NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture

Nikolay Karpov


Abstract
In many areas, such as social science, politics or market research, people need to deal with dataset shifting over time. Distribution drift phenomenon usually appears in the field of sentiment analysis, when proportions of instances are changing over time. In this case, the task is to correctly estimate proportions of each sentiment expressed in the set of documents (quantification task). Basically, our study was aimed to analyze the effectiveness of a mixture of quantification technique with one of deep learning architecture. All the techniques are evaluated using the SemEval-2017 Task4 dataset and source code, mentioned in this paper and available online in the Python programming language. The results of an application of the quantification techniques are discussed.
Anthology ID:
S17-2113
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
683–688
Language:
URL:
https://www.aclweb.org/anthology/S17-2113
DOI:
10.18653/v1/S17-2113
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2113.pdf