We are very proud to have participated in the proceedings of the 11th Hellenic Conference on Artificial Intelligence which was held virtually between 2nd – 4th of September 2020. SETN conferences have already been established as one of the most prominent forums, where Greek and International AI scientists, can present original and high-quality research on emerging topics on Artificial Intelligence.
Our senior data scientist, Konstantinos Korovesis, presented our latest research work on “Leveraging aspect-based sentiment prediction with textual features and document metadata“. This has been a joint venture with the Intelligent Interaction Research Group, from the Cultural Technology Department of the University of the Aegean.
Aspect-based sentiment prediction is a specific area of sentiment analysis that models the sentiment of a text excerpt as a multi-dimensional quantity pertaining to various interpretations, rather than a scalar one, that admits a single explanation. Extending earlier work, the said task is examined as a part of a unified architecture that collects, analyzes and stores documents from various online sources, including blogs & social network posts. The obtained data are processed at various levels; initially, a hybrid, attention-based bi-directional long short-term memory network, coupled with convolutional layers, is used to extract the textual features of the document. Following, an additional number of document metadata are also examined, such as the number of repetitions, the existence, type and frequency of emoji ideograms and, especially, the presence of keywords, assigned either manually (e.g. in the form of hashtags) or automatically. All of the aforementioned features are subsequently provided as input to a fully-connected, multi-layered, feed-forward artificial neural network that performs the final prediction task. The overall approach is tested on a large corpus of documents, with encouraging results.
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