Polarity classification using structure-based vector representations of text

Alexander Hogenboom, Flavius Frasincar, Franciska M.G. de Jong, Uzay Kaymak

    Research output: Contribution to journalArticleAcademicpeer-review

    12 Citations (Scopus)

    Abstract

    The exploitation of structural aspects of content is becoming increasingly popular in rule-based polarity classification systems. Such systems typically weight the sentiment conveyed by text segments in accordance with these segments' roles in the structure of a text, as identified by deep linguistic processing. Conversely, state-of-the-art machine learning polarity classifiers typically aim to exploit patterns in vector representations of texts, mostly covering the occurrence of words or word groups in these texts. However, since structural aspects of content have been shown to contain valuable information as well, we propose to use structure-based features in vector representations of text. We evaluate the usefulness of our novel features on collections of English reviews in various domains. Our experimental results suggest that, even though word-based features are indispensable to good polarity classifiers, structure-based sentiment information provides valuable additional guidance that can help significantly improve the polarity classification performance of machine learning classifiers. The most informative features capture the sentiment conveyed by specific rhetorical elements that constitute a text's core or provide crucial contextual information.
    Original languageUndefined
    Pages (from-to)46-56
    Number of pages11
    JournalDecision support systems
    Volume74
    Issue numberJune
    DOIs
    Publication statusPublished - Jun 2015

    Keywords

    • EWI-25971
    • METIS-312577
    • IR-96333

    Cite this

    Hogenboom, Alexander ; Frasincar, Flavius ; de Jong, Franciska M.G. ; Kaymak, Uzay. / Polarity classification using structure-based vector representations of text. In: Decision support systems. 2015 ; Vol. 74, No. June. pp. 46-56.
    @article{07d4accbaebc4303a000fe492ace44d0,
    title = "Polarity classification using structure-based vector representations of text",
    abstract = "The exploitation of structural aspects of content is becoming increasingly popular in rule-based polarity classification systems. Such systems typically weight the sentiment conveyed by text segments in accordance with these segments' roles in the structure of a text, as identified by deep linguistic processing. Conversely, state-of-the-art machine learning polarity classifiers typically aim to exploit patterns in vector representations of texts, mostly covering the occurrence of words or word groups in these texts. However, since structural aspects of content have been shown to contain valuable information as well, we propose to use structure-based features in vector representations of text. We evaluate the usefulness of our novel features on collections of English reviews in various domains. Our experimental results suggest that, even though word-based features are indispensable to good polarity classifiers, structure-based sentiment information provides valuable additional guidance that can help significantly improve the polarity classification performance of machine learning classifiers. The most informative features capture the sentiment conveyed by specific rhetorical elements that constitute a text's core or provide crucial contextual information.",
    keywords = "EWI-25971, METIS-312577, IR-96333",
    author = "Alexander Hogenboom and Flavius Frasincar and {de Jong}, {Franciska M.G.} and Uzay Kaymak",
    note = "eemcs-eprint-25971",
    year = "2015",
    month = "6",
    doi = "10.1016/j.dss.2015.04.002",
    language = "Undefined",
    volume = "74",
    pages = "46--56",
    journal = "Decision support systems",
    issn = "0167-9236",
    publisher = "Elsevier",
    number = "June",

    }

    Polarity classification using structure-based vector representations of text. / Hogenboom, Alexander; Frasincar, Flavius; de Jong, Franciska M.G.; Kaymak, Uzay.

    In: Decision support systems, Vol. 74, No. June, 06.2015, p. 46-56.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - Polarity classification using structure-based vector representations of text

    AU - Hogenboom, Alexander

    AU - Frasincar, Flavius

    AU - de Jong, Franciska M.G.

    AU - Kaymak, Uzay

    N1 - eemcs-eprint-25971

    PY - 2015/6

    Y1 - 2015/6

    N2 - The exploitation of structural aspects of content is becoming increasingly popular in rule-based polarity classification systems. Such systems typically weight the sentiment conveyed by text segments in accordance with these segments' roles in the structure of a text, as identified by deep linguistic processing. Conversely, state-of-the-art machine learning polarity classifiers typically aim to exploit patterns in vector representations of texts, mostly covering the occurrence of words or word groups in these texts. However, since structural aspects of content have been shown to contain valuable information as well, we propose to use structure-based features in vector representations of text. We evaluate the usefulness of our novel features on collections of English reviews in various domains. Our experimental results suggest that, even though word-based features are indispensable to good polarity classifiers, structure-based sentiment information provides valuable additional guidance that can help significantly improve the polarity classification performance of machine learning classifiers. The most informative features capture the sentiment conveyed by specific rhetorical elements that constitute a text's core or provide crucial contextual information.

    AB - The exploitation of structural aspects of content is becoming increasingly popular in rule-based polarity classification systems. Such systems typically weight the sentiment conveyed by text segments in accordance with these segments' roles in the structure of a text, as identified by deep linguistic processing. Conversely, state-of-the-art machine learning polarity classifiers typically aim to exploit patterns in vector representations of texts, mostly covering the occurrence of words or word groups in these texts. However, since structural aspects of content have been shown to contain valuable information as well, we propose to use structure-based features in vector representations of text. We evaluate the usefulness of our novel features on collections of English reviews in various domains. Our experimental results suggest that, even though word-based features are indispensable to good polarity classifiers, structure-based sentiment information provides valuable additional guidance that can help significantly improve the polarity classification performance of machine learning classifiers. The most informative features capture the sentiment conveyed by specific rhetorical elements that constitute a text's core or provide crucial contextual information.

    KW - EWI-25971

    KW - METIS-312577

    KW - IR-96333

    U2 - 10.1016/j.dss.2015.04.002

    DO - 10.1016/j.dss.2015.04.002

    M3 - Article

    VL - 74

    SP - 46

    EP - 56

    JO - Decision support systems

    JF - Decision support systems

    SN - 0167-9236

    IS - June

    ER -