaches which produces buy/sell signals to trade in the market. In this thesis, a heuristic based trading system on Forex data is developed using popular technical indicators. The system grounds on selecting and combining the trading rules based on indicators using heuristic methods. The selection of the trading rules is realized by using Genetic Algorithm and a local search method. A weighted majority voting method is proposed to combine the technical indicator based trading rules to form a single trading rule. The experiments are conducted on 2 major currency pairs v in 3 different time frames where promising results are achieved.

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Purpose: The purpose of this paper is the presentation of a new method for blog quality assessment. The method uses the temporal sequence of link creation events between blogs as an implicit source for the collective tacit knowledge of blog authors about blog quality. Design/methodology/approach: The blog data are processed by the novel method for the assessment of blog quality. The results are compared to Google Page Rank with respect to the Gold Standard, the BlogRazzi Bookmark Rank. Findings: The method is similar or better than Google Page Rank with respect to the chosen Gold Standard. Originality/value: The major contribution of this paper is the introduction of a novel method for blog quality assessment. Even though its superiority to other and more established methods cannot be proven in the context of this limited study, it enriches the toolset available for blog quality assessment and may become important for a deeper understanding of organizational learning. © Emerald Group Publishing Limited

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