Predictor for Company Mergers

(Bachelorthesis Anne)

This project contains python classes for text mining and machine learning models to recognize company mergers in news articles. The csv file classification_labelled_corrected.csv contains 1497 labeled news articles from Reuters.com and is used for the machine learning models.

Best F1 score results:

• Support Vector Machines Classifier (SVM):
  F1 score: 0.894
  Best parameters set found on development set: {'SVC__C': 0.1, 'SVC__gamma': 0.01, 'SVC__kernel': 'linear', 'perc__percentile': 50}

• Naive Bayes Classifier:
  F1 score: 0.832 (average)
  Parameters: SelectPercentile(25), own Bag of Words implementation, 10-fold cross validation

The complete documentation can be found in the latex document in the thesis folder.

Installation under Windows

$ pip install xy

Requirements

pandas==0.20.1
nltk==3.2.5
webhoseio==0.5
numpy==1.14.0
graphviz==0.9
scikit_learn==0.19.2

Usage

The scripts can be called separately. You need to enter a valid personal key for webhose.io before you call Requester.py. To run NER.py you need to change the path to the JAVAHOME environment variable in find_companies method.

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Author: Anne Lorenz / Datavard AG

Project Status: work in progress