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Ordinal Text Quantification
- Source :
- SIGIR, SIGIR 2016-39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 937–940, Pisa, Italy, 17-21 July 2016, info:cnr-pdr/source/autori:Da San Martino G.; Gao W.; Sebastiani F./congresso_nome:SIGIR 2016-39th International ACM SIGIR Conference on Research and Development in Information Retrieval/congresso_luogo:Pisa, Italy/congresso_data:17-21 July 2016/anno:2016/pagina_da:937/pagina_a:940/intervallo_pagine:937–940
- Publication Year :
- 2016
- Publisher :
- ACM, 2016.
-
Abstract
- In recent years there has been a growing interest in text quantification, a supervised learning task where the goal is to accurately estimate, in an unlabelled set of items, the prevalence (or "relative frequency") of each class c in a predefined set C. Text quantification has several applications, and is a dominant concern in fields such as market research, the social sciences, political science, and epidemiology. In this paper we tackle, for the first time, the problem of ordinal text quantification, defined as the task of performing text quantification when a total order is defined on the set of classes; estimating the prevalence of "five stars" reviews in a set of reviews of a given product, and monitoring this prevalence across time, is an example application. We present OQT, a novel tree-based OQ algorithm, and discuss experimental results obtained on a dataset of tweets classified according to sentiment strength.
- Subjects :
- ARTIFICIAL INTELLIGENCE. Learning
business.industry
Computer science
Supervised learning
Sentiment analysis
02 engineering and technology
Machine learning
computer.software_genre
Frequency
Task (project management)
Set (abstract data type)
Market research
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Ordinal number
020201 artificial intelligence & image processing
Artificial intelligence
Product (category theory)
Tree (set theory)
business
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
- Accession number :
- edsair.doi.dedup.....a2b5de57ab463a5cbe1c5e2afe30bb22