Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

ABSTRACT:

Background

Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions.

Objective

Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes.

Methods

Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models we

SUBMITTER: Cole-Lewis H 

PROVIDER: S-EPMC4642404 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature