The document presents a method for sentiment classification using n-gram IDF within a general machine learning framework. It demonstrates that this approach, utilizing automated machine learning tools, yields the highest F1 scores for positive and negative sentences across various datasets. System requirements for implementation include specific hardware and software configurations.

1. 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

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