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        <title>SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model</title>
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        <namePart type="given">Shuying</namePart>
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        <namePart type="given">Huosheng</namePart>
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    <name type="personal">
        <namePart type="given">Liang-Chih</namePart>
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            <title>Proceedings of the IJCNLP 2017, Shared Tasks</title>
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            <namePart type="given">Chao-Hong</namePart>
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            <namePart type="given">Nianwen</namePart>
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    <abstract>The analysis of customer feedback is useful to provide good customer service. There are a lot of online customer feedback are produced. Manual classification is impractical because the high volume of data. Therefore, the automatic classification of the customer feedback is of importance for the analysis system to identify meanings or intentions that the customer express. The aim of shared Task 4 of IJCNLP 2017 is to classify the customer feedback into six tags categorization. In this paper, we present a system that uses word embeddings to express the feature of the sentence in the corpus and the neural network as the classifier to complete the shared task. And then the ensemble method is used to get final predictive result. The proposed method get ranked first among twelve teams in terms of micro-averaged F1 and second for accura-cy metric.</abstract>
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%0 Conference Proceedings
%T SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model
%A Lin, Shuying
%A Xie, Huosheng
%A Yu, Liang-Chih
%A Lai, K. Robert
%Y Liu, Chao-Hong
%Y Nakov, Preslav
%Y Xue, Nianwen
%S Proceedings of the IJCNLP 2017, Shared Tasks
%D 2017
%8 December
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F lin-etal-2017-sentinlp
%X The analysis of customer feedback is useful to provide good customer service. There are a lot of online customer feedback are produced. Manual classification is impractical because the high volume of data. Therefore, the automatic classification of the customer feedback is of importance for the analysis system to identify meanings or intentions that the customer express. The aim of shared Task 4 of IJCNLP 2017 is to classify the customer feedback into six tags categorization. In this paper, we present a system that uses word embeddings to express the feature of the sentence in the corpus and the neural network as the classifier to complete the shared task. And then the ensemble method is used to get final predictive result. The proposed method get ranked first among twelve teams in terms of micro-averaged F1 and second for accura-cy metric.
%U https://aclanthology.org/I17-4025
%P 149-154

Markdown (Informal)

[SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model](https://aclanthology.org/I17-4025) (Lin et al., IJCNLP 2017)

• SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model (Lin et al., IJCNLP 2017)

ACL

• Shuying Lin, Huosheng Xie, Liang-Chih Yu, and K. Robert Lai. 2017. SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 149–154, Taipei, Taiwan. Asian Federation of Natural Language Processing.