Titlebook: Analysis of Images, Social Networks and Texts; 7th International Co Wil M. P. van der Aalst,Vladimir Batagelj,Andrey V Conference proceedin

吼叫

Russian Q&A Method Study: From Naive Bayes to Convolutional Neural Networksnal neural network for question classification. We took advantage of an existing corpus of 2008 questions, manually annotated in accordance with a pragmatic 14-class typology. We modified the data by reducing the typology to 13 classes, expanding the dataset and improving the representativeness of s

escalate

Extraction of Explicit Consumer Intentions from Social Network Messageswork users to purchase certain goods or use certain services. The utilized approach is machine learning with annotation. A training set for expert annotation consists of messages from the “VKontakte” social network, selected through the LeadScanner API. The invented system of semantic tags allows di

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https://doi.org/10.1007/978-3-658-28741-2s, administrative support from the head office to subsidiaries, and levels of subsidiary integration. This is because social relationships between different actors inside the organization, the strength of ties and the size of networks, as well as other characteristics, could be the explanatory varia

BULLY

https://doi.org/10.1007/978-3-658-16277-1nd we also build models for determining the sentiment intensity for individual modalities and a combination of them. Different classification algorithms are compared: linear, neural networks and ensembles of decision trees, where XGBoost works best for audio, Logistic Regression - for text and Light

justify

Inequality and the Digital Economy% accuracy on the new dataset). We also tested several widely-used machine learning methods (logistic regression, Bernoulli Naïve Bayes) trained on the new question representation. The best result of 72.38% accuracy (micro) was achieved with the CNN model. We also ran experiments on pertinent featur

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https://doi.org/10.1007/978-3-319-78420-5es of its main word. The edges of the graph connect the intentional blocks that can be found in adjacent positions across all the messages of the training set. Extraction of intention objects and their properties is achieved by test set analysis in accordance to the constructed graph. Test set inclu

etiquette

Lorenzo Pupillo,Eli Noam,Leonard Waverman embeddings from the E-step. Second, we show that Biterm Topic Model (Yan et al. [.]) and Word Network Topic Model (Zuo et al. [.]) are equivalent with the only difference of tying word and context embeddings. We further extend these models by adjusting representation of each sliding window with a f