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Abstract-Social Media is becoming major and popular technological platform that allows users discussing and sharing information. Information is generated and managed through either computer or mobile devices by one person and consumed by many other persons. Most of these user generated content are textual information, as Social Networks(Facebook, LinkedIn), Microblogging(Twitter), blogs(Blogspot, Wordpress). Looking for valuable nuggets of knowledge, such as capturing and summarizing sentiments from these huge amount of data could help users make informed decisions. In this paper, we develop a sentiment identification system called SES which implements three different sentiment identification algorithms. We augment basic compositional semantic rules in the first algorithm. In the second algorithm, we think sentiment should not be simply classified as positive, negative, and objective but a continuous score to reflect sentiment degree. All word scores are calculated based on a large volume of customer reviews. Due to the special characteristics of social media texts, we propose a third algorithm which takes emoticons, negation word position, and domain-specific words into account. Furthermore, a machine learning model is employed on features derived from outputs of three algorithms. We conduct our experiments on user comments from Facebook and tweets from twitter. The results show that utilizing Random Forest will acquire a better accuracy than decision tree, neural network, and logistic regression. We also propose a flexible way to represent document sentiment based on sentiments of each sentence contained. SES is available online.

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SUMMARYHomology detection is a fundamental step in sequence analysis. In the recent years, pairwise statistical significance has emerged as a promising alternative to database statistical significance for homology detection. Although more accurate, currently it is much time consuming because it involves generating tens of hundreds of alignment scores to construct the empirical score distribution. This paper presents a parallel algorithm for pairwise statistical significance estimation, called MPIPairwiseStatSig, implemented in C using MPI library. We further apply the parallelization technique to estimate non-conservative pairwise statistical significance using standard, sequence-specific, and position-specific substitution matrices, which has earlier demonstrated superior sequence comparison accuracy than original pairwise statistical significance. Distributing the most compute-intensive portions of the pairwise statistical significance estimation procedure across multiple processors has been shown to result in near-linear speed-ups for the application. The MPIPairwiseStatSig program for pairwise statistical significance estimation is available for free academic use at