--- abstract: 'The evaluative character of a word is called its semantic orientation. A positive semantic orientation implies desirability (e.g., "honest", "intrepid") and a negative semantic orientation implies undesirability (e.g., "disturbing", "superfluous"). This paper introduces a simple algorithm for unsupervised learning of semantic orientation from extremely large corpora. The method involves issuing queries to a Web search engine and using pointwise mutual information to analyse the results. The algorithm is empirically evaluated using a training corpus of approximately one hundred billion words the subset of the Web that is indexed by the chosen search engine. Tested with 3,596 words (1,614 positive and 1,982 negative), the algorithm attains an accuracy of 80%. The 3,596 test words include adjectives, adverbs, nouns, and verbs. The accuracy is comparable with the results achieved by Hatzivassiloglou and McKeown (1997), using a complex four-stage supervised learning algorithm that is restricted to determining the semantic orientation of adjectives. ' altloc: - http://extractor.iit.nrc.ca/reports/ERB-1094.pdf chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: [] creators_name: - family: Turney given: Peter D. honourific: '' lineage: '' - family: Littman given: Michael L. honourific: '' lineage: '' date: 2002 date_type: published datestamp: 2002-07-15 department: Institute for Information Technology dir: disk0/00/00/23/22 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 2322 fileinfo: /style/images/fileicons/application_postscript.png;/2322/1/ERB%2D1094.ps|/style/images/fileicons/application_pdf.png;/2322/5/ERB%2D1094.pdf full_text_status: public importid: ~ institution: National Research Council Canada isbn: ~ ispublished: unpub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: ~ lastmod: 2011-03-11 08:54:57 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: ~ pubdom: FALSE publication: ~ publisher: ~ refereed: FALSE referencetext: ~ relation_type: [] relation_uri: [] reportno: NRC Technical Report ERB-1094 rev_number: 14 series: ~ source: ~ status_changed: 2007-09-12 16:44:14 subjects: - comp-sci-art-intel - comp-sci-lang - comp-sci-mach-learn - comp-sci-stat-model succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus type: techreport userid: 2175 volume: ~