Intent classification of short-text on social media,
Open accessAbstract
Social media platforms facilitate the emergence of citizen communities that discuss real-world events. Their content reflects a variety of intent ranging from social good (e.g., volunteering to help) to commercial interest (e.g., criticizing product features). Hence, mining intent from social data can aid in filtering social media to support organizations, such as an emergency management unit for resource planning. However, effective intent mining is inherently challenging due to ambiguity in interpretation, and sparsity of relevant behaviors in social data. In this paper, we address the problem of multiclass classification of intent with a use-case of social data generated during crisis events. Our novel method exploits a hybrid feature representation created by combining top-down processing using knowledge-guided patterns with bottom-up processing using a bag-of-tokens model. We employ pattern-set creation from a variety of knowledge sources including psycholinguistics to tackle the ambiguity challenge, social behavior about conversations to enrich context, and contrast patterns to tackle the sparsity challenge. Our results show a significant absolute gain up to 7% in the F1 score relative to a baseline using bottom-up processing alone, within the popular multiclass frameworks of One-vs-One and One-vs-All. Intent mining can help design efficient cooperative information systems between citizens and organizations for serving organizational information needs.
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Purohit et al. (2015) showed that it is possible to extract intent when the channel is text-only and restricted in length.
“the work by Purohit et al. (2015), who applied intent classification to short messages on Twitter, showed that it is possible to extract intent when the channel is text-only and restricted in length.”
From Modeling Feedback in Interaction With Conversational Agents—A Review (2022) · cites this paper as Purohit et al., 2015
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- Authors
- Purohit, H., Dong, G., Shalin, V., Thirunarayan, K., & Sheth, A.
- Affiliations
- George Mason University (US)
- Wright State University (US)
- Venue
- 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 222-228
- OpenAlex
- W2237059446
- Cited by
- 66
- References
- 33
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 3 · 2023: 9 · 2022: 6 · 2021: 15 · 2020: 9 · 2019: 5 · 2018: 6 · 2017: 6
- FWCI
- 2.25
- Type
- conference paper
- Language
- en
- Open access
- green
- works.bepress.com
- Licence
- cc-by
- Locations
- IEEE International Conference on Smart City/SocialCom/SustainCom (publishedVersion)
- CORE Scholar (Wright State University) (submittedVersion)
- Scholar Commons (University of South Carolina) (submittedVersion)
- Journal of Bioresource Management (submittedVersion, cc-by)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- intent classification, short text, social media, crisis events, multi-class classification, hybrid feature representation, psycholinguistics, ambiguity, emergency management, citizen participation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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