Sentiment Classification using N-gram IDF and Automated Machine Learning

Sentiment Classification using N-gram IDF and Automated Machine Learning

ABSTRACT:

We propose a sentiment classification method with a general machine learning framework. For feature representation, n-gram IDF is used to extract software-engineering related, dataset-specific, positive, neutral, and negative n-gram expressions. For classifiers, an automated machine learning tool is used. In the comparison using publicly available datasets, our method achieved the highest F1 values in positive and negative sentences on all datasets.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : Python
  • Database : MYSQL

REFERENCE:

Rungroj Maipradit_, Hideki Hata_, Kenichi Matsumoto, “Sentiment Classification using N-gram IDF and Automated Machine Learning”, IEEE Software, 2019

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