@inproceedings{s-etal-2018-ssn-mlrg1,
title = "{SSN} {MLRG}1 at {S}em{E}val-2018 Task 3: Irony Detection in {E}nglish Tweets Using {M}ulti{L}ayer Perceptron",
author = "S, Rajalakshmi and
S, Angel Deborah and
Rajendram, S Milton and
T T, Mirnalinee",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1103",
doi = "10.18653/v1/S18-1103",
pages = "633--637",
abstract = "Sentiment analysis plays an important role in E-commerce. Identifying ironic and sarcastic content in text plays a vital role in inferring the actual intention of the user, and is necessary to increase the accuracy of sentiment analysis. This paper describes the work on identifying the irony level in twitter texts. The system developed by the SSN MLRG1 team in SemEval-2018 for task 3 (irony detection) uses rule based approach for feature selection and MultiLayer Perceptron (MLP) technique to build the model for multiclass irony classification subtask, which classifies the given text into one of the four class labels.",
}
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%0 Conference Proceedings
%T SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron
%A S, Rajalakshmi
%A S, Angel Deborah
%A Rajendram, S. Milton
%A T T, Mirnalinee
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F s-etal-2018-ssn-mlrg1
%X Sentiment analysis plays an important role in E-commerce. Identifying ironic and sarcastic content in text plays a vital role in inferring the actual intention of the user, and is necessary to increase the accuracy of sentiment analysis. This paper describes the work on identifying the irony level in twitter texts. The system developed by the SSN MLRG1 team in SemEval-2018 for task 3 (irony detection) uses rule based approach for feature selection and MultiLayer Perceptron (MLP) technique to build the model for multiclass irony classification subtask, which classifies the given text into one of the four class labels.
%R 10.18653/v1/S18-1103
%U https://aclanthology.org/S18-1103
%U https://doi.org/10.18653/v1/S18-1103
%P 633-637
Markdown (Informal)
[SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron](https://aclanthology.org/S18-1103) (S et al., SemEval 2018)
ACL