<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="west-etal-2014-exploiting">
    <titleInfo>
        <title>Exploiting Social Network Structure for Person-to-Person Sentiment Analysis</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Robert</namePart>
        <namePart type="family">West</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hristo</namePart>
        <namePart type="given">S</namePart>
        <namePart type="family">Paskov</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jure</namePart>
        <namePart type="family">Leskovec</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Christopher</namePart>
        <namePart type="family">Potts</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2014</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A’s opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.</abstract>
    <identifier type="citekey">west-etal-2014-exploiting</identifier>
    <identifier type="doi">10.1162/tacl_a_00184</identifier>
    <location>
        <url>https://aclanthology.org/Q14-1024</url>
    </location>
    <part>
        <date>2014</date>
        <detail type="volume"><number>2</number></detail>
        <extent unit="page">
            <start>297</start>
            <end>310</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Exploiting Social Network Structure for Person-to-Person Sentiment Analysis
%A West, Robert
%A Paskov, Hristo S.
%A Leskovec, Jure
%A Potts, Christopher
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F west-etal-2014-exploiting
%X Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A’s opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.
%R 10.1162/tacl_a_00184
%U https://aclanthology.org/Q14-1024
%U https://doi.org/10.1162/tacl_a_00184
%P 297-310

Markdown (Informal)

[Exploiting Social Network Structure for Person-to-Person Sentiment Analysis](https://aclanthology.org/Q14-1024) (West et al., TACL 2014)

• Exploiting Social Network Structure for Person-to-Person Sentiment Analysis (West et al., TACL 2014)

ACL

• Robert West, Hristo S. Paskov, Jure Leskovec, and Christopher Potts. 2014. Exploiting Social Network Structure for Person-to-Person Sentiment Analysis. Transactions of the Association for Computational Linguistics, 2:297–310.