2
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Problem Description
I have a dataset, for which it's quite complex to explain what the data contextually means. However, I hope that I can explain the goal with some fictitious data.

Let's say we have a sentence, and we are always interested on the first 50 characters and assume that instead of the whole alphabet we only have QWERTY . Than we can create a matrix like this:

   sentence                                               text group
1         1 RYQQQRQTWEQQYYWQRQQWEYWREYWYQQTWRWYWTTEQQTWTYEEWQE  TRUE
2         2 RYQRREQWYEQTYQWYTYEEWRYTYWTTRQREQYTEQWTWTRRTQYREQW  TRUE
3         3 RYRRRYQQQRRQWEQYRWQEWWTRWWQYYYYEEYWEYYRWQYWEWRYQWE  TRUE
4         4 YTQTQWWQQWQETRTEQTRRTQQTYEQWTWRWEQEYQRTWWYTYQYYRWY  TRUE
5         5 EQERTREEQEEWTQRTTTTRTTQRYYTWTETTYQRTQYYQYWRYQQTTTR  TRUE
6         6 QEQQYREYWEETTYWQWEYQERERREWERWWYETTWYWTWQRWWTTYETE FALSE
7         7 RRQWEQYTTRYYRRTWQWQRYTYWEEERWRWTRRTWQYQTWEYQETTQET FALSE
8         8 EEYERYWYEWYEWETYQERQYYEQWTYWTWTEYQWQQQETTYTRTWRWWY FALSE
9         9 TTEYEYWTRTREQEWTRTYYEYWETQTRRQEEYYQRTTWWWYQEWQEREQ FALSE
10       10 REYYTETYQEQQRRRRETQWYEQRETQTQWEWTEERYWYQTRTEYRRYYQ FALSE

Now I want to know which letters at which positions can (best) differentiate the two groups (TRUE and FALSE) here. Or which combination of letters + positions.


Tried PCA

We first wanted to perform a PCA analysis by converting this matrix to a binary one like so:

"Q1" "W1" "E1" "R1" "T1" "Y1" "Q2" "W2" "E2" "R2" "T2" "Y2" "Q3" "W3" "E3" "R3" "T3" "Y3" "Q4" "W4" "E4" "R4" "T4" "Y4" "Q5" "W5" "E5" "R5" "T5" "Y5" "Q6" "W6" "E6" "R6" "T6" "Y6" "Q7" "W7" "E7" "R7" "T7" "Y7" "Q8" "W8" "E8" "R8" "T8" "Y8" "Q9" "W9" "E9" "R9" "T9" "Y9" "Q10" "W10" "E10" "R10" "T10" "Y10" "Q11" "W11" "E11" "R11" "T11" "Y11" "Q12" "W12" "E12" "R12" "T12" "Y12" "Q13" "W13" "E13" "R13" "T13" "Y13" "Q14" "W14" "E14" "R14" "T14" "Y14" "Q15" "W15" "E15" "R15" "T15" "Y15" "Q16" "W16" "E16" "R16" "T16" "Y16" "Q17" "W17" "E17" "R17" "T17" "Y17" "Q18" "W18" "E18" "R18" "T18" "Y18" "Q19" "W19" "E19" "R19" "T19" "Y19" "Q20" "W20" "E20" "R20" "T20" "Y20" "Q21" "W21" "E21" "R21" "T21" "Y21" "Q22" "W22" "E22" "R22" "T22" "Y22" "Q23" "W23" "E23" "R23" "T23" "Y23" "Q24" "W24" "E24" "R24" "T24" "Y24" "Q25" "W25" "E25" "R25" "T25" "Y25" "Q26" "W26" "E26" "R26" "T26" "Y26" "Q27" "W27" "E27" "R27" "T27" "Y27" "Q28" "W28" "E28" "R28" "T28" "Y28" "Q29" "W29" "E29" "R29" "T29" "Y29" "Q30" "W30" "E30" "R30" "T30" "Y30" "Q31" "W31" "E31" "R31" "T31" "Y31" "Q32" "W32" "E32" "R32" "T32" "Y32" "Q33" "W33" "E33" "R33" "T33" "Y33" "Q34" "W34" "E34" "R34" "T34" "Y34" "Q35" "W35" "E35" "R35" "T35" "Y35" "Q36" "W36" "E36" "R36" "T36" "Y36" "Q37" "W37" "E37" "R37" "T37" "Y37" "Q38" "W38" "E38" "R38" "T38" "Y38" "Q39" "W39" "E39" "R39" "T39" "Y39" "Q40" "W40" "E40" "R40" "T40" "Y40" "Q41" "W41" "E41" "R41" "T41" "Y41" "Q42" "W42" "E42" "R42" "T42" "Y42" "Q43" "W43" "E43" "R43" "T43" "Y43" "Q44" "W44" "E44" "R44" "T44" "Y44" "Q45" "W45" "E45" "R45" "T45" "Y45" "Q46" "W46" "E46" "R46" "T46" "Y46" "Q47" "W47" "E47" "R47" "T47" "Y47" "Q48" "W48" "E48" "R48" "T48" "Y48" "Q49" "W49" "E49" "R49" "T49" "Y49" "Q50" "W50" "E50" "R50" "T50" "Y50"
0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0
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0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0
1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0
0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0
0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0

However, then we could capture the positions with most variation, however we weren't able to separate these by groups.


Tried Euclidean distance

This will show us the distance between the sentences, but not necessarily (at least not directly) the variables explaining the differences between the groups.

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  • 1
    $\begingroup$ Why don't you try LDA? $\endgroup$ – gunes Sep 14 '18 at 15:41
  • $\begingroup$ @gunes Aah forgot to mention that I have constants in the sentences, then LDA will throw an error (in R: variables ...... appear to be constant within groups ) $\endgroup$ – KingBoomie Sep 14 '18 at 15:49
  • $\begingroup$ I could possibly remove these though, i will take a look at it again @gunes $\endgroup$ – KingBoomie Sep 14 '18 at 16:00
  • $\begingroup$ @RickBeeloo Instead of removing them from the data, use VEM method for your LDA. That should do the job. $\endgroup$ – HoofarLotusX Sep 17 '18 at 13:21

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