This project represent a code for paper Methods of Lexicon Integration with Machine Learning for Sentiment Analysis System and describes the application of SVM classifier and Neural Networks classifiers for sentiment classification of Russian Twitter messages in the banking and telecommunications domains of SentiRuEval-2016 competition.

All dependencies described in Makefile and could be installed as follows:

make install

For research purposes. Use run/Makefile to run workflow for certain task (bank or tcc) and classifier (svm, lr), for example:

cd run && make svm_sre15_bank_w2v_bal

returns F-macro/micro result for SentiRuEval-2015 bank dataset using w2v-based embedding model for balanced test collection.

All embedding classifier settings presented in data/embedding folder.

• SentiRuEval-2015 contest data;
• SentiRuEval-2016 contest data & results of this approach (participant #1) in comparation with the other participants.

This work has been formed into AIDT Journal 2017/2 paper publication.

The latter was presented at RUSSIR conference in a form of the following posters: RUSSIR-2017, RUSSIR-2016;

Early publication could be found here: Dialog-2016;

@article{rusnachenko2017methods,
  title={Методы интеграции лексиконов в машинное обучение для систем анализа тональности},
  author={Русначенко, Николай Леонидович and Лукашевич, Наталья Валентиновна},
  journal={Искусственный интеллект и принятие решений},
  number={2},
  pages={78--89},
  year={2017},
  publisher={Федеральное государственное учреждение" Федеральный исследовательский центр~…}
}