Aliases: findMostFreqTerms findMostFreqTerms.term_frequency findMostFreqTerms.DocumentTermMatrix findMostFreqTerms.TermDocumentMatrix
Keywords:
### ** Examples data("crude") ## Term frequencies: tf <- termFreq(crude[[14L]]) findMostFreqTerms(tf)
oil opec the that was crude 4 4 4 3 3 2
## Document-term matrices: dtm <- DocumentTermMatrix(crude) ## Most frequent terms for each document: findMostFreqTerms(dtm)
$`127` oil the its prices crude cut 5 5 3 3 2 2 $`144` the oil opec that and said 17 11 10 10 9 9 $`191` the canada canadian crude for oil 4 2 2 2 2 2 $`194` the crude bbl. dlrs for price 4 3 2 2 2 2 $`211` the said and discounted estimates for 8 3 2 2 2 2 $`236` the its kuwait and oil was 15 8 8 7 7 7 $`237` the and report economic government growth 30 11 7 6 5 4 $`242` the were and oil said after 6 4 3 3 3 2 $`246` the and billion budget for government 18 9 6 6 6 6 $`248` the oil prices and opec said 27 9 7 6 6 5 $`273` the mln bpd from last saudi 21 9 7 7 7 7 $`349` the oil and arab crude emirates 5 3 2 2 2 2 $`352` the oil prices saudi and accord 7 5 4 4 3 2 $`353` oil opec the that was crude 4 4 4 3 3 2 $`368` the power oil ship after closed 11 4 3 3 2 2 $`489` the and for oil about development 8 5 4 4 2 2 $`502` the and for oil u.s. about 13 6 5 4 3 2 $`543` the 1.50 dlrs for posted company 5 3 3 3 3 2 $`704` the futures exchange nymex and will 21 8 6 6 5 5 $`708` january 1986, 1987 billion cubic fiscales 4 2 2 2 2 2
## Most frequent terms for the first 10 the second 10 documents, ## respectively: findMostFreqTerms(dtm, INDEX = rep(1 : 2, each = 10L))
$`1` the and oil said for its 134 49 46 33 29 28 $`2` the oil and for said prices 95 34 28 21 19 17