Identifying Personal Messages: A Step towards Product/Service Review and Opinion Mining

Sasan Azizian, Elham Rastegari, Brian Ricks, Magie Hall

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Twitter is one of the most popular micro-blogging services, with millions of users exchanging information. Twitter's popularity and low barriers has led many commercial entities to start using the service. As a result, the Twitter stream has a combination of personal and professional tweets. These professional tweets are marketing messages and do not provide insight into individual people's experiences. Thus, filtering personal tweets from commercial or professional ones is a crucial, though often overlooked, first step in mining micro-blogging data. Identifying personal messages is essential for opinion mining or product/service review in every domain, and it is specifically crucial in the healthcare domain. In this research study, we propose a method of classifying tweets as either personal or professional tweets using a novel feature set. Here we collected and analyzed three data sets from the Twitter stream related to the healthcare domain. Using a large number of hand-labeled tweets as input, we trained several classifiers on our proposed set of features and compared classifiers' accuracy, precision, and recall using 10-fold cross validation technique. On a combination of three health-related data sets, random forest classifier provided the maximum accuracy of 91.5%. This result shows that our approach can significantly increase the accuracy of data mining on the Twitter stream.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
EditorsFernando G. Tinetti, Quoc-Nam Tran, Leonidas Deligiannidis, Mary Qu Yang, Mary Qu Yang, Hamid R. Arabnia
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages876-881
Number of pages6
ISBN (Electronic)9781538626528
DOIs
StatePublished - Dec 4 2018
Event2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 - Las Vegas, United States
Duration: Dec 14 2017Dec 16 2017

Publication series

NameProceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

Conference

Conference2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
CountryUnited States
CityLas Vegas
Period12/14/1712/16/17

Keywords

  • classification
  • data mining
  • features selection
  • healthcare
  • twitter

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Safety, Risk, Reliability and Quality

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