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where S is the frequency of social words (such as friend, buddy, coworker) returned from 2.4 Candidate ranking

Figure 3 where S is the frequency of social words (such as friend, buddy, coworker) returned from 2.4 Candidate ranking

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List with ranked candidates
List with ranked candidates
Fig. 2. Part of the implemented IT skills taxonomy
Fig. 2. Part of the implemented IT skills taxonomy
dvantage of data mining and text analysis techniques would be far more effective. Nowadays, under the auspices of Web 2.0, large amounts of textual data exist for a larg yortion of the web users that have been indicated as reliable predictors of a user’s personality \s mentioned before, the proposed system requires a link to each candidate’s blog, as blog lave been shown to reflect important aspects of the personality of a blogger (Oberlande nd Nowson 2006). Previous works have shown that by applying linguistic analysis to blo yosts, the author’s personality traits can be derived (Gill et al. 2009) as well as his moo nd emotions (Mishne 2005). The text analysis in these works is performed with the LIW‘ Linguistic Inquiry and Word Count) system, which analyzes written text samples and extract inguistic features that act as markers of the author’s personality. TTR can (Dee ile wesd DA ONOWD ores Aas anand ke wecloplaw elie en. os mswlaa.c
dvantage of data mining and text analysis techniques would be far more effective. Nowadays, under the auspices of Web 2.0, large amounts of textual data exist for a larg yortion of the web users that have been indicated as reliable predictors of a user’s personality \s mentioned before, the proposed system requires a link to each candidate’s blog, as blog lave been shown to reflect important aspects of the personality of a blogger (Oberlande nd Nowson 2006). Previous works have shown that by applying linguistic analysis to blo yosts, the author’s personality traits can be derived (Gill et al. 2009) as well as his moo nd emotions (Mishne 2005). The text analysis in these works is performed with the LIW‘ Linguistic Inquiry and Word Count) system, which analyzes written text samples and extract inguistic features that act as markers of the author’s personality. TTR can (Dee ile wesd DA ONOWD ores Aas anand ke wecloplaw elie en. os mswlaa.c
dimensions we focus on extraversion due to its importance in candidate selection. Linguis- tic markers for extraversion are the use of many positive emotion words and social process words, but fewer negative emotion words (Pennebaker and Booth 2001). In this work, the extraversion score is estimated directly from LIWC scores (or frequencies), by summing the emotional positivity score and the social orientation score, also obtained from LIWC frequencies:
dimensions we focus on extraversion due to its importance in candidate selection. Linguis- tic markers for extraversion are the use of many positive emotion words and social process words, but fewer negative emotion words (Pennebaker and Booth 2001). In this work, the extraversion score is estimated directly from LIWC scores (or frequencies), by summing the emotional positivity score and the social orientation score, also obtained from LIWC frequencies:
Fig. 5 Job application process 3 Prototype implementation
Fig. 5 Job application process 3 Prototype implementation
Fig. 6 Candidate ranking results
Fig. 6 Candidate ranking results
Table 2 Correlation coefficients for applicants’ relevance scores versus different machine learning models industry. The search results were manually inspected and only bloggers with a LinkedIn profile associated with their blogs were taken into account. What’s more, blogs with no autobiographical content (e.g. technical blogs) were excluded from our study, as they carried no information regarding the author’s personality. We also collected three representative technical positions announced by an unnamed IT company with different requirements. The use of different requirements per position is expected to test the ability of our system to match candidates’ profiles with the appropriate job position. — +. . 4as4 a - . sod . . oof
Table 2 Correlation coefficients for applicants’ relevance scores versus different machine learning models industry. The search results were manually inspected and only bloggers with a LinkedIn profile associated with their blogs were taken into account. What’s more, blogs with no autobiographical content (e.g. technical blogs) were excluded from our study, as they carried no information regarding the author’s personality. We also collected three representative technical positions announced by an unnamed IT company with different requirements. The use of different requirements per position is expected to test the ability of our system to match candidates’ profiles with the appropriate job position. — +. . 4as4 a - . sod . . oof
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