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    <titleInfo>
        <title>Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora</title>
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    <name type="personal">
        <namePart type="given">Andrius</namePart>
        <namePart type="family">Mudinas</namePart>
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    <name type="personal">
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        <namePart type="family">Zhang</namePart>
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    <name type="personal">
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    <abstract>There is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier or a pre-built one for a different domain, the effectiveness would be inferior. In this paper, we explore the possibility of building domain-specific sentiment classifiers with unlabeled documents only. Our investigation indicates that in the word embeddings learned from the unlabeled corpus of a given domain, the distributed word representations (vectors) for opposite sentiments form distinct clusters, though those clusters are not transferable across domains. Exploiting such a clustering structure, we are able to utilize machine learning algorithms to induce a quality domain-specific sentiment lexicon from just a few typical sentiment words (“seeds”). An important finding is that simple linear model based supervised learning algorithms (such as linear SVM) can actually work better than more sophisticated semi-supervised/transductive learning algorithms which represent the state-of-the-art technique for sentiment lexicon induction. The induced lexicon could be applied directly in a lexicon-based method for sentiment classification, but a higher performance could be achieved through a two-phase bootstrapping method which uses the induced lexicon to assign positive/negative sentiment scores to unlabeled documents first, a nd t hen u ses those documents found to have clear sentiment signals as pseudo-labeled examples to train a document sentiment classifier v ia supervised learning algorithms (such as LSTM). On several benchmark datasets for document sentiment classification, our end-to-end pipelined approach which is overall unsupervised (except for a tiny set of seed words) outperforms existing unsupervised approaches and achieves an accuracy comparable to that of fully supervised approaches.</abstract>
    <identifier type="citekey">mudinas-etal-2018-bootstrap</identifier>
    <identifier type="doi">10.1162/tacl_a_00020</identifier>
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        <date>2018</date>
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            <start>269</start>
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%0 Journal Article
%T Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora
%A Mudinas, Andrius
%A Zhang, Dell
%A Levene, Mark
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F mudinas-etal-2018-bootstrap
%X There is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier or a pre-built one for a different domain, the effectiveness would be inferior. In this paper, we explore the possibility of building domain-specific sentiment classifiers with unlabeled documents only. Our investigation indicates that in the word embeddings learned from the unlabeled corpus of a given domain, the distributed word representations (vectors) for opposite sentiments form distinct clusters, though those clusters are not transferable across domains. Exploiting such a clustering structure, we are able to utilize machine learning algorithms to induce a quality domain-specific sentiment lexicon from just a few typical sentiment words (“seeds”). An important finding is that simple linear model based supervised learning algorithms (such as linear SVM) can actually work better than more sophisticated semi-supervised/transductive learning algorithms which represent the state-of-the-art technique for sentiment lexicon induction. The induced lexicon could be applied directly in a lexicon-based method for sentiment classification, but a higher performance could be achieved through a two-phase bootstrapping method which uses the induced lexicon to assign positive/negative sentiment scores to unlabeled documents first, a nd t hen u ses those documents found to have clear sentiment signals as pseudo-labeled examples to train a document sentiment classifier v ia supervised learning algorithms (such as LSTM). On several benchmark datasets for document sentiment classification, our end-to-end pipelined approach which is overall unsupervised (except for a tiny set of seed words) outperforms existing unsupervised approaches and achieves an accuracy comparable to that of fully supervised approaches.
%R 10.1162/tacl_a_00020
%U https://aclanthology.org/Q18-1020
%U https://doi.org/10.1162/tacl_a_00020
%P 269-285

Markdown (Informal)

[Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora](https://aclanthology.org/Q18-1020) (Mudinas et al., TACL 2018)

• Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora (Mudinas et al., TACL 2018)

ACL

• Andrius Mudinas, Dell Zhang, and Mark Levene. 2018. Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora. Transactions of the Association for Computational Linguistics, 6:269–285.