An analysis of twitter data on e-cigarette sentiments and promotion

Abstract

We investigate general sentiments and information dissemination concerning electronic cigarettes or e-cigs using Twitter. E-cigs are relatively new products, and hence, not much research has been conducted in this area using large-scale social media data. However, the fact that e-cigs contain potentially dangerous substances makes them an interesting subject to study. In this paper, we propose novel features for e-cigs sentiment classification and create sentiment dictionaries relevant to e-cigs. We combine the proposed features with traditional features (i. e., bag-of-words and SentiStrength features) and use them in conjunction with supervised machine learning classifiers. The feature combination proves to be more effective than the traditional features for e-cigs sentiment classification. We also found that Twitter users are mainly concerned with sharing information (33%) and promoting e-cigs (22%). Although a low percentage of users share opinions, the majority of these users have positive opinions about e-cigs (11% positive, 3% negative).