@inproceedings{tumarada-etal-2021-opinion,
title = "Opinion Prediction with User Fingerprinting",
author = "Tumarada, Kishore and
Zhang, Yifan and
Yang, Fan and
Dragut, Eduard and
Gnawali, Omprakash and
Mukherjee, Arjun",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.159",
pages = "1423--1431",
abstract = "Opinion prediction is an emerging research area with diverse real-world applications, such as market research and situational awareness. We identify two lines of approaches to the problem of opinion prediction. One uses topic-based sentiment analysis with time-series modeling, while the other uses static embedding of text. The latter approaches seek user-specific solutions by generating user fingerprints. Such approaches are useful in predicting user{'}s reactions to unseen content. In this work, we propose a novel dynamic fingerprinting method that leverages contextual embedding of user{'}s comments conditioned on relevant user{'}s reading history. We integrate BERT variants with a recurrent neural network to generate predictions. The results show up to 13{\%} improvement in micro F1-score compared to previous approaches. Experimental results show novel insights that were previously unknown such as better predictions for an increase in dynamic history length, the impact of the nature of the article on performance, thereby laying the foundation for further research.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tumarada-etal-2021-opinion">
<titleInfo>
<title>Opinion Prediction with User Fingerprinting</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kishore</namePart>
<namePart type="family">Tumarada</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yifan</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fan</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eduard</namePart>
<namePart type="family">Dragut</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Omprakash</namePart>
<namePart type="family">Gnawali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Arjun</namePart>
<namePart type="family">Mukherjee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-sep</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
</titleInfo>
<originInfo>
<publisher>INCOMA Ltd.</publisher>
<place>
<placeTerm type="text">Held Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Opinion prediction is an emerging research area with diverse real-world applications, such as market research and situational awareness. We identify two lines of approaches to the problem of opinion prediction. One uses topic-based sentiment analysis with time-series modeling, while the other uses static embedding of text. The latter approaches seek user-specific solutions by generating user fingerprints. Such approaches are useful in predicting user’s reactions to unseen content. In this work, we propose a novel dynamic fingerprinting method that leverages contextual embedding of user’s comments conditioned on relevant user’s reading history. We integrate BERT variants with a recurrent neural network to generate predictions. The results show up to 13% improvement in micro F1-score compared to previous approaches. Experimental results show novel insights that were previously unknown such as better predictions for an increase in dynamic history length, the impact of the nature of the article on performance, thereby laying the foundation for further research.</abstract>
<identifier type="citekey">tumarada-etal-2021-opinion</identifier>
<location>
<url>https://aclanthology.org/2021.ranlp-1.159</url>
</location>
<part>
<date>2021-sep</date>
<extent unit="page">
<start>1423</start>
<end>1431</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Opinion Prediction with User Fingerprinting
%A Tumarada, Kishore
%A Zhang, Yifan
%A Yang, Fan
%A Dragut, Eduard
%A Gnawali, Omprakash
%A Mukherjee, Arjun
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 sep
%I INCOMA Ltd.
%C Held Online
%F tumarada-etal-2021-opinion
%X Opinion prediction is an emerging research area with diverse real-world applications, such as market research and situational awareness. We identify two lines of approaches to the problem of opinion prediction. One uses topic-based sentiment analysis with time-series modeling, while the other uses static embedding of text. The latter approaches seek user-specific solutions by generating user fingerprints. Such approaches are useful in predicting user’s reactions to unseen content. In this work, we propose a novel dynamic fingerprinting method that leverages contextual embedding of user’s comments conditioned on relevant user’s reading history. We integrate BERT variants with a recurrent neural network to generate predictions. The results show up to 13% improvement in micro F1-score compared to previous approaches. Experimental results show novel insights that were previously unknown such as better predictions for an increase in dynamic history length, the impact of the nature of the article on performance, thereby laying the foundation for further research.
%U https://aclanthology.org/2021.ranlp-1.159
%P 1423-1431
Markdown (Informal)
[Opinion Prediction with User Fingerprinting](https://aclanthology.org/2021.ranlp-1.159) (Tumarada et al., RANLP 2021)
ACL
- Kishore Tumarada, Yifan Zhang, Fan Yang, Eduard Dragut, Omprakash Gnawali, and Arjun Mukherjee. 2021. Opinion Prediction with User Fingerprinting. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1423–1431, Held Online. INCOMA Ltd..