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User-Level Twitter Sentiment Analysis with a Hybrid Approach

  • Meng Joo ErEmail author
  • Fan Liu
  • Ning Wang
  • Yong Zhang
  • Mahardhika Pratama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9719)

Abstract

With the objective of extracting useful information from the opinion-rich data on Twitter, both supervised learning-based and unsupervised lexicon-based methods for sentiment analysis on Twitter corpus have been studied in recent years. However, the unique characteristics of tweets such as the lack of labels and frequent usage of emoticons poses challenges to most of the existing learning-based and lexicon-based methods. In addition, studies on Twitter sentiment analysis nowadays mainly focus on domain specific tweets while a larger amount of tweets are about personal feelings and comments on daily life events. In this paper, a hybrid approach of augmented lexicon-based and learning-based method is designed to handle the distinctive characteristics of tweets and perform sentiment analysis on a user level, providing us information of specific Twitter users’ typing habits and their online sentiment fluctuations. Our model is capable of achieving an overall accuracy of 81.9 %, largely outperforming current baseline models on tweet sentiment analysis.

Keywords

Twitter Social media Date mining Sentiment analysis 

Notes

Acknowledgement

The authors would like to acknowledge the funding support from the National Natural Science Foundation of P. R. China (under Grants 51009017 and 51379002), Applied Basic Research Funds from Ministry of Transport of P.R. China (under Grant 2012-329-225-060), and Program for Liaoning Excellent Talents in University (under Grant LJQ2013055).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Meng Joo Er
    • 1
    Email author
  • Fan Liu
    • 2
  • Ning Wang
    • 1
  • Yong Zhang
    • 2
  • Mahardhika Pratama
    • 3
  1. 1.Marine Engineering CollegeDalian Maritime UniversityDalianChina
  2. 2.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Department of Computer Science and ITLa Trobe UniversityMelbourneAustralia

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