Abstract
The goal of our research and experiments is to find the definitions and values of key performance indicators (KPIs) in unstructured text. The direct access to opinions of customers served as a motivating factor for us to choose Twitter data for our experiments. For our case study, we have chosen the restaurant business domain. As in the other business domains, KPIs often serve as a solution for identification of current problems. Therefore, it is essential to learn which criteria are important to restaurant guests. The mission of our Proof-of-Concept KPI discovery tool presented in this paper is to facilitate the explorative analysis taking Twitter user posts as a data source. After processing tweets with Stanford CoreNLP toolkit, aggregated values are computed and presented as visual graphs. We see our tool as an instrument for data discovery applicable, for example, to define new qualitative and quantitative KPIs based on the values found in the graph. The graph represents a complete view of aggregated data that corresponds to the search results according to the user-defined keywords, and gives easy access to detailed data (tweets) that, in its turn, leads to better understanding of the post context and its emotional coloring.
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Zemnickis, J., Niedrite, L., Kozmina, N. (2020). A Little Bird Told Me: Discovering KPIs from Twitter Data. In: Robal, T., Haav, HM., Penjam, J., Matulevičius, R. (eds) Databases and Information Systems. DB&IS 2020. Communications in Computer and Information Science, vol 1243. Springer, Cham. https://doi.org/10.1007/978-3-030-57672-1_13
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