| word rank | frequency | word |
|---|---|---|
| 1 | 28599 | de |
| 2 | 16639 | a |
| 3 | 14245 | o |
| 4 | 13976 | e |
| 5 | 13634 | que |
| 6 | 11364 | do |
| 7 | 9575 | da |
| 8 | 7610 | em |
| 9 | 7100 | para |
| word rank | frequency | word |
|---|---|---|
| 10 | 6086 | com |
| 20 | 3154 | por |
| 30 | 1504 | ser |
| 40 | 987 | ainda |
| 50 | 851 | foram |
| 60 | 712 | vai |
| 70 | 599 | As |
| 80 | 543 | contra |
| 90 | 476 | dias |
| word rank | frequency | word |
|---|---|---|
| 100 | 444 | só |
| 200 | 247 | município |
| 300 | 179 | pública |
| 400 | 142 | problema |
| 500 | 122 | veículos |
| 600 | 107 | desse |
| 700 | 93 | Essa |
| 800 | 83 | Mato Grosso |
| 900 | 75 | respeito |
| word rank | frequency | word |
|---|---|---|
| 1000 | 67 | Que |
| 2000 | 34 | necessária |
| 3000 | 22 | aumenta |
| 4000 | 16 | déficit |
| 5000 | 12 | Lazer |
| 6000 | 10 | detenção |
| 7000 | 8 | Gilmar Mendes |
| 8000 | 7 | coordenar |
| 9000 | 6 | captar |
| word rank | frequency | word |
|---|---|---|
| 10000 | 5 | JORNAL |
| 20000 | 2 | Marcelino |
| 30000 | 1 | Badminton |
| 40000 | 1 | Odilon |
| 50000 | 1 | cumpriam |
| 60000 | 1 | sequencia |
Words from different frequency regions are shown. For simplicity, the words with rank k10n, k=1,2,…,9; n=0,1,…, are chosen. In the case n=0 we see the 10 most frequent words, of course.
The tables provide words with fixed rank which might be useful for several comparisons. The average word should get longer with its rank.
For meaningful words at higher ranks we need at least medium a sized corpus.
For rank 1000, 2000, …, 9000:
set @k=3;
select w_id-100 as rank, freq, word from where w_id-100 in (1*pow(10,@k),2*pow(10,@k), 3*pow(10,@k),4*pow(10,@k),5*pow(10,@k), 6*pow(10,@k),7*pow(10,@k),8*pow(10,@k), 9*pow(10,@k)) order by rank;
3.2.1. The most frequent 50 words