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        <title>Personal Sense and Idiolect: Combining Authorship Attribution and Opinion Analysis</title>
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    <name type="personal">
        <namePart type="given">Polina</namePart>
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            <title>Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)</title>
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    <abstract>Subjectivity analysis and authorship attribution are very popular areas of research. However, work in these two areas has been done separately. We believe that by combining information about subjectivity in texts and authorship, the performance of both tasks can be improved. In the paper a personalized approach to opinion mining is presented, in which the notions of personal sense and idiolect are introduced; the approach is applied to the polarity classification task. It is assumed that different authors express their private states in text individually, and opinion mining results could be improved by analyzing texts by different authors separately. The hypothesis is tested on a corpus of movie reviews by ten authors. The results of applying the personalized approach to opinion mining are presented, confirming that the approach increases the performance of the opinion mining task. Automatic authorship attribution is further applied to model the personalized approach, classifying documents by their assumed authorship. Although the automatic authorship classification imposes a number of limitations on the dataset for further experiments, after overcoming these issues the authorship attribution technique modeling the personalized approach confirms the increase over the baseline with no authorship information used.</abstract>
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%0 Conference Proceedings
%T Personal Sense and Idiolect: Combining Authorship Attribution and Opinion Analysis
%A Panicheva, Polina
%A Cardiff, John
%A Rosso, Paolo
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 may
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F panicheva-etal-2010-personal
%X Subjectivity analysis and authorship attribution are very popular areas of research. However, work in these two areas has been done separately. We believe that by combining information about subjectivity in texts and authorship, the performance of both tasks can be improved. In the paper a personalized approach to opinion mining is presented, in which the notions of personal sense and idiolect are introduced; the approach is applied to the polarity classification task. It is assumed that different authors express their private states in text individually, and opinion mining results could be improved by analyzing texts by different authors separately. The hypothesis is tested on a corpus of movie reviews by ten authors. The results of applying the personalized approach to opinion mining are presented, confirming that the approach increases the performance of the opinion mining task. Automatic authorship attribution is further applied to model the personalized approach, classifying documents by their assumed authorship. Although the automatic authorship classification imposes a number of limitations on the dataset for further experiments, after overcoming these issues the authorship attribution technique modeling the personalized approach confirms the increase over the baseline with no authorship information used.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/491_Paper.pdf

Markdown (Informal)

[Personal Sense and Idiolect: Combining Authorship Attribution and Opinion Analysis](http://www.lrec-conf.org/proceedings/lrec2010/pdf/491_Paper.pdf) (Panicheva et al., LREC 2010)

• Personal Sense and Idiolect: Combining Authorship Attribution and Opinion Analysis (Panicheva et al., LREC 2010)

ACL

• Polina Panicheva, John Cardiff, and Paolo Rosso. 2010. Personal Sense and Idiolect: Combining Authorship Attribution and Opinion Analysis. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).