Mining online e-liquid reviews for opinion polarities about e-liquid features

نویسندگان

  • Zhipeng Chen
  • Daniel D Zeng
چکیده

BACKGROUND In recent years, the emerging electronic cigarette (e-cigarette) marketplace has developed prosperously all over the world. By analyzing online e-liquid reviews, we seek to identify the features attracting users. METHODS We collected e-liquid reviews from one of the largest online e-liquid review websites and extracted the e-liquid features by keywords. Then we used sentiment analysis to classify the features into two polarities: positive and negative. The positive sentiment ratio of a feature reflects the e-cigarette users' preference on this feature. RESULTS The popularity and preference of e-liquid features are not correlated. Nuts and cream are the favorite flavor categories, while fruit and cream are the most popular categories. The top mixed flavors are preferable to single flavors. Fruit and cream categories are most frequently mixed with other flavors. E-cigarette users are satisfied with cloud production, but not satisfied with the ingredients and throat hit. CONCLUSIONS We identified the flavors that e-cigarette users were satisfied with, and we found the users liked e-cigarette cloud production. Therefore, flavors and cloud production are potential factors attracting new users.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Feature extraction in opinion mining through Persian reviews

Opinion mining deals with an analysis of user reviews for extracting their opinions, sentiments and demands in a specific area, which can play an important role in making major decisions in such area. In general, opinion mining extracts user reviews at three levels of document, sentence and feature. Opinion mining at the feature level is taken into consideration more than the other two levels d...

متن کامل

Mining Interesting Aspects of a Product using Aspect-based Opinion Mining from Product Reviews (RESEARCH NOTE)

As the internet and its applications are growing, E-commerce has become one of its rapid applications. Customers of E-commerce were provided with the opportunity to express their opinion about the product on the web as a text in the form of reviews. In the previous studies, mere founding sentiment from reviews was not helpful to get the exact opinion of the review. In this paper, we have used A...

متن کامل

An Enhanced Sentence Level Sentiment Classification

-Sentiment analysis addresses the computational of feeling, sentiments and subjectivity in content, has reached an impressive consideration in recent years. Rather than the customary coarse-grained sentiment analysis tasks, for example document-level sentiment analysis. Aspect-oriented opinion mining aims to identify product aspects (features of products) about which opinion has been expressed ...

متن کامل

Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews

Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated...

متن کامل

Managing Risk and Enhancing Discoverability of Opinion From Online Reviews Using Classification Algorithm

A million number of reviews and opinions about any aspect are being posted in numerous blogs, forums, and online sites. This enormous information on worldwide network platforms make them feasible and can be used as source, in applications based on opinion mining and review analysis. The aim of this paper is to discover opinions from online reviews and managing risk in future. Our proposed metho...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره 17  شماره 

صفحات  -

تاریخ انتشار 2017