519 líneas
17 KiB
PHP
519 líneas
17 KiB
PHP
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<?php
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require_once "sentitext_cat.php";
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//Constants
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// (empirically derived mean sentiment intensity rating increase for booster words)
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define("B_INCR",0.293);
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define("B_DECR",-0.293);
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// (empirically derived mean sentiment intensity rating increase for using
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// ALLCAPs to emphasize a word)
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define("C_INCR", 0.733);
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define("N_SCALAR", -0.74);
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// for removing punctuation
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//REGEX_REMOVE_PUNCTUATION = re.compile('[%s]' % re.escape(string.punctuation))
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const NEGATE = ["no", "ca", "no puc", "no podria", "no vull", "darent", "no fer", "no faces",
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"ain't", "aren't", "can't", "couldn't", "daren't", "didn't", "doesn't",
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"dont", "hadnt", "hasnt", "havent", "isnt", "mightnt", "mustnt", "neither",
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"don't", "hadn't", "hasn't", "haven't", "isn't", "mightn't", "mustn't",
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"neednt", "needn't", "mai", "cap", "nope", "tampoc", "ni", "res de res", "res", "gens", "gens ni mica", "gens ni miqueta", "cap lloc",
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"oughtnt", "shant", "shouldnt", "meck", "wasnt", "werent",
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"oughtn't", "shan't", "shouldn't", "uh-uh", "wasn't", "weren't",
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"sense", "costum", "habit", "wouldnt", "won't", "wouldn't", "estrany", "raro", "seldom", "malgrat"];
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//booster/dampener 'intensifiers' or 'degree adverbs'
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//http://en.wiktionary.org/wiki/Category:English_degree_adverbs
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const BOOSTER_DICT = ["completament"=> B_INCR, "sorprendentment"=> B_INCR, "horrible"=> B_INCR, "barbaritat"=> B_INCR, "considerable"=> B_INCR, "considerablement"=> B_INCR,
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"decidit"=> B_INCR, "profundament"=> B_INCR, "maleit"=> B_INCR, "maleida"=> B_INCR, "enorme"=> B_INCR, "tot"=> B_INCR, "tota"=> B_INCR,
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"completament"=> B_INCR, "especialment"=> B_INCR, "excepcionalment"=> B_INCR, "extremadament"=> B_INCR,
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"fabulos"=> B_INCR, "condemnat"=> B_INCR, "molt"=> B_INCR,
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"fotut"=> B_INCR, "frickin"=> B_INCR, "frigging"=> B_INCR, "friggin"=> B_INCR, "completament"=> B_INCR, "fucking"=> B_INCR,
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"greatly"=> B_INCR, "hella"=> B_INCR, "highly"=> B_INCR, "enormement"=> B_INCR, "increible"=> B_INCR,
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"intensament"=> B_INCR, "majoritariament"=> B_INCR, "mes"=> B_INCR, "major"=> B_INCR, "particular"=> B_INCR,
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"estrictament"=> B_INCR, "simplement"=> B_INCR, "prou"=> B_INCR, "realment"=> B_INCR, "destacable"=> B_INCR,
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"tan"=> B_INCR, "tant"=> B_INCR, "susbtancia"=> B_INCR, "en esència"=> B_INCR,
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"thoroughly"=> B_INCR, "totalment"=> B_INCR, "tremendament"=> B_INCR,
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"uber"=> B_INCR, "increible"=> B_INCR, "inusual"=> B_INCR, "utterly"=> B_INCR,
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"molt"=> B_INCR, "prou"=> B_INCR,
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"la major"=> B_DECR, "rarament"=> B_DECR, "difícil"=> B_DECR, "ja n'hi ha prou"=> B_DECR,
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"tipus de"=> B_DECR, "kinda"=> B_DECR, "prou bò"=> B_DECR, "prou bona"=> B_INCR, "kind-of"=> B_DECR,
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"menys"=> B_DECR, "menut"=> B_DECR, "marginal"=> B_DECR, "en ocasions"=> B_DECR, "en part"=> B_DECR,
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"escasament"=> B_DECR, "lleugerament"=> B_DECR, "somewhat"=> B_DECR,
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"tipus de"=> B_DECR, "sorta"=> B_DECR, "un poc"=> B_DECR, "sort-of"=> B_DECR];
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// check for special case idioms using a sentiment-laden keyword known to SAGE
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const SPECIAL_CASE_IDIOMS = ["quin goig"=> 3, "per menys"=> 3, "si clar"=> 1.5, "burro"=> -2, "burrot" => -2, "ruc" => -2,
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"anda que"=> 2, "poca solta"=> -1.5, "quin poc trellat"=> -1.5, "anda calla"=> -2, "ni un"=> -2,
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"la mare que els ha parit"=> -2, "no tenen vergonya"=> -2, "Enhorabona"=>3];
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##Static methods##
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/*
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Normalize the score to be between -1 and 1 using an alpha that
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approximates the max expected value
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*/
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function normalize($score, $alpha=15){
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$norm_score = $score/sqrt(($score*$score) + $alpha);
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return $norm_score;
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}
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/*
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Give a sentiment intensity score to sentences.
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*/
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class SentimentIntensityAnalyzer{
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private $lexicon_file = "";
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private $lexicon = "";
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private $current_sentitext = null;
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function __construct($lexicon_file="vader_sentiment_lexicon_cat.txt"){
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//Not sure about this as it forces lexicon file to be in the same directory as executing script
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$this->lexicon_file = realpath(dirname(__FILE__)) . "/" . $lexicon_file;
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$this->lexicon = $this->make_lex_dict();
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}
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/*
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Determine if input contains negation words
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*/
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function IsNegated($wordToTest, $include_nt=true){
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if(in_array($wordToTest,NEGATE)){
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return true;
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}
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if ($include_nt) {
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if (strpos($wordToTest,"n't")){
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return true;
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}
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}
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return false;
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}
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/*
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Convert lexicon file to a dictionary
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*/
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function make_lex_dict(){
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$lex_dict = [];
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$fp = fopen($this->lexicon_file,"r");
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if(!$fp){
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die("Cannot load lexicon file");
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}
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while (($line = fgets($fp, 4096)) !== false) {
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list($word, $measure) = explode("\t",trim($line));
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//.strip().split('\t')[0:2]
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$lex_dict[$word] = $measure;
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//lex_dict[word] = float(measure)
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}
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return $lex_dict;
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}
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private function IsKindOf($firstWord,$secondWord){
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return "tipus" === strtolower($firstWord) && "de" === strtolower($secondWord);
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}
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private function IsBoosterWord($word){
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return array_key_exists(strtolower($word),BOOSTER_DICT);
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}
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private function getBoosterScaler($word){
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return BOOSTER_DICT[strtolower($word)];
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}
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private function IsInLexicon($word){
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$lowercase = strtolower($word);
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return array_key_exists($lowercase,$this->lexicon);
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}
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private function IsUpperCaseWord($word){
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return ctype_upper($word);
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}
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private function getValenceFromLexicon($word){
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return $this->lexicon[strtolower($word)];
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}
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private function getTargetWordFromContext($wordInContext){
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return $wordInContext[count($wordInContext)-1];
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}
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/*
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Gets the precedding two words to check for emphasis
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*/
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private function getWordInContext($wordList,$currentWordPosition){
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$precedingWordList =[];
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//push the actual word on to the context list
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array_unshift($precedingWordList,$wordList[$currentWordPosition]);
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//If the word position is greater than 2 then we know we are not going to overflow
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if(($currentWordPosition-1)>=0){
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array_unshift($precedingWordList,$wordList[$currentWordPosition-1]);
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}else{
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array_unshift($precedingWordList,"");
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}
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if(($currentWordPosition-2)>=0){
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array_unshift($precedingWordList,$wordList[$currentWordPosition-2]);
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}else{
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array_unshift($precedingWordList,"");
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}
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if(($currentWordPosition-3)>=0){
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array_unshift($precedingWordList,$wordList[$currentWordPosition-3]);
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}else{
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array_unshift($precedingWordList,"");
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}
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return $precedingWordList;
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}
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/*
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Return a float for sentiment strength based on the input text.
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Positive values are positive valence, negative value are negative
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valence.
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*/
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function getSentiment($text){
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$this->current_sentitext = new SentiText($text);
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$sentiments = [];
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$words_and_emoticons = $this->current_sentitext->words_and_emoticons;
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for($i=0;$i<count($words_and_emoticons)-1;$i++){
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$valence = 0.0;
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$wordBeingTested = $words_and_emoticons[$i];
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//If this is a booster word add a 0 valances then go to next word as it does not express sentiment directly
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/* if ($this->IsBoosterWord($wordBeingTested)){
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echo "\t\tThe word is a booster word: setting sentiment to 0.0\n";
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}*/
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//If the word is not in the Lexicon then it does not express sentiment. So just ignore it.
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if($this->IsInLexicon($wordBeingTested)){
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//Special case because kind is in the lexicon so the modifier kind of needs to be skipped
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if("tipus" !=$words_and_emoticons[$i] && "de" != $words_and_emoticons[$i+1]){
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$valence = $this->getValenceFromLexicon($wordBeingTested);
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$wordInContext = $this->getWordInContext($words_and_emoticons,$i);
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//If we are here then we have a word that enhance booster words
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$valence = $this->adjustBoosterSentiment($wordInContext,$valence);
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}
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}
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array_push($sentiments,$valence);
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}
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//Once we have a sentiment for each word adjust the sentimest if but is present
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$sentiments = $this->_but_check($words_and_emoticons, $sentiments);
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return $this->score_valence($sentiments, $text);
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}
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private function applyValenceCapsBoost($targetWord,$valence){
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if($this->IsUpperCaseWord($targetWord) && $this->current_sentitext->is_cap_diff){
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if($valence > 0){
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$valence += C_INCR;
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}
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else{
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$valence -= C_INCR;
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}
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}
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return $valence;
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}
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/*
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Check if the preceding words increase, decrease, or negate/nullify the
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valence
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*/
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private function boosterScaleAdjustment($word, $valence){
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$scalar = 0.0;
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if(!$this->IsBoosterWord($word)){
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return $scalar;
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}
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$scalar = $this->getBoosterScaler($word);
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if ($valence < 0){
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$scalar *= -1;
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}
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//check if booster/dampener word is in ALLCAPS (while others aren't)
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$scalar = $this->applyValenceCapsBoost($word,$scalar);
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return $scalar;
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}
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// dampen the scalar modifier of preceding words and emoticons
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// (excluding the ones that immediately preceed the item) based
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// on their distance from the current item.
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private function dampendBoosterScalerByPosition($booster,$position){
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if(0===$booster){
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return $booster;
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}
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if(1==$position){
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return $booster*0.95;
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}
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if(2==$position){
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return $booster*0.9;
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}
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return $booster;
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}
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private function adjustBoosterSentiment($wordInContext,$valence){
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//The target word is always the last word
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$targetWord = $this->getTargetWordFromContext($wordInContext);
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//check if sentiment laden word is in ALL CAPS (while others aren't) and apply booster
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$valence = $this->applyValenceCapsBoost($targetWord,$valence);
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$valence = $this->modifyValenceBasedOnContext($wordInContext,$valence);
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return $valence;
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}
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private function modifyValenceBasedOnContext($wordInContext,$valence){
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$wordToTest = $this->getTargetWordFromContext($wordInContext);
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//if($this->IsInLexicon($wordToTest)){
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// continue;
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//}
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for($i=0;$i<count($wordInContext)-1;$i++){
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$scalarValue = $this->boosterScaleAdjustment($wordInContext[$i], $valence);
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$scalarValue = $this->dampendBoosterScalerByPosition($scalarValue,$i);
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$valence = $valence+$scalarValue;
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}
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$valence = $this->_never_check($wordInContext, $valence);
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$valence = $this->_idioms_check($wordInContext, $valence);
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# future work: consider other sentiment-laden idioms
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# other_idioms =
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# {"back handed": -2, "blow smoke": -2, "blowing smoke": -2,
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# "upper hand": 1, "break a leg": 2,
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# "cooking with gas": 2, "in the black": 2, "in the red": -2,
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# "on the ball": 2,"under the weather": -2}
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$valence = $this->_least_check($wordInContext, $valence);
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return $valence;
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}
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function _least_check($wordInContext, $valence){
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# check for negation case using "least"
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//if the previous word is least"
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if(strtolower($wordInContext[2]) == "sense importància"){
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//but not "at least {word}" "very least {word}"
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if (strtolower($wordInContext[1]) != "a" && strtolower($wordInContext[1]) != "molt"){
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$valence = $valence*N_SCALAR;
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}
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}
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return $valence;
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}
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function _but_check($words_and_emoticons, $sentiments){
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# check for modification in sentiment due to contrastive conjunction 'però'
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$bi = array_search("pero",$words_and_emoticons);
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if(!$bi){
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$bi = array_search("PERO",$words_and_emoticons);
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}
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if($bi){
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for($si=0;$si<count($sentiments);$si++){
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if($si<$bi){
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$sentiments[$si] = $sentiments[$si]*0.5;
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}elseif($si> $bi){
|
||
|
|
$sentiments[$si] = $sentiments[$si]*1.5;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return $sentiments;
|
||
|
|
}
|
||
|
|
|
||
|
|
function _idioms_check($wordInContext, $valence){
|
||
|
|
$onezero = sprintf("%s %s",$wordInContext[2], $wordInContext[3]);
|
||
|
|
|
||
|
|
$twoonezero = sprintf("%s %s %s",$wordInContext[1],
|
||
|
|
$wordInContext[2], $wordInContext[3]);
|
||
|
|
|
||
|
|
$twoone = sprintf("%s %s",$wordInContext[1], $wordInContext[2]);
|
||
|
|
|
||
|
|
$threetwoone = sprintf("%s %s %s",$wordInContext[0],
|
||
|
|
$wordInContext[1], $wordInContext[2]);
|
||
|
|
|
||
|
|
$threetwo = sprintf("%s %s",$wordInContext[0], $wordInContext[1]);
|
||
|
|
|
||
|
|
$zeroone = sprintf("%s %s",$wordInContext[3], $wordInContext[2]);
|
||
|
|
|
||
|
|
$zeroonetwo = sprintf("%s %s %s",$wordInContext[3], $wordInContext[2], $wordInContext[1]);
|
||
|
|
|
||
|
|
$sequences = [$onezero, $twoonezero, $twoone, $threetwoone, $threetwo];
|
||
|
|
|
||
|
|
foreach($sequences as $seq){
|
||
|
|
if (array_key_exists(strtolower($seq), SPECIAL_CASE_IDIOMS)){
|
||
|
|
$valence = SPECIAL_CASE_IDIOMS[$seq];
|
||
|
|
break;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
/*
|
||
|
|
Positive idioms check. Not implementing it yet
|
||
|
|
if(count($words_and_emoticons)-1 > $i){
|
||
|
|
$zeroone = sprintf("%s %s",$words_and_emoticons[$i], $words_and_emoticons[$i+1]);
|
||
|
|
if (in_array($zeroone, SPECIAL_CASE_IDIOMS)){
|
||
|
|
$valence = SPECIAL_CASE_IDIOMS[$zeroone];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if(count($words_and_emoticons)-1 > $i+1){
|
||
|
|
$zeroonetwo = sprintf("%s %s %s",$words_and_emoticons[$i], $words_and_emoticons[$i+1], $words_and_emoticons[$i+2]);
|
||
|
|
if (in_array($zeroonetwo, SPECIAL_CASE_IDIOMS)){
|
||
|
|
$valence = SPECIAL_CASE_IDIOMS[$zeroonetwo];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
*/
|
||
|
|
|
||
|
|
// check for booster/dampener bi-grams such as 'sort of' or 'kind of'
|
||
|
|
if($this->IsBoosterWord($threetwo) || $this->IsBoosterWord($twoone)){
|
||
|
|
$valence = $valence+B_DECR;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return $valence;
|
||
|
|
}
|
||
|
|
|
||
|
|
function _never_check($wordInContext,$valance){
|
||
|
|
//If the sentiment word is preceded by never so/this we apply a modifier
|
||
|
|
$neverModifier = 0;
|
||
|
|
if("mai" == $wordInContext[0]){
|
||
|
|
$neverModifier = 1.25;
|
||
|
|
}else if("mai" == $wordInContext[1]){
|
||
|
|
$neverModifier = 1.5;
|
||
|
|
}
|
||
|
|
if("aixi" == $wordInContext[1] || "aixi"== $wordInContext[2] || "este" == $wordInContext[1] || "este" == $wordInContext[2]){
|
||
|
|
$valance *= $neverModifier;
|
||
|
|
}
|
||
|
|
|
||
|
|
//if any of the words in context are negated words apply negative scaler
|
||
|
|
foreach($wordInContext as $wordToCheck){
|
||
|
|
if($this->IsNegated($wordToCheck)){
|
||
|
|
$valance *= B_DECR;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
return $valance;
|
||
|
|
}
|
||
|
|
|
||
|
|
function _punctuation_emphasis($sum_s, $text){
|
||
|
|
# add emphasis from exclamation points and question marks
|
||
|
|
$ep_amplifier = $this->_amplify_ep($text);
|
||
|
|
$qm_amplifier = $this->_amplify_qm($text);
|
||
|
|
$punct_emph_amplifier = $ep_amplifier+$qm_amplifier;
|
||
|
|
return $punct_emph_amplifier;
|
||
|
|
}
|
||
|
|
|
||
|
|
function _amplify_ep($text){
|
||
|
|
# check for added emphasis resulting from exclamation points (up to 4 of them)
|
||
|
|
$ep_count = substr_count($text,"!");
|
||
|
|
if ($ep_count > 4){
|
||
|
|
$ep_count = 4;
|
||
|
|
}
|
||
|
|
# (empirically derived mean sentiment intensity rating increase for
|
||
|
|
# exclamation points)
|
||
|
|
$ep_amplifier = $ep_count*0.292;
|
||
|
|
return $ep_amplifier;
|
||
|
|
}
|
||
|
|
|
||
|
|
function _amplify_qm($text){
|
||
|
|
# check for added emphasis resulting from question marks (2 or 3+)
|
||
|
|
$qm_count = substr_count ($text,"?");
|
||
|
|
$qm_amplifier = 0;
|
||
|
|
if ($qm_count > 1){
|
||
|
|
if ($qm_count <= 3){
|
||
|
|
# (empirically derived mean sentiment intensity rating increase for
|
||
|
|
# question marks)
|
||
|
|
$qm_amplifier = $qm_count*0.18;
|
||
|
|
}else{
|
||
|
|
$qm_amplifier = 0.96;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return $qm_amplifier;
|
||
|
|
}
|
||
|
|
|
||
|
|
function _sift_sentiment_scores($sentiments){
|
||
|
|
# want separate positive versus negative sentiment scores
|
||
|
|
$pos_sum = 0.0;
|
||
|
|
$neg_sum = 0.0;
|
||
|
|
$neu_count = 0;
|
||
|
|
foreach($sentiments as $sentiment_score){
|
||
|
|
if($sentiment_score > 0){
|
||
|
|
$pos_sum += $sentiment_score +1; # compensates for neutral words that are counted as 1
|
||
|
|
}
|
||
|
|
if ($sentiment_score < 0){
|
||
|
|
$neg_sum += $sentiment_score -1; # when used with math.fabs(), compensates for neutrals
|
||
|
|
}
|
||
|
|
if ($sentiment_score == 0){
|
||
|
|
$neu_count += 1;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return [$pos_sum, $neg_sum, $neu_count];
|
||
|
|
}
|
||
|
|
|
||
|
|
function score_valence($sentiments, $text){
|
||
|
|
if ($sentiments){
|
||
|
|
$sum_s = array_sum($sentiments);
|
||
|
|
# compute and add emphasis from punctuation in text
|
||
|
|
$punct_emph_amplifier = $this->_punctuation_emphasis($sum_s, $text);
|
||
|
|
if ($sum_s > 0){
|
||
|
|
$sum_s += $punct_emph_amplifier;
|
||
|
|
}
|
||
|
|
elseif ($sum_s < 0){
|
||
|
|
$sum_s -= $punct_emph_amplifier;
|
||
|
|
}
|
||
|
|
|
||
|
|
$compound = normalize($sum_s);
|
||
|
|
# discriminate between positive, negative and neutral sentiment scores
|
||
|
|
list($pos_sum, $neg_sum, $neu_count) = $this->_sift_sentiment_scores($sentiments);
|
||
|
|
|
||
|
|
if ($pos_sum > abs($neg_sum)){
|
||
|
|
$pos_sum += $punct_emph_amplifier;
|
||
|
|
}
|
||
|
|
elseif ($pos_sum < abs($neg_sum)){
|
||
|
|
$neg_sum -= $punct_emph_amplifier;
|
||
|
|
}
|
||
|
|
|
||
|
|
$total = $pos_sum + abs($neg_sum) + $neu_count;
|
||
|
|
$pos =abs($pos_sum / $total);
|
||
|
|
$neg = abs($neg_sum / $total);
|
||
|
|
$neu = abs($neu_count / $total);
|
||
|
|
|
||
|
|
}else{
|
||
|
|
$compound = 0.0;
|
||
|
|
$pos = 0.0;
|
||
|
|
$neg = 0.0;
|
||
|
|
$neu = 0.0;
|
||
|
|
}
|
||
|
|
|
||
|
|
$sentiment_dict =
|
||
|
|
["neg" => round($neg, 3),
|
||
|
|
"neu" => round($neu, 3),
|
||
|
|
"pos" => round($pos, 3),
|
||
|
|
"compound" => round($compound, 4)];
|
||
|
|
|
||
|
|
return $sentiment_dict;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
?>
|