Abstract
The objective of the research is the development and evaluation testing of a speed-optimized «Dual-trained Lazy CNN» neural network model for language-dependent sentiment analysis in Slavic languages in specific cultural context, as well as the software implementation of the resulting network. Convolutional neural networks are easy to train and implement. To train them, a standard error back propagation algorithm is used, and because the filter weights are evenly distributed the number of parameters in the convolutional neural network is small. From the viewpoint of computational linguistics, convolutional neural networks are a powerful tool for classification, that, however, does not have any language intuition, which significantly complicates the analysis of algorithm errors. However, it is convolutional networks that are widely used in text data analysis tasks. Neural networks always work with big data and often require a lot of processing power. Therefore, to simplify computations, it makes sense to use «lazy» NNs. A «lazy» neural network is a network, which, if it receives input data that repeats the patterns that were previously processed by it, returns a result that was obtained earlier.
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Ivanyuk, V., Tsapina, E. (2021). Creating Emotion Recognition Algorithms Based on a Convolutional Neural Network for Sentiment Analysis. In: Kerautret, B., Colom, M., Krähenbühl, A., Lopresti, D., Monasse, P., Talbot, H. (eds) Reproducible Research in Pattern Recognition. RRPR 2021. Lecture Notes in Computer Science(), vol 12636. Springer, Cham. https://doi.org/10.1007/978-3-030-76423-4_5
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