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See detailSentiment Analysis of Microblogs Using Multilayer Feed-forward Artificial Neural Networks
Despotovic, Vladimir UL; Tanikic, Dejan

in Computing and Informatics (2017), 36(5), 11271142

Sentiment analysis aims to extract public opinion on a particular topic and microblogs, especially Twitter as the most influential platform, represent a significant source of information. The application ... [more ▼]

Sentiment analysis aims to extract public opinion on a particular topic and microblogs, especially Twitter as the most influential platform, represent a significant source of information. The application to microblogs has to cope with difficulties, such as informal language with abbreviations, internet jargons, emoticons, hashtags that do not appear in conventional text documents. Sentiment analysis technique for microblogs based on a feed-forward artificial neural network (ANN) with sigmoid activation function is proposed in this paper and compared to machine learning approaches, i.e. Multinomial Naive Bayes, Support Vector Machines and Maximum Entropy. Experiments were performed on Stanford Twitter Sentiment corpus, a balanced dataset which contains noisy training labels weakly annotated using emoticons as sentiment indicators; and SemEval-2014 Task 9 corpus, an unbalanced dataset which contains manually annotated training examples. The obtained results show that ANN produces superior or at least comparable results to state-of-the-art machine learning techniques. [less ▲]

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See detailEnergy Efficient Scheduling in Heterogeneous Systems with a Parallel Multiobjective Local Search
Iturriaga, Santiago; Nesmachnow, Sergio; Dorronsoro, Bernabe et al

in Computing and Informatics (2013), 32(2), 273-294

This article introduces ME-MLS, an e cient multithreading local search algorithm for solving the multiobjective scheduling problem in heterogeneous com- puting systems. We consider the minimization of ... [more ▼]

This article introduces ME-MLS, an e cient multithreading local search algorithm for solving the multiobjective scheduling problem in heterogeneous com- puting systems. We consider the minimization of both the makespan and energy consumption objectives. The proposed method follows a fully multiobjective ap- proach, applying a Pareto-based dominance search that is executed in parallel by using several threads. The experimental analysis demonstrates that the new multi- threading algorithm outperforms a set of fast and accurate two-phases deterministic heuristics based on the traditional MinMin. The new ME-MLS method is able to achieve signi cant improvements in both makespan and energy consumption objec- tives in reduced execution times for a large set of testbed instances, while exhibiting a near linear speedup behavior when using up to 24 threads. [less ▲]

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