demonstration

Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization

Authors:

Shubhanshu Mishra,

Elizabeth SurbeckAuthors Info & Claims

HT '15: Proceedings of the 26th ACM Conference on Hypertext & Social Media

Pages 323 - 325

https://doi.org/10.1145/2700171.2791022

Published: 24 August 2015 Publication History

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.

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Cited By

Salman Al-Tameemi IFeizi-Derakhshi MPashazadeh SAsadpour M(2023)An Efficient Sentiment Classification Method with the Help of Neighbors and a Hybrid of RNN ModelsComplexity10.1155/2023/18965562023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/1896556
Zhao SHong XYang JZhao YDing G(2023)Toward Label-Efficient Emotion and Sentiment AnalysisProceedings of the IEEE10.1109/JPROC.2023.3309299111:10(1159-1197)Online publication date: Oct-2023
https://doi.org/10.1109/JPROC.2023.3309299
Mishra SRezapour RDiesner JAl Hasan MXiong L(2022)Information Extraction from Social MediaProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557503(5148-5151)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557503
Show More Cited By

Index Terms

Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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Information & Contributors

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Published In

cover image ACM Conferences

HT '15: Proceedings of the 26th ACM Conference on Hypertext & Social Media

August 2015

360 pages

ISBN:9781450333955

DOI:10.1145/2700171

General Chair:
Yeliz Yesilada
Middle East Technical University Northern Cyprus Campus
,
Program Chairs:
Rosta Farzan
University of Pittsburgh, USA
,
Geert-Jan Houben
Delft University of Technology, The Netherlands

Copyright © 2015 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2015

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Demonstration

Funding Sources

Anheuser Busch InBev

Conference

HT '15

Sponsor:

SIGWEB

HT '15: 26th ACM Conference on Hypertext and Social Media

September 1 - 4, 2015

Guzelyurt, Northern Cyprus

Acceptance Rates

HT '15 Paper Acceptance Rate 24 of 60 submissions, 40%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Citations

Cited By

Salman Al-Tameemi IFeizi-Derakhshi MPashazadeh SAsadpour M(2023)An Efficient Sentiment Classification Method with the Help of Neighbors and a Hybrid of RNN ModelsComplexity10.1155/2023/18965562023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/1896556
Zhao SHong XYang JZhao YDing G(2023)Toward Label-Efficient Emotion and Sentiment AnalysisProceedings of the IEEE10.1109/JPROC.2023.3309299111:10(1159-1197)Online publication date: Oct-2023
https://doi.org/10.1109/JPROC.2023.3309299
Mishra SRezapour RDiesner JAl Hasan MXiong L(2022)Information Extraction from Social MediaProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557503(5148-5151)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557503
Mishra SRezapour RDiesner J(2022)Information Extraction from Social Media: A Hands-On Tutorial on Tasks, Data, and Open Source ToolsAdvances in Information Retrieval10.1007/978-3-030-99739-7_74(589-596)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-030-99739-7_74
Mishra S(2021)Information extraction from digital social trace data with applications to social media and scholarly communication dataACM SIGIR Forum10.1145/3451964.345198154:1(1-2)Online publication date: 19-Feb-2021
https://dl.acm.org/doi/10.1145/3451964.3451981
Egorova ETsarev DSurikov A(2021)Emotion Analysis based on Incremental Online Learning in Social Networks2021 IEEE 15th International Conference on Application of Information and Communication Technologies (AICT)10.1109/AICT52784.2021.9620224(1-6)Online publication date: 13-Oct-2021
https://doi.org/10.1109/AICT52784.2021.9620224
Capuano NGreco LRitrovato PVento M(2020)Sentiment analysis for customer relationship management: an incremental learning approachApplied Intelligence10.1007/s10489-020-01984-xOnline publication date: 12-Nov-2020
https://doi.org/10.1007/s10489-020-01984-x
Tripathy JChakkaravarthy SSatapathy SSahoo MVaidehi V(2020)ALBERT-based fine-tuning model for cyberbullying analysisMultimedia Systems10.1007/s00530-020-00690-528:6(1941-1949)Online publication date: 18-Sep-2020
https://doi.org/10.1007/s00530-020-00690-5
Mishra SAtzenbeck CRubart JMillard D(2019)Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from TweetsProceedings of the 30th ACM Conference on Hypertext and Social Media10.1145/3342220.3344929(283-284)Online publication date: 12-Sep-2019
https://dl.acm.org/doi/10.1145/3342220.3344929
Wang BXiong SHuang YLi X(2018)Review Rating Prediction Based on User Context and Product ContextApplied Sciences10.3390/app81018498:10(1849)Online publication date: 9-Oct-2018
https://doi.org/10.3390/app8101849
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