SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model

2017-12-01 · IJCNLP 2017 12 · Shuying Lin, Huosheng Xie, Liang-Chih Yu, K. Robert Lai

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.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

General Classification Multi-Label Classification Sarcasm DetectionSentenceSentence Classification Sentiment AnalysisWord Embeddings

NITMZ-JU at IJCNLP-2017 Task 4: Customer Feedback Analysis

2017-12-01 · IJCNLP 2017 12 · Somnath Banerjee, Partha Pakray, Riyanka Manna, Dipankar Das 외

In this paper, we describe a deep learning framework for analyzing the customer feedback as part of our participation in the shared task on Customer Feedback Analysis at the 8th International Joint Conference on Natural …

Text Classification

IJCNLP-2017 Task 4: Customer Feedback Analysis

2017-12-01 · IJCNLP 2017 12 · Chao-Hong Liu, Yasufumi Moriya, Alberto Poncelas, Declan Groves

This document introduces the IJCNLP 2017 Shared Task on Customer Feedback Analysis. In this shared task we have prepared corpora of customer feedback in four languages, i.e. English, French, Spanish and Japanese. They we…

Machine Translation Prediction

IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network

2017-12-01 · IJCNLP 2017 12 · Deepak Gupta, Pabitra Lenka, Harsimran Bedi, Asif Ekbal 외

Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers. Therefore an automated system which can analyze the customer behavior is i…

Document Classification Emotion Classification Marketing Sentiment Analysis

OhioState at IJCNLP-2017 Task 4: Exploring Neural Architectures for Multilingual Customer Feedback Analysis

2017-10-18 · IJCNLP 2017 12 · Dushyanta Dhyani

This paper describes our systems for IJCNLP 2017 Shared Task on Customer Feedback Analysis. We experimented with simple neural architectures that gave competitive performance on certain tasks. This includes shallow CNN a…

Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification

2017-12-01 · IJCNLP 2017 12 · Heba Elfardy, Manisha Srivastava, Wei Xiao, Jared Kramer 외

The ability to automatically and accurately process customer feedback is a necessity in the private sector. Unfortunately, customer feedback can be one of the most difficult types of data to work with due to the sheer vo…

Feature Engineering General Classification Machine Translation Sentiment Analysis+3