php - Using str_word_count for UTF8 texts

Question

I have this text:

$text  = "Ba?ka, küskün otomobil ka?t? buraya küskün otomobil neden ka?t?
          ka?t? buraya, oraya KISMEN @here #there J.J.Johanson hep.
          Danny:Where is mom? I don't know! Café est wei? for 2 €uros.
          My 2nd nickname is mike18.";

Recently I was using this.

$a1= array_count_values(str_word_count($text, 1, '???????I???üü@#é?€1234567890'));
arsort($a1);

You can check with this fiddle:
http://ideone.com/oVUGYa

But this solution doesn't solve all UTF8 problems. I can't write whole UTF8 set into str_word_count as parameter.

So I created this:

$wordsArray = explode(" ",$text);
foreach ($wordsArray as $k => $w) {
    $wordsArray[$k] = str_replace(array(",","."),"",$w);
}
$wordsArray2 = array_count_values($wordsArray);
arsort($wordsArray2);

Output should be like this:

Array (
 [ka?t?] => 3
 [küskün] => 2
 [buraya] => 2
 [@here] => 1
 [#there] => 1
 [Danny] => 1
 [mom] => 1
 [don't] => 1
 [know] => 1
 ...
 ...
)

This works well but it doesn't cover all sentence-word problems. For example I removed comma and dots with str_replace.

For example this solution doesn't cover the words like this: Hello Mike,how are you ? Mike and how won't be treated as different words.

This doesn't covered in str_word_count solution: KISMEN @here #there. At and dash sign and won't be taken into consideration.

This will not be covered J.J.Johanson. Although it is a word, it will be treated as JJJohanson

Question, exclamation signs should be removed from words.

Is there a better way to get str_word_count behaviour with UTF8 support ? The $text which exists in the top of this question is reference for me.

(It would be better if you can provide a fiddle with your answer)

Answer 1

You will never have a prefect solution of word-count, because word-count concept is not exists or too difficult in some languages. UTF8 or not does not matter.

Japanese and Chinese are not space tokenism language. They even don't have a static word list, you have to read the whole sentence before find verb and noun.

If you want to support multiple languages, you will need language specific tokenizer engine. You may research full-text index, tokenizer, CJK-tokenizer, CJK-analyzer for more information.

If you only want to support limited selected languages, just improve your regex patters with more and more cases.