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 thes...

• Type: Journal
• Year: 2015
• Where: AIME
• Authors: Andreea Kamiana Godea, Cornelia Caragea, Florin Adrian Bulgarov, Suhasini Ramisetty-Mikler
• Keywords: AIME 2015, Artificial Intelligence