## 5 Conclusions  
In our work, we tested and applied a \"text mining\" method to extract geophysical and geochemical monitoring parameters from free unstructured text in daily and weekly bulletins officially issued by research institutes for the Stromboli volcano in southern Italy. The proposed natural language processing (NLP) system generates a structured database of user-defined relevant parameters and their temporal characterization, which are thus time series data available to geoscientists and specialists for further use and analysis. The system can potentially be tuned for other target parameters and other datasets published on the web. In the context of time series studies of volcanic processes, this method relieves specialists of time-consuming manual reading and dataset creation tasks. Thanks to this tool, when specialists have no access to original raw data, they are involved in a very limited way in the data extraction process. In particular, they are required to use their expertise to read and tag a small portion of the whole dataset during the training phase, while the analysis of the whole dataset is carried out by the text mining system.  
This proof-of-concept exercise demonstrates the feasibility of performing a fast (and low cost) analysis of datasets available online, providing crucial information that volcanologists may further analyze and interpret for volcanological investigations and applications. In addition, in our test, we used data from bulletins that are freely accessible on the web,  
Figure 15: Time series of VLP, SO\({}_{2}\) flux and thermal anomalies (the latter expressed in MW) in 2015–2021 extracted from LGS daily bulletins.  
without the need to access internal database systems. Moreover, a simple correlation between different gas parameters has been shown. This tool could be easily integrated into the next version of our script in order to realize different kinds of automatic or semi-automatic statistical analyses to verify the performance of the extraction algorithm and workflow.  
This work represents the base step for further innovative developments, such as the integration of gas monitoring data from ground-based sensor networks with other types, such as airborne and satellite (e.g., Sentinel-5P tropospheric monitoring data [22]). The information-technology-assisted integration of diverse datasets is a promising multi-scale and multi-sensor approach considering the intrinsic complexity of volcanic phenomena, and the challenge for scientists to achieve the synthesis of a multitude of monitoring data and observations.  
Conceptualization, M.B., L.S.A., F.C., D.T. and M.S.d.C.; software, M.B.; visualization, M.B.; validation, L.S.A., F.C., D.T. and M.S.d.C.; writing--original draft preparation, M.B., L.S.A. and M.S.d.C.; writing--review and editing, F.C. and D.T. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by: \"OT4 CLIMA\" project (ARS01 00405, D.D. 2261 of 6 September 2018, PON R&I 2014-2020 and FSC) from the Ministry of University and Research (MUR) and fruitful interaction with projects MOST and WhiTech of the Italian Space Agency (ASI).  
Not applicable.  
Daily and weekly monitoring bulletins by LGS can be accessed at [[http://lgs.geo.unifi.it](http://lgs.geo.unifi.it)]([http://lgs.geo.unifi.it](http://lgs.geo.unifi.it)) (last access on 20 February 2022), while INGV bulletins can be accessed at [[https://www.ct.ingv.it](https://www.ct.ingv.it)]([https://www.ct.ingv.it](https://www.ct.ingv.it)) (last access on 20 February 2022).  
The authors declare no conflict of interest.