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MEETUPS - Identification of themes

MEETUPS identification of people and places is a tool developed using Python and Jupyter Notebook. SKLEARN and a set of Machine Learning algorithms to classify sentences according to the established type of events. The tool allows the extraction of one (the type of encounter) of the four elements that define a historical meetup. The encounter types are music-making, business meetings, personal life, social life, coincidence, public celebration, and education.

This implementation is divided in three main tasks: a) Generation of the training dataset In order to identify and classify sentences according to the encounter type we need first to build a dataset with sentences that describe the different encounter types. Approach:

  • Manually prepare seed terms for each meetup type
  • Randomly select sentences with those words from the corpus
  • Assign the relevant meetup type to each one of those sentences

b) Training the classifier Approach:

  • Build a balanced training set by selecting first sentences from low represented classes
  • Train and test MLPClassifier

c) Applying the classifier Use the model tested in b) and infer the type of encounter for all the data in the corpus

Information on installation and setup

  • Jupyter Notebook: MeetupType_applyClassifier.ipynb

Details of the data 

Running the Themes classifier:
|_ MeetupType_applyClassifier.ipynb

Training the Themes classifier:
|_ MeetupType_prototypeSentences.ipynb

Generating the training dataset:
|_ MeetupType_trainClassifier.ipynb

Data location

Data input:
|_ indexedSentences/

Data output:
|_ extractedMeetupTypes/        

Classifier:
meetupType/models/MLPClassifier_2.clf'

Prototype sentences:
|_ meetupType/prototypeSentences_*.csv


|_ README_identification_themes.md

DOI:

DOI