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        <title>JUNLP at IJCNLP-2017 Task 3: A Rank Prediction Model for Review Opinion Diversification</title>
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        <namePart type="given">Monalisa</namePart>
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            <namePart type="given">Chao-Hong</namePart>
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            <namePart type="given">Nianwen</namePart>
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    <abstract>IJCNLP-17 Review Opinion Diversification (RevOpiD-2017) task has been designed for ranking the top-k reviews of a product from a set of reviews, which assists in identifying a summarized output to express the opinion of the entire review set. The task is divided into three independent subtasks as subtask-A,subtask-B, and subtask-C. Each of these three subtasks selects the top-k reviews based on helpfulness, representativeness, and exhaustiveness of the opinions expressed in the review set individually. In order to develop the modules and predict the rank of reviews for all three subtasks, we have employed two well-known supervised classifiers namely, Naïve Bayes and Logistic Regression on the top of several extracted features such as the number of nouns, number of verbs, and number of sentiment words etc from the provided datasets. Finally, the organizers have helped to validate the predicted outputs for all three subtasks by using their evaluation metrics. The metrics provide the scores of list size 5 as (0.80 (mth)) for subtask-A, (0.86 (cos), 0.87 (cos d), 0.71 (cpr), 4.98 (a-dcg), and 556.94 (wt)) for subtask B, and (10.94 (unwt) and 0.67 (recall)) for subtask C individually.</abstract>
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%0 Conference Proceedings
%T JUNLP at IJCNLP-2017 Task 3: A Rank Prediction Model for Review Opinion Diversification
%A Dey, Monalisa
%A Mondal, Anupam
%A Das, Dipankar
%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 dey-etal-2017-junlp
%X IJCNLP-17 Review Opinion Diversification (RevOpiD-2017) task has been designed for ranking the top-k reviews of a product from a set of reviews, which assists in identifying a summarized output to express the opinion of the entire review set. The task is divided into three independent subtasks as subtask-A,subtask-B, and subtask-C. Each of these three subtasks selects the top-k reviews based on helpfulness, representativeness, and exhaustiveness of the opinions expressed in the review set individually. In order to develop the modules and predict the rank of reviews for all three subtasks, we have employed two well-known supervised classifiers namely, Naïve Bayes and Logistic Regression on the top of several extracted features such as the number of nouns, number of verbs, and number of sentiment words etc from the provided datasets. Finally, the organizers have helped to validate the predicted outputs for all three subtasks by using their evaluation metrics. The metrics provide the scores of list size 5 as (0.80 (mth)) for subtask-A, (0.86 (cos), 0.87 (cos d), 0.71 (cpr), 4.98 (a-dcg), and 556.94 (wt)) for subtask B, and (10.94 (unwt) and 0.67 (recall)) for subtask C individually.
%U https://aclanthology.org/I17-4023
%P 138-142

Markdown (Informal)

[JUNLP at IJCNLP-2017 Task 3: A Rank Prediction Model for Review Opinion Diversification](https://aclanthology.org/I17-4023) (Dey et al., IJCNLP 2017)

• JUNLP at IJCNLP-2017 Task 3: A Rank Prediction Model for Review Opinion Diversification (Dey et al., IJCNLP 2017)

ACL

• Monalisa Dey, Anupam Mondal, and Dipankar Das. 2017. JUNLP at IJCNLP-2017 Task 3: A Rank Prediction Model for Review Opinion Diversification. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 138–142, Taipei, Taiwan. Asian Federation of Natural Language Processing.