English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Topic Detection and Tracking (TDT) is a very active research question withinthe area of text mining, generally applied to news feeds and Twitter datasets,where topics and events are detected. The notion of "event" is broad, buttypically it applies to occurrences that can be detected from a single post ora message. Little attention has been drawn to what we call "micro-events",which, due to their nature, cannot be detected from a single piece of textualinformation. The study investigates the feasibility of micro-event detection ontextual data using a sample of messages from the Stack Overflow Q&A platformand Free/Libre Open Source Software (FLOSS) version releases from Libraries.iodataset. We build pipelines for detection of micro-events using three differentestimators whose parameters are optimized using a grid search approach. Weconsider two feature spaces: LDA topic modeling with sentiment analysis, andhSBM topics with sentiment analysis. The feature spaces are optimized using therecursive feature elimination with cross validation (RFECV) strategy.  In our experiments we investigate whether there is a characteristic change inthe topics distribution or sentiment features before or after micro-events takeplace and we thoroughly evaluate the capacity of each variant of our analysispipeline to detect micro-events. Additionally, we perform a detailedstatistical analysis of the models, including influential cases, varianceinflation factors, validation of the linearity assumption, pseudo R squaredmeasures and no-information rate. Finally, in order to study limits ofmicro-event detection, we design a method for generating micro-event syntheticdatasets with similar properties to the real-world data, and use them toidentify the micro-event detectability threshold for each of the evaluatedclassifiers.

Is it feasible to detect FLOSS version release events from textual messages? A case study on Stack Overflow