--- abstract: 'The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This paper introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words. ' altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: [] creators_name: - family: Turney given: Peter honourific: '' lineage: '' - family: Littman given: Michael honourific: '' lineage: '' date: 2003 date_type: published datestamp: 2003-09-19 department: ~ dir: disk0/00/00/31/64 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 3164 fileinfo: /style/images/fileicons/application_pdf.png;/3164/1/turney%2Dlittman%2Dacm.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub 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: 'semantic orientation, semantic association, web mining, text mining, text classification, unsupervised learning, mutual information, latent semantic analysis' lastmod: 2011-03-11 08:55:20 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: 4 pagerange: 315-346 pubdom: FALSE publication: ACM Transactions on Information Systems (TOIS) publisher: ~ refereed: TRUE referencetext: |+ AGRESTI, A. 1996. 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Aalborg, Denmark. relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 16:48:45 subjects: - comp-sci-stat-model - comp-sci-lang - ling-comput - ling-sem - comp-sci-mach-learn succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: 'Measuring praise and criticism: Inference of semantic orientation from association' type: journalp userid: 2175 volume: 21