@inproceedings{misra-etal-2019-using,
title = "Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in Customer Service Conversations",
author = "Misra, Amita and
Bhuiyan, Mansurul and
Mahmud, Jalal and
Tripathy, Saurabh",
editor = "Balahur, Alexandra and
Klinger, Roman and
Hoste, Veronique and
Strapparava, Carlo and
De Clercq, Orphee",
booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = jun,
year = "2019",
address = "Minneapolis, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-1306",
doi = "10.18653/v1/W19-1306",
pages = "46--56",
abstract = "Twitter customer service interactions have recently emerged as an effective platform to respond and engage with customers. In this work, we explore the role of {''}negation{''} in customer service interactions, particularly applied to sentiment analysis. We define rules to identify true negation cues and scope more suited to conversational data than existing general review data. Using semantic knowledge and syntactic structure from constituency parse trees, we propose an algorithm for scope detection that performs comparable to state of the art BiLSTM. We further investigate the results of negation scope detection for the sentiment prediction task on customer service conversation data using both a traditional SVM and a Neural Network. We propose an antonym dictionary based method for negation applied to a combination CNN-LSTM for sentiment analysis. Experimental results show that the antonym-based method outperforms the previous lexicon-based and Neural Network methods.",
}
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<abstract>Twitter customer service interactions have recently emerged as an effective platform to respond and engage with customers. In this work, we explore the role of ”negation” in customer service interactions, particularly applied to sentiment analysis. We define rules to identify true negation cues and scope more suited to conversational data than existing general review data. Using semantic knowledge and syntactic structure from constituency parse trees, we propose an algorithm for scope detection that performs comparable to state of the art BiLSTM. We further investigate the results of negation scope detection for the sentiment prediction task on customer service conversation data using both a traditional SVM and a Neural Network. We propose an antonym dictionary based method for negation applied to a combination CNN-LSTM for sentiment analysis. Experimental results show that the antonym-based method outperforms the previous lexicon-based and Neural Network methods.</abstract>
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%0 Conference Proceedings
%T Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in Customer Service Conversations
%A Misra, Amita
%A Bhuiyan, Mansurul
%A Mahmud, Jalal
%A Tripathy, Saurabh
%Y Balahur, Alexandra
%Y Klinger, Roman
%Y Hoste, Veronique
%Y Strapparava, Carlo
%Y De Clercq, Orphee
%S Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, USA
%F misra-etal-2019-using
%X Twitter customer service interactions have recently emerged as an effective platform to respond and engage with customers. In this work, we explore the role of ”negation” in customer service interactions, particularly applied to sentiment analysis. We define rules to identify true negation cues and scope more suited to conversational data than existing general review data. Using semantic knowledge and syntactic structure from constituency parse trees, we propose an algorithm for scope detection that performs comparable to state of the art BiLSTM. We further investigate the results of negation scope detection for the sentiment prediction task on customer service conversation data using both a traditional SVM and a Neural Network. We propose an antonym dictionary based method for negation applied to a combination CNN-LSTM for sentiment analysis. Experimental results show that the antonym-based method outperforms the previous lexicon-based and Neural Network methods.
%R 10.18653/v1/W19-1306
%U https://aclanthology.org/W19-1306
%U https://doi.org/10.18653/v1/W19-1306
%P 46-56
Markdown (Informal)
[Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in Customer Service Conversations](https://aclanthology.org/W19-1306) (Misra et al., WASSA 2019)
ACL