Age Detection in Chat
This paper presents the results of using statistical analysis and automatic text categorization to identify an author's age group based on the author's online chat posts. A naive Bayesian classifier and support vector machine (SVM) model were used. The SVM model experiments generated an f-...
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| creator | Tam, J. Martell, C.H. |
| description | This paper presents the results of using statistical analysis and automatic text categorization to identify an author's age group based on the author's online chat posts. A naive Bayesian classifier and support vector machine (SVM) model were used. The SVM model experiments generated an f-score measurement of 0.996 on test data distinguishing teens from adults. We also introduce an alternative method for generating ldquostop wordsrdquo that chooses n-grams based on their relative distribution across the classes. |
| doi_str_mv | 10.1109/ICSC.2009.37 |
| format | Conference Proceeding |
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A naive Bayesian classifier and support vector machine (SVM) model were used. The SVM model experiments generated an f-score measurement of 0.996 on test data distinguishing teens from adults. We also introduce an alternative method for generating ldquostop wordsrdquo that chooses n-grams based on their relative distribution across the classes.</description><identifier>ISBN: 1424449626</identifier><identifier>ISBN: 9781424449620</identifier><identifier>EISBN: 9780769538006</identifier><identifier>EISBN: 0769538002</identifier><identifier>DOI: 10.1109/ICSC.2009.37</identifier><language>eng</language><publisher>IEEE</publisher><subject>age classification ; Bayesian methods ; Computer science ; Internet ; Law enforcement ; Naïve Bayesian Classifier ; Niobium compounds ; online chat ; Statistical analysis ; stop words ; Support Vector Machine ; Support vector machine classification ; Support vector machines ; Testing ; Text categorization</subject><ispartof>2009 IEEE International Conference on Semantic Computing, 2009, p.33-39</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5298540$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,2052,27906,54901</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5298540$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tam, J.</creatorcontrib><creatorcontrib>Martell, C.H.</creatorcontrib><title>Age Detection in Chat</title><title>2009 IEEE International Conference on Semantic Computing</title><addtitle>ICOSC</addtitle><description>This paper presents the results of using statistical analysis and automatic text categorization to identify an author's age group based on the author's online chat posts. A naive Bayesian classifier and support vector machine (SVM) model were used. The SVM model experiments generated an f-score measurement of 0.996 on test data distinguishing teens from adults. We also introduce an alternative method for generating ldquostop wordsrdquo that chooses n-grams based on their relative distribution across the classes.</description><subject>age classification</subject><subject>Bayesian methods</subject><subject>Computer science</subject><subject>Internet</subject><subject>Law enforcement</subject><subject>Naïve Bayesian Classifier</subject><subject>Niobium compounds</subject><subject>online chat</subject><subject>Statistical analysis</subject><subject>stop words</subject><subject>Support Vector Machine</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>Testing</subject><subject>Text categorization</subject><isbn>1424449626</isbn><isbn>9781424449620</isbn><isbn>9780769538006</isbn><isbn>0769538002</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotzLtOw0AQQNFFCAkI7qCi8Q_YzMzOPqaMzCtSJAqgjtabWVgEAcVu-HuQ4Danu8acI_SIIFer4XHoCUB6Gw5MIyFC8OJsBPCH5hSZmFk8-WPTTNMb_MaOovMn5mL5ou21zprn-rlr664dXtN8Zo5Kep-0-Xdhnm9vnob7bv1wtxqW665icHM3Rp9CQFWMkO0WCjkeJRfNJDlxEHHitpwISyZMEX0AEgG1tkTLo12Yy79vVdXN175-pP33xpFEx2B_AO2hOCk</recordid><startdate>200909</startdate><enddate>200909</enddate><creator>Tam, J.</creator><creator>Martell, C.H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200909</creationdate><title>Age Detection in Chat</title><author>Tam, J. ; Martell, C.H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-b86a771ee180c3d0f254b9cfec29ca4799595d4a21fc21a816702990e33f834b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>age classification</topic><topic>Bayesian methods</topic><topic>Computer science</topic><topic>Internet</topic><topic>Law enforcement</topic><topic>Naïve Bayesian Classifier</topic><topic>Niobium compounds</topic><topic>online chat</topic><topic>Statistical analysis</topic><topic>stop words</topic><topic>Support Vector Machine</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><topic>Testing</topic><topic>Text categorization</topic><toplevel>online_resources</toplevel><creatorcontrib>Tam, J.</creatorcontrib><creatorcontrib>Martell, C.H.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tam, J.</au><au>Martell, C.H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Age Detection in Chat</atitle><btitle>2009 IEEE International Conference on Semantic Computing</btitle><stitle>ICOSC</stitle><date>2009-09</date><risdate>2009</risdate><spage>33</spage><epage>39</epage><pages>33-39</pages><isbn>1424449626</isbn><isbn>9781424449620</isbn><eisbn>9780769538006</eisbn><eisbn>0769538002</eisbn><abstract>This paper presents the results of using statistical analysis and automatic text categorization to identify an author's age group based on the author's online chat posts. A naive Bayesian classifier and support vector machine (SVM) model were used. The SVM model experiments generated an f-score measurement of 0.996 on test data distinguishing teens from adults. We also introduce an alternative method for generating ldquostop wordsrdquo that chooses n-grams based on their relative distribution across the classes.</abstract><pub>IEEE</pub><doi>10.1109/ICSC.2009.37</doi><tpages>7</tpages></addata></record> |
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| ispartof | 2009 IEEE International Conference on Semantic Computing, 2009, p.33-39 |
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| language | eng |
| recordid | cdi_ieee_primary_5298540 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | age classification Bayesian methods Computer science Internet Law enforcement Naïve Bayesian Classifier Niobium compounds online chat Statistical analysis stop words Support Vector Machine Support vector machine classification Support vector machines Testing Text categorization |
| title | Age Detection in Chat |
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