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The QWERTY effect postulates that the keyboard layout influences word meanings by linking positivity to the use of the right hand and negativity to the use of the left hand. For example, previous research has established that words with more right hand letters are rated more positively than words with more left hand letters by human subjects in small scale experiments. In this paper, we perform large scale investigations of the QWERTY effect on the web. Using data from eleven web platforms related to products, movies, books, and videos, we conduct observational tests whether a hand-meaning relationship can be found in decoding text on the web. Furthermore, we investigate whether encoding text on the web exhibits the QWERTY effect as well, by analyzing the relationship between the text of online reviews and their star ratings in four additional datasets. Overall, we find robust evidence for the QWERTY effect both at the point of text interpretation (decoding) and at the point of text creation (encoding). We also find under which conditions the effect might not hold. Our findings have implications for any algorithmic method aiming to evaluate the meaning of words on the web, including for example semantic or sentiment analysis, and show the existence of "dactilar onomatopoeias" that shape the dynamics of word-meaning associations. To the best of our knowledge, this is the first work to reveal the extent to which the QWERTY effect exists in large scale human-computer interaction on the web.

The QWERTY effect postulates that the keyboard layout influences word meanings by linking positivity to the use of the right hand and negativity to the use of the left hand. For example, previous research has established that words with more right hand letters are rated more positively than words with more left hand letters by human subjects in small scale experiments. In this paper, we perform large scale investigations of the QWERTY effect on the web. Using data from eleven web platforms related to products, movies, books, and videos, we conduct observational tests whether a hand-meaning relationship can be found in decoding text on the web. Furthermore, we investigate whether encoding text on the web exhibits the QWERTY effect as well, by analyzing the relationship between the text of online reviews and their star ratings in four additional datasets. Overall, we find robust evidence for the QWERTY effect both at the point of text interpretation (decoding) and at the point of text creation (encoding). We also find under which conditions the effect might not hold. Our findings have implications for any algorithmic method aiming to evaluate the meaning of words on the web, including for example semantic or sentiment analysis, and show the existence of "dactilar onomatopoeias" that shape the dynamics of word-meaning associations. To the best of our knowledge, this is the first work to reveal the extent to which the QWERTY effect exists in large scale human-computer interaction on the web.

Includes bibliographical references and index

Includes bibliographical references and index

Includes bibliographical references and index

174 p. ; 23 cm

Textual data is everywhere, in email and scientific papers, in online newspapers and e-commerce sites. The Web contains more than 200 terabytes of text not even counting the contents of dynamic textual databases. This enormous source of knowledge is seriously underexploited. Textual documents on the Web are very hard to model computationally: they are mostly unstructured, time-dependent, collectively authored, multilingual, and of uneven importance. Traditional grammar-based techniques don't scale up to address such problems. Novel representations and analytical tools are needed. I will discuss several recent contributions related to text mining from a variety of genres. More specifically these include (a) lexical models of the growth of the Web, (b) graph-based entity classification, Evolving news summarization, and (d) mining protein interactions in papers. As it turns out, the right representations, when complemented with traditional NLP techniques, turn all of these into instances of better studied problems in areas such as social networks, statistical mechanics, sequence analysis, and computational phylogenetics. ©2004 Microsoft Corporation. All rights reserved.

Textual data is everywhere, in email and scientific papers, in online newspapers and e-commerce sites. The Web contains more than 200 terabytes of text not even counting the contents of dynamic textual databases. This enormous source of knowledge is seriously underexploited. Textual documents on the Web are very hard to model computationally: they are mostly unstructured, time-dependent, collectively authored, multilingual, and of uneven importance. Traditional grammar-based techniques don't scale up to address such problems. Novel representations and analytical tools are needed. I will discuss several recent contributions related to text mining from a variety of genres. More specifically these include (a) lexical models of the growth of the Web, (b) graph-based entity classification, Evolving news summarization, and (d) mining protein interactions in papers. As it turns out, the right representations, when complemented with traditional NLP techniques, turn all of these into instances of better studied problems in areas such as social networks, statistical mechanics, sequence analysis, and computational phylogenetics. ©2004 Microsoft Corporation. All rights reserved.

Textual data is everywhere, in email and scientific papers, in online newspapers and e-commerce sites. The Web contains more than 200 terabytes of text not even counting the contents of dynamic textual databases. This enormous source of knowledge is seriously underexploited. Textual documents on the Web are very hard to model computationally: they are mostly unstructured, time-dependent, collectively authored, multilingual, and of uneven importance. Traditional grammar-based techniques don't scale up to address such problems. Novel representations and analytical tools are needed. I will discuss several recent contributions related to text mining from a variety of genres. More specifically these include (a) lexical models of the growth of the Web, (b) graph-based entity classification, Evolving news summarization, and (d) mining protein interactions in papers. As it turns out, the right representations, when complemented with traditional NLP techniques, turn all of these into instances of better studied problems in areas such as social networks, statistical mechanics, sequence analysis, and computational phylogenetics. ©2004 Microsoft Corporation. All rights reserved.

An attempt was made to apply current notions in linguistics and philosophy to the problems of technical writing. Major sections are: the frame of reference; words and word-books; words plus words make sentences; sentences plus sentences make prose. Typical headings include: Gulliver the gullible, or linguistics in Lagado; the definer defined; Aristotle was class conscious; what your dictionary won't tell you; must we write pidgin; the mystique of sentencehood; it's art, but is it pretty; a sentence is a discrete fragment of a correlation network of 'thoughts' expressed as a sequential linear array of symbols.

An attempt was made to apply current notions in linguistics and philosophy to the problems of technical writing. Major sections are: the frame of reference; words and word-books; words plus words make sentences; sentences plus sentences make prose. Typical headings include: Gulliver the gullible, or linguistics in Lagado; the definer defined; Aristotle was class conscious; what your dictionary won't tell you; must we write pidgin; the mystique of sentencehood; it's art, but is it pretty; a sentence is a discrete fragment of a correlation network of 'thoughts' expressed as a sequential linear array of symbols.

Few people outside of academic life are fully aware of the yawning gap that has opened between the cultural allegiances of American higher education and the country at large. Particularly arresting is the degree to which traditional conceptions of a unified American nationality, based on shared beliefs in liberty and individualism, have been replaced in universities by a corporatist vision emphasizing a mosaic of distinct ethnic and gender groups, whose disparate values and folkways merit equal recognition and acceptance. The verbal emblem of this outlook is the word "diversity," employed to celebrate "difference" and stigmatize the "privileging" of any particular cultural ideal or lifestyle above any other. This paper discusses the findings of a 2006 study of university websites that reveals, among other things, that references to diversity exceed those to traditional American ideals like freedom, democracy, and liberty. Methodological Appendix is included.

Few people outside of academic life are fully aware of the yawning gap that has opened between the cultural allegiances of American higher education and the country at large. Particularly arresting is the degree to which traditional conceptions of a unified American nationality, based on shared beliefs in liberty and individualism, have been replaced in universities by a corporatist vision emphasizing a mosaic of distinct ethnic and gender groups, whose disparate values and folkways merit equal recognition and acceptance. The verbal emblem of this outlook is the word "diversity," employed to celebrate "difference" and stigmatize the "privileging" of any particular cultural ideal or lifestyle above any other. This paper discusses the findings of a 2006 study of university websites that reveals, among other things, that references to diversity exceed those to traditional American ideals like freedom, democracy, and liberty. Methodological Appendix is included.

Few people outside of academic life are fully aware of the yawning gap that has opened between the cultural allegiances of American higher education and the country at large. Particularly arresting is the degree to which traditional conceptions of a unified American nationality, based on shared beliefs in liberty and individualism, have been replaced in universities by a corporatist vision emphasizing a mosaic of distinct ethnic and gender groups, whose disparate values and folkways merit equal recognition and acceptance. The verbal emblem of this outlook is the word "diversity," employed to celebrate "difference" and stigmatize the "privileging" of any particular cultural ideal or lifestyle above any other. This paper discusses the findings of a 2006 study of university websites that reveals, among other things, that references to diversity exceed those to traditional American ideals like freedom, democracy, and liberty. Methodological Appendix is included.

Textual data is everywhere, in email and scientific papers, in online newspapers and e-commerce sites. The Web contains more than 200 terabytes of text not even counting the contents of dynamic textual databases. This enormous source of knowledge is seriously underexploited. Textual documents on the Web are very hard to model computationally: they are mostly unstructured, time-dependent, collectively authored, multilingual, and of uneven importance. Traditional grammar-based techniques don't scale up to address such problems. Novel representations and analytical tools are needed. I will discuss several recent contributions related to text mining from a variety of genres. More specifically these include (a) lexical models of the growth of the Web, (b) graph-based entity classification, Evolving news summarization, and (d) mining protein interactions in papers. As it turns out, the right representations, when complemented with traditional NLP techniques, turn all of these into instances of better studied problems in areas such as social networks, statistical mechanics, sequence analysis, and computational phylogenetics. ©2004 Microsoft Corporation. All rights reserved.

Textual data is everywhere, in email and scientific papers, in online newspapers and e-commerce sites. The Web contains more than 200 terabytes of text not even counting the contents of dynamic textual databases. This enormous source of knowledge is seriously underexploited. Textual documents on the Web are very hard to model computationally: they are mostly unstructured, time-dependent, collectively authored, multilingual, and of uneven importance. Traditional grammar-based techniques don't scale up to address such problems. Novel representations and analytical tools are needed. I will discuss several recent contributions related to text mining from a variety of genres. More specifically these include (a) lexical models of the growth of the Web, (b) graph-based entity classification, Evolving news summarization, and (d) mining protein interactions in papers. As it turns out, the right representations, when complemented with traditional NLP techniques, turn all of these into instances of better studied problems in areas such as social networks, statistical mechanics, sequence analysis, and computational phylogenetics. ©2004 Microsoft Corporation. All rights reserved.

Textual data is everywhere, in email and scientific papers, in online newspapers and e-commerce sites. The Web contains more than 200 terabytes of text not even counting the contents of dynamic textual databases. This enormous source of knowledge is seriously underexploited. Textual documents on the Web are very hard to model computationally: they are mostly unstructured, time-dependent, collectively authored, multilingual, and of uneven importance. Traditional grammar-based techniques don't scale up to address such problems. Novel representations and analytical tools are needed. I will discuss several recent contributions related to text mining from a variety of genres. More specifically these include (a) lexical models of the growth of the Web, (b) graph-based entity classification, Evolving news summarization, and (d) mining protein interactions in papers. As it turns out, the right representations, when complemented with traditional NLP techniques, turn all of these into instances of better studied problems in areas such as social networks, statistical mechanics, sequence analysis, and computational phylogenetics. ©2004 Microsoft Corporation. All rights reserved.

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