Sentiment analysis with incremental human-in-the-loop learning and lexical resource customization

Shubhanshu Mishra, Jana Diesner, Jason Byrne, Elizabeth Surbeck

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

Abstract

The adjustment of probabilistic models for sentiment analysis to changes in language use and the perception of products can be realized via incremental learning techniques. We provide a free, open and GUI-based sentiment analysis tool that allows for a) relabeling predictions and/or adding labeled instances to retrain the weights of a given model, and b) customizing lexical resources to account for false positives and false negatives in sentiment lexicons. Our results show that incrementally updating a model with information from new and labeled instances can substantially increase accuracy. The provided solution can be particularly helpful for gradually refining or enhancing models in an easily accessible fashion while avoiding a) the costs for training a new model from scratch and b) the deterioration of prediction accuracy over time.

Original languageEnglish (US)
Title of host publicationHT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media
PublisherAssociation for Computing Machinery, Inc
Pages323-325
Number of pages3
ISBN (Electronic)9781450333955
DOIs
StatePublished - Aug 24 2015
Event26th ACM Conference on Hypertext and Social Media, HT 2015 - Guzelyurt, Cyprus
Duration: Sep 1 2015Sep 4 2015

Publication series

NameHT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media

Other

Other26th ACM Conference on Hypertext and Social Media, HT 2015
CountryCyprus
CityGuzelyurt
Period9/1/159/4/15

Keywords

  • Incremental learning
  • Lexical resource customization
  • Sentiment analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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  • Cite this

    Mishra, S., Diesner, J., Byrne, J., & Surbeck, E. (2015). Sentiment analysis with incremental human-in-the-loop learning and lexical resource customization. In HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media (pp. 323-325). (HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media). Association for Computing Machinery, Inc. https://doi.org/10.1145/2700171.2791022