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Research in financial domain has shown that sentiment aspects of stock news have a profound impact on volume trades, volatility, stock prices and firm earnings. In-depth analysis of stock news is now sourced from financial reviews by various social networking and marketing sites to help improve decision making. Nonetheless, such reviews are in the form of unstructured text, which requires natural language processing (NLP) in order to extract the sentiments. Accordingly, in this study we investigate the use of NLP tasks in effort to improve the performance of sentiment classification in evaluating the information content of financial news as an instrument in investment decision support system. At present, feature extraction approach is mainly based on the occurrence frequency of words. Therefore low-frequency linguistic features that could be critical in sentiment classification are typically ignored. In this research, we attempt to improve current sentiment analysis approaches for financial news classification by focusing on low-frequency but informative linguistic expressions. Our proposed combination of low and high-frequency linguistic expressions contributes a novel set of features for sentiment classification. The experimental results show that an optimal Ngram feature selection (combination of optimal unigram and bigram features) enhances sentiment classification accuracy as compared to other types of feature sets. [ABSTRACT FROM AUTHOR]

Therefore, in this paper the authors [[14]] study the role of big data component analysis for handling and protecting sensitive information from disclosure along with preserving the data utility. An implementation of the algorithm is tested on standard datasets from the UCI repository and performed comparably, if not better than, other algorithms for dealing with uncertain and fuzzy data. Big data analytics has received numerous attentions in many areas [[1]-[5]]. Jiang et al. [[6]] consider the structural information of the posts and the semantic information of users' interests and use UICD (User Interest Community Detection) model to analyze text streams from microblogging sites for detecting users' interest communities. [Extracted from the article]

Utilising the mobilisation of a sink node in a wireless sensor network to combat the energy hole, or hotspot issue, is well referenced. However, another issue, that of energy spikes may remain. With the mobile sink node potentially communicating with some nodes more than others. In this study we propose the Mobility Aware Duty Cycling and Dynamic Preambling Algorithm (MADCaDPAL). This algorithm utilises an existing solution where a communication threshold is built between a mobile sink node using predictable mobility and static nodes on its path. MADCaDPAL bases decisions relating to node sleep function, moving to clear channel assessment and the subsequent sending of preambles on the relation between the threshold built by the static node and the position of the mobile sink node. MADCaDPAL achieves a reduction in average energy consumption of up to 80%, this when used in conjunction with a lightweight carrier-sense multiple access based MAC implementation. Maximum energy consumption amongst individual nodes is also brought closer to the average, reducing energy spikes and subsequently improving network lifetime. Additionally, frame delivery to the sink is improved overall. [ABSTRACT FROM AUTHOR]