Sentiment analysis of the text deals with the mining of the opinions of people from their written communication. With the increasing usage of online social media platforms for user interactions, abundant opinionated textual data emerges. Therefore, it leads to increased mining of opinions and sentiments and hence greater interest in sentiment analysis. The article introduces the novel Lexico-Semantic features and their use in the sentiment polarity task of English language text. These features are derived using the semantic extension of the lexicons by employing sentiment lexicons and semantic models. These features make data sample size consistent when used in deep learning settings, thereby eliminating the zero padding. For evaluation, we use different semantic models and lexicons to determine the role and impact of Lexico-Semantic features in classification performance. These features, along with the other features, are used to train the different classifiers. Our experimental evaluation shows that introducing Lexico-Semantic features to various state-of-the-art methods of both machine and deep learning improves the overall performance of classifiers.

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Modern GPUs perform computation at a very high rate when compared to CPUs; as a result, they are increasingly used for general purpose parallel computation. Determining if a statically optimal binary search tree is an optimization problem to find the optimal arrangement of nodes in a binary search tree so that average search time is minimized. Knuth's modification to the dynamic programming algorithm improves the time complexity to O(n2). We develop a multiple GPU-based implementation of this algorithm using different approaches. Using suitable GPU implementation for a given workload provides a speedup of up to four times over other GPU based implementations. We are able to achieve a speedup factor of 409 on older GTX 570 and a speedup factor of 745 is achieved on a more modern GTX 1060 when compared to a conventional single threaded CPU based implementation.

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GPUs (Graphics processing units) can be used for general purpose parallel computation. Developers can develop parallel programs running on GPUs using different computing architectures like CUDA or OpenCL. The Optimal Matrix Chain Multiplication problem is an optimization problem to find the optimal order for multiplying a chain of matrices. The optimal order of multiplication depends only on the dimensions of the matrices. It is known that this problem can be solved by dynamic programming technique using O(n 3 )-time complexity algorithm and a work space of size O(n 2 ). The main contribution of this paper is to present a parallel implementation of this O(n 3 )-time algorithm on a GPU and to assess the amount of programming effort required to develop this parallel implementation when compared to a similar sequential implementation.