Abstract
Due to social media’s ubiquitousness, most advertising campaigns take place on such platforms as Facebook, Twitter, or Instagram. As a result, Natural Language Processing has become an essential tool to extract information about the users: their personality traits, brand preferences, distinctive vocabulary, etc. Such data can be further used to create text adverts profiled to engage with users who share a certain set of features. While most of the algorithms capable of processing the text are neural network-driven, associative graphs serve the same purpose, attaining usually similar or better accuracy, but being more explainable than black box-like models based on neural networks. This paper presents an associative graph for natural language processing and fine-grained sentiment analysis. The ability of associative graphs to represent complex relations between phrases can be used to create a model capable of classifying the input data into many categories simultaneously with high accuracy and efficiency. This approach enabled us to acquire a model performing similarly or better than the state-of-the-art solutions while being more explicit and easier to create and explain.
Supported by Native Hash LTD and the AGH University of Science and Technology.
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Wójcik, M., Horzyk, A., Bulanda, D. (2021). Associative Graphs for Fine-Grained Text Sentiment Analysis. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13109. Springer, Cham. https://doi.org/10.1007/978-3-030-92270-2_21
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