Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network

作者：

Highlights：

• We focus on the role of Twitter and social media in the business environment.

• We develop tools to collect a large data set of more than 10 million brand-specific tweets.

• We develop a reduced (1/8th) Twitter-specific lexicon to replace traditional sentiment lexicons.

• We demonstrate the lexicon provides improved corpus coverage and sentiment analysis performance.

• We develop comparative sentiment classification models using DAN2 and SVM.

摘要

•We focus on the role of Twitter and social media in the business environment.•We develop tools to collect a large data set of more than 10 million brand-specific tweets.•We develop a reduced (1/8th) Twitter-specific lexicon to replace traditional sentiment lexicons.•We demonstrate the lexicon provides improved corpus coverage and sentiment analysis performance.•We develop comparative sentiment classification models using DAN2 and SVM.

论文关键词：Twitter,Sentiment analysis,Twitter-specific lexicon,DAN2,Feature engineering,n-gram analysis,Machine learning,SVM

论文评审过程：Available online 30 May 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.05.057