@inproceedings{srivastava-singh-2020-iit,
title = "{IIT} {G}andhinagar at {S}em{E}val-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection",
author = "Srivastava, Vivek and
Singh, Mayank",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.168",
doi = "10.18653/v1/2020.semeval-1.168",
pages = "1259--1264",
abstract = "Code-mixing is the phenomenon of using multiple languages in the same utterance. It is a frequently used pattern of communication on social media sites such as Facebook, Twitter, etc. Sentiment analysis of the monolingual text is a well-studied task. Code-mixing adds to the challenge of analyzing the sentiment of the text on various platforms such as social media, online gaming, forums, product reviews, etc. We present a candidate sentence generation and selection based approach on top of the Bi-LSTM based neural classifier to classify the Hinglish code-mixed text into one of the three sentiment classes positive, negative, or neutral. The proposed candidate sentence generation and selection based approach show an improvement in the system performance as compared to the Bi-LSTM based neural classifier. We can extend the proposed method to solve other problems with code-mixing in the textual data, such as humor-detection, intent classification, etc.",
}
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<abstract>Code-mixing is the phenomenon of using multiple languages in the same utterance. It is a frequently used pattern of communication on social media sites such as Facebook, Twitter, etc. Sentiment analysis of the monolingual text is a well-studied task. Code-mixing adds to the challenge of analyzing the sentiment of the text on various platforms such as social media, online gaming, forums, product reviews, etc. We present a candidate sentence generation and selection based approach on top of the Bi-LSTM based neural classifier to classify the Hinglish code-mixed text into one of the three sentiment classes positive, negative, or neutral. The proposed candidate sentence generation and selection based approach show an improvement in the system performance as compared to the Bi-LSTM based neural classifier. We can extend the proposed method to solve other problems with code-mixing in the textual data, such as humor-detection, intent classification, etc.</abstract>
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%0 Conference Proceedings
%T IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection
%A Srivastava, Vivek
%A Singh, Mayank
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 dec
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F srivastava-singh-2020-iit
%X Code-mixing is the phenomenon of using multiple languages in the same utterance. It is a frequently used pattern of communication on social media sites such as Facebook, Twitter, etc. Sentiment analysis of the monolingual text is a well-studied task. Code-mixing adds to the challenge of analyzing the sentiment of the text on various platforms such as social media, online gaming, forums, product reviews, etc. We present a candidate sentence generation and selection based approach on top of the Bi-LSTM based neural classifier to classify the Hinglish code-mixed text into one of the three sentiment classes positive, negative, or neutral. The proposed candidate sentence generation and selection based approach show an improvement in the system performance as compared to the Bi-LSTM based neural classifier. We can extend the proposed method to solve other problems with code-mixing in the textual data, such as humor-detection, intent classification, etc.
%R 10.18653/v1/2020.semeval-1.168
%U https://aclanthology.org/2020.semeval-1.168
%U https://doi.org/10.18653/v1/2020.semeval-1.168
%P 1259-1264
Markdown (Informal)
[IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection](https://aclanthology.org/2020.semeval-1.168) (Srivastava & Singh, SemEval 2020)
ACL