Sentiment Analysis of Microblogs Using Multilayer Feed-Forward Artificial Neural Networks

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

Sentiment Analysis of Microblogs Using Multilayer Feed-forward Artificial Neural Networks

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

Metal cutting process parameters modeling: an artificial intelligence approach

530-539This study presents metal cutting process’ parameters modeling (cutting temperature, cutting force, and quality of machinedsurface) using artificial neural networks, and hybrid, adaptive neuro-fuzzy systems. Proposed models can be used for metalcutting process optimization, increasing productivity and reducing manufacturing costs

The Artificial Neural Network Based System for Validation of Thermocouples Used in Biomedicine

Machining operations are widely used in the orthopedic surgery. The temperature which occurs in the cutting zone, during the machining of the bones, may have many negative consequences in the postoperative period. Therefore, the measuring and the modeling of this parameter is a very important task. In this paper, the thermocouples are presented as a potential tool for the temperature measuring. The paper also deals with the system for validation of the thermocouples. The artificial neural network is used for modeling of the relationship between the electromotive force (as the thermocouple output) and the corresponding temperature. It is shown that the results of the modeling are in good correlation with the measured data