<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yang-eisenstein-2017-overcoming">
    <titleInfo>
        <title>Overcoming Language Variation in Sentiment Analysis with Social Attention</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yi</namePart>
        <namePart type="family">Yang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jacob</namePart>
        <namePart type="family">Eisenstein</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017</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>Variation in language is ubiquitous, particularly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the author. In this paper, we show how to exploit social networks to make sentiment analysis more robust to social language variation. The key idea is linguistic homophily: the tendency of socially linked individuals to use language in similar ways. We formalize this idea in a novel attention-based neural network architecture, in which attention is divided among several basis models, depending on the author’s position in the social network. This has the effect of smoothing the classification function across the social network, and makes it possible to induce personalized classifiers even for authors for whom there is no labeled data or demographic metadata. This model significantly improves the accuracies of sentiment analysis on Twitter and on review data.</abstract>
    <identifier type="citekey">yang-eisenstein-2017-overcoming</identifier>
    <identifier type="doi">10.1162/tacl_a_00062</identifier>
    <location>
        <url>https://aclanthology.org/Q17-1021</url>
    </location>
    <part>
        <date>2017</date>
        <detail type="volume"><number>5</number></detail>
        <extent unit="page">
            <start>295</start>
            <end>307</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Overcoming Language Variation in Sentiment Analysis with Social Attention
%A Yang, Yi
%A Eisenstein, Jacob
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F yang-eisenstein-2017-overcoming
%X Variation in language is ubiquitous, particularly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the author. In this paper, we show how to exploit social networks to make sentiment analysis more robust to social language variation. The key idea is linguistic homophily: the tendency of socially linked individuals to use language in similar ways. We formalize this idea in a novel attention-based neural network architecture, in which attention is divided among several basis models, depending on the author’s position in the social network. This has the effect of smoothing the classification function across the social network, and makes it possible to induce personalized classifiers even for authors for whom there is no labeled data or demographic metadata. This model significantly improves the accuracies of sentiment analysis on Twitter and on review data.
%R 10.1162/tacl_a_00062
%U https://aclanthology.org/Q17-1021
%U https://doi.org/10.1162/tacl_a_00062
%P 295-307

Markdown (Informal)

[Overcoming Language Variation in Sentiment Analysis with Social Attention](https://aclanthology.org/Q17-1021) (Yang & Eisenstein, TACL 2017)

• Overcoming Language Variation in Sentiment Analysis with Social Attention (Yang & Eisenstein, TACL 2017)

ACL

• Yi Yang and Jacob Eisenstein. 2017. Overcoming Language Variation in Sentiment Analysis with Social Attention. Transactions of the Association for Computational Linguistics, 5:295–307.