commit 4a0b6c1bf4ec7213ad2b5538eb737a27dc28faa8 Author: J. Fernando Sánchez Date: Tue Jun 12 10:01:44 2018 +0200 Squashed 'sentiment-vader/' content from commit ddb7432 git-subtree-dir: sentiment-vader git-subtree-split: ddb7432d260fd2d8fca719f1b3ee46117019f475 diff --git a/README.md b/README.md new file mode 100644 index 0000000..c97f3b7 --- /dev/null +++ b/README.md @@ -0,0 +1,40 @@ +# Sentimet-vader plugin + +========= + +Vader is a plugin developed at GSI UPM for sentiment analysis. + +For developing this plugin, it has been used the module vaderSentiment, which is described in the paper: + VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text + C.J. Hutto and Eric Gilbert + Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014. + +If you use this plugin in your research, please cite the above paper. + +For more information about the functionality, check the official repository + +https://github.com/cjhutto/vaderSentiment + +The response of this plugin uses [Marl ontology](https://www.gsi.dit.upm.es/ontologies/marl/) developed at GSI UPM for semantic web. + +## Usage + +Params accepted: +- Language: es (Spanish), en(English). +- Input: Text to analyse. + + +Example request: +``` +http://senpy.cluster.gsi.dit.upm.es/api/?algo=sentiment-vader&language=en&input=I%20love%20Madrid +``` + +Example respond: This plugin follows the standard for the senpy plugin response. For more information, please visit [senpy documentation](http://senpy.readthedocs.io). Specifically, NIF API section. + +This plugin supports **python3** + +![alt GSI Logo][logoGSI] + +[logoGSI]: http://www.gsi.dit.upm.es/images/stories/logos/gsi.png "GSI Logo" + +======== diff --git a/README.txt b/README.txt new file mode 100644 index 0000000..bf89160 --- /dev/null +++ b/README.txt @@ -0,0 +1,16 @@ +========== + +This README file describes the plugin vaderSentiment. + +For developing this plugin, it has been used the module vaderSentiment, which is described in the paper: + VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text + C.J. Hutto and Eric Gilbert + Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014. + +If you use this plugin in your research, please cite the above paper + +For more information about the functionality, check the official repository + +https://github.com/cjhutto/vaderSentiment + +======== \ No newline at end of file diff --git a/sentiment-vader.py b/sentiment-vader.py new file mode 100644 index 0000000..5bec3e8 --- /dev/null +++ b/sentiment-vader.py @@ -0,0 +1,49 @@ +# -*- coding: utf-8 -*- + +from vaderSentiment import sentiment +from senpy.plugins import SentimentPlugin, SenpyPlugin +from senpy.models import Results, Sentiment, Entry +import logging + +logger = logging.getLogger(__name__) + +class vaderSentimentPlugin(SentimentPlugin): + + def analyse_entry(self,entry,params): + + logger.debug("Analysing with params {}".format(params)) + + text_input = entry.get("text", None) + aggregate = params['aggregate'] + + score = sentiment(text_input) + + opinion0 = Sentiment(id= "Opinion_positive", + marl__hasPolarity= "marl:Positive", + marl__algorithmConfidence= score['pos'] + ) + opinion1 = Sentiment(id= "Opinion_negative", + marl__hasPolarity= "marl:Negative", + marl__algorithmConfidence= score['neg'] + ) + opinion2 = Sentiment(id= "Opinion_neutral", + marl__hasPolarity = "marl:Neutral", + marl__algorithmConfidence = score['neu'] + ) + + if aggregate == 'true': + res = None + confident = max(score['neg'],score['neu'],score['pos']) + if opinion0.marl__algorithmConfidence == confident: + res = opinion0 + elif opinion1.marl__algorithmConfidence == confident: + res = opinion1 + elif opinion2.marl__algorithmConfidence == confident: + res = opinion2 + entry.sentiments.append(res) + else: + entry.sentiments.append(opinion0) + entry.sentiments.append(opinion1) + entry.sentiments.append(opinion2) + + yield entry diff --git a/sentiment-vader.senpy b/sentiment-vader.senpy new file mode 100644 index 0000000..be3ba15 --- /dev/null +++ b/sentiment-vader.senpy @@ -0,0 +1,25 @@ +{ + "name": "sentiment-vader", + "module": "sentiment-vader", + "description": "Sentiment classifier using vaderSentiment module. Params accepted: Language: {en, es}. The output uses Marl ontology developed at GSI UPM for semantic web.", + "author": "@icorcuera", + "version": "0.1", + "extra_params": { + "language": { + "@id": "lang_rand", + "aliases": ["language", "l"], + "required": false, + "options": ["es", "en", "auto"] + }, + + "aggregate": { + "aliases": ["aggregate","agg"], + "options": ["true", "false"], + "required": false, + "default": false + + } + + }, + "requirements": {} +} diff --git a/test.py b/test.py new file mode 100644 index 0000000..b85c976 --- /dev/null +++ b/test.py @@ -0,0 +1,44 @@ +import os +import logging +logging.basicConfig() +try: + import unittest.mock as mock +except ImportError: + import mock +from senpy.extensions import Senpy +from flask import Flask +from flask.ext.testing import TestCase +import unittest + +class vaderTest(unittest.TestCase): + + def setUp(self): + self.app = Flask("test_plugin") + self.dir = os.path.join(os.path.dirname(__file__)) + self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False) + self.senpy.init_app(self.app) + + def tearDown(self): + self.senpy.deactivate_plugin("vaderSentiment", sync=True) + + def test_analyse(self): + plugin = self.senpy.plugins["vaderSentiment"] + plugin.activate() + + texts = {'I am tired :(' : 'marl:Negative', + 'I love pizza' : 'marl:Positive', + 'I like going to the cinema :)' : 'marl:Positive', + 'This cake is disgusting' : 'marl:Negative'} + + for text in texts: + response = plugin.analyse(input=text) + expected = texts[text] + sentimentSet = response.entries[0].sentiments + + max_sentiment = max(sentimentSet, key=lambda x: x['marl:polarityValue']) + assert max_sentiment['marl:hasPolarity'] == expected + + plugin.deactivate() + +if __name__ == '__main__': + unittest.main() diff --git a/vaderSentiment.py b/vaderSentiment.py new file mode 100644 index 0000000..02658d2 --- /dev/null +++ b/vaderSentiment.py @@ -0,0 +1,363 @@ +#!/usr/bin/python +# coding: utf-8 +''' +Created on July 04, 2013 +@author: C.J. Hutto + +Citation Information + +If you use any of the VADER sentiment analysis tools +(VADER sentiment lexicon or Python code for rule-based sentiment +analysis engine) in your work or research, please cite the paper. +For example: + + Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for + Sentiment Analysis of Social Media Text. Eighth International Conference on + Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014. +''' + +import os, math, re, sys, fnmatch, string +reload(sys) + +def make_lex_dict(f): + return dict(map(lambda (w, m): (w, float(m)), [wmsr.strip().split('\t')[0:2] for wmsr in open(f) ])) + +f = 'vader_sentiment_lexicon.txt' # empirically derived valence ratings for words, emoticons, slang, swear words, acronyms/initialisms +try: + word_valence_dict = make_lex_dict(f) +except: + f = os.path.join(os.path.dirname(__file__),'vader_sentiment_lexicon.txt') + word_valence_dict = make_lex_dict(f) + +# for removing punctuation +regex_remove_punctuation = re.compile('[%s]' % re.escape(string.punctuation)) + +def sentiment(text): + """ + Returns a float for sentiment strength based on the input text. + Positive values are positive valence, negative value are negative valence. + """ + wordsAndEmoticons = str(text).split() #doesn't separate words from adjacent punctuation (keeps emoticons & contractions) + text_mod = regex_remove_punctuation.sub('', text) # removes punctuation (but loses emoticons & contractions) + wordsOnly = str(text_mod).split() + # get rid of empty items or single letter "words" like 'a' and 'I' from wordsOnly + for word in wordsOnly: + if len(word) <= 1: + wordsOnly.remove(word) + # now remove adjacent & redundant punctuation from [wordsAndEmoticons] while keeping emoticons and contractions + puncList = [".", "!", "?", ",", ";", ":", "-", "'", "\"", + "!!", "!!!", "??", "???", "?!?", "!?!", "?!?!", "!?!?"] + for word in wordsOnly: + for p in puncList: + pword = p + word + x1 = wordsAndEmoticons.count(pword) + while x1 > 0: + i = wordsAndEmoticons.index(pword) + wordsAndEmoticons.remove(pword) + wordsAndEmoticons.insert(i, word) + x1 = wordsAndEmoticons.count(pword) + + wordp = word + p + x2 = wordsAndEmoticons.count(wordp) + while x2 > 0: + i = wordsAndEmoticons.index(wordp) + wordsAndEmoticons.remove(wordp) + wordsAndEmoticons.insert(i, word) + x2 = wordsAndEmoticons.count(wordp) + # get rid of residual empty items or single letter "words" like 'a' and 'I' from wordsAndEmoticons + for word in wordsAndEmoticons: + if len(word) <= 1: + wordsAndEmoticons.remove(word) + + # remove stopwords from [wordsAndEmoticons] + #stopwords = [str(word).strip() for word in open('stopwords.txt')] + #for word in wordsAndEmoticons: + # if word in stopwords: + # wordsAndEmoticons.remove(word) + + # check for negation + negate = ["aint", "arent", "cannot", "cant", "couldnt", "darent", "didnt", "doesnt", + "ain't", "aren't", "can't", "couldn't", "daren't", "didn't", "doesn't", + "dont", "hadnt", "hasnt", "havent", "isnt", "mightnt", "mustnt", "neither", + "don't", "hadn't", "hasn't", "haven't", "isn't", "mightn't", "mustn't", + "neednt", "needn't", "never", "none", "nope", "nor", "not", "nothing", "nowhere", + "oughtnt", "shant", "shouldnt", "uhuh", "wasnt", "werent", + "oughtn't", "shan't", "shouldn't", "uh-uh", "wasn't", "weren't", + "without", "wont", "wouldnt", "won't", "wouldn't", "rarely", "seldom", "despite"] + def negated(list, nWords=[], includeNT=True): + nWords.extend(negate) + for word in nWords: + if word in list: + return True + if includeNT: + for word in list: + if "n't" in word: + return True + if "least" in list: + i = list.index("least") + if i > 0 and list[i-1] != "at": + return True + return False + + def normalize(score, alpha=15): + # normalize the score to be between -1 and 1 using an alpha that approximates the max expected value + normScore = score/math.sqrt( ((score*score) + alpha) ) + return normScore + + def wildCardMatch(patternWithWildcard, listOfStringsToMatchAgainst): + listOfMatches = fnmatch.filter(listOfStringsToMatchAgainst, patternWithWildcard) + return listOfMatches + + + def isALLCAP_differential(wordList): + countALLCAPS= 0 + for w in wordList: + if str(w).isupper(): + countALLCAPS += 1 + cap_differential = len(wordList) - countALLCAPS + if cap_differential > 0 and cap_differential < len(wordList): + isDiff = True + else: isDiff = False + return isDiff + isCap_diff = isALLCAP_differential(wordsAndEmoticons) + + b_incr = 0.293 #(empirically derived mean sentiment intensity rating increase for booster words) + b_decr = -0.293 + # booster/dampener 'intensifiers' or 'degree adverbs' http://en.wiktionary.org/wiki/Category:English_degree_adverbs + booster_dict = {"absolutely": b_incr, "amazingly": b_incr, "awfully": b_incr, "completely": b_incr, "considerably": b_incr, + "decidedly": b_incr, "deeply": b_incr, "effing": b_incr, "enormously": b_incr, + "entirely": b_incr, "especially": b_incr, "exceptionally": b_incr, "extremely": b_incr, + "fabulously": b_incr, "flipping": b_incr, "flippin": b_incr, + "fricking": b_incr, "frickin": b_incr, "frigging": b_incr, "friggin": b_incr, "fully": b_incr, "fucking": b_incr, + "greatly": b_incr, "hella": b_incr, "highly": b_incr, "hugely": b_incr, "incredibly": b_incr, + "intensely": b_incr, "majorly": b_incr, "more": b_incr, "most": b_incr, "particularly": b_incr, + "purely": b_incr, "quite": b_incr, "really": b_incr, "remarkably": b_incr, + "so": b_incr, "substantially": b_incr, + "thoroughly": b_incr, "totally": b_incr, "tremendously": b_incr, + "uber": b_incr, "unbelievably": b_incr, "unusually": b_incr, "utterly": b_incr, + "very": b_incr, + + "almost": b_decr, "barely": b_decr, "hardly": b_decr, "just enough": b_decr, + "kind of": b_decr, "kinda": b_decr, "kindof": b_decr, "kind-of": b_decr, + "less": b_decr, "little": b_decr, "marginally": b_decr, "occasionally": b_decr, "partly": b_decr, + "scarcely": b_decr, "slightly": b_decr, "somewhat": b_decr, + "sort of": b_decr, "sorta": b_decr, "sortof": b_decr, "sort-of": b_decr} + sentiments = [] + for item in wordsAndEmoticons: + v = 0 + i = wordsAndEmoticons.index(item) + if (i < len(wordsAndEmoticons)-1 and str(item).lower() == "kind" and \ + str(wordsAndEmoticons[i+1]).lower() == "of") or str(item).lower() in booster_dict: + sentiments.append(v) + continue + item_lowercase = str(item).lower() + if item_lowercase in word_valence_dict: + #get the sentiment valence + v = float(word_valence_dict[item_lowercase]) + + #check if sentiment laden word is in ALLCAPS (while others aren't) + c_incr = 0.733 #(empirically derived mean sentiment intensity rating increase for using ALLCAPs to emphasize a word) + if str(item).isupper() and isCap_diff: + if v > 0: v += c_incr + else: v -= c_incr + + #check if the preceding words increase, decrease, or negate/nullify the valence + def scalar_inc_dec(word, valence): + scalar = 0.0 + word_lower = str(word).lower() + if word_lower in booster_dict: + scalar = booster_dict[word_lower] + if valence < 0: scalar *= -1 + #check if booster/dampener word is in ALLCAPS (while others aren't) + if str(word).isupper() and isCap_diff: + if valence > 0: scalar += c_incr + else: scalar -= c_incr + return scalar + n_scalar = -0.74 + if i > 0 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict: + s1 = scalar_inc_dec(wordsAndEmoticons[i-1], v) + v = v+s1 + if negated([wordsAndEmoticons[i-1]]): v = v*n_scalar + if i > 1 and str(wordsAndEmoticons[i-2]).lower() not in word_valence_dict: + s2 = scalar_inc_dec(wordsAndEmoticons[i-2], v) + if s2 != 0: s2 = s2*0.95 + v = v+s2 + # check for special use of 'never' as valence modifier instead of negation + if wordsAndEmoticons[i-2] == "never" and (wordsAndEmoticons[i-1] == "so" or wordsAndEmoticons[i-1] == "this"): + v = v*1.5 + # otherwise, check for negation/nullification + elif negated([wordsAndEmoticons[i-2]]): v = v*n_scalar + if i > 2 and str(wordsAndEmoticons[i-3]).lower() not in word_valence_dict: + s3 = scalar_inc_dec(wordsAndEmoticons[i-3], v) + if s3 != 0: s3 = s3*0.9 + v = v+s3 + # check for special use of 'never' as valence modifier instead of negation + if wordsAndEmoticons[i-3] == "never" and \ + (wordsAndEmoticons[i-2] == "so" or wordsAndEmoticons[i-2] == "this") or \ + (wordsAndEmoticons[i-1] == "so" or wordsAndEmoticons[i-1] == "this"): + v = v*1.25 + # otherwise, check for negation/nullification + elif negated([wordsAndEmoticons[i-3]]): v = v*n_scalar + + # check for special case idioms using a sentiment-laden keyword known to SAGE + special_case_idioms = {"the shit": 3, "the bomb": 3, "bad ass": 1.5, "yeah right": -2, + "cut the mustard": 2, "kiss of death": -1.5, "hand to mouth": -2} + # future work: consider other sentiment-laden idioms + #other_idioms = {"back handed": -2, "blow smoke": -2, "blowing smoke": -2, "upper hand": 1, "break a leg": 2, + # "cooking with gas": 2, "in the black": 2, "in the red": -2, "on the ball": 2,"under the weather": -2} + onezero = "{} {}".format(str(wordsAndEmoticons[i-1]), str(wordsAndEmoticons[i])) + twoonezero = "{} {} {}".format(str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1]), str(wordsAndEmoticons[i])) + twoone = "{} {}".format(str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1])) + threetwoone = "{} {} {}".format(str(wordsAndEmoticons[i-3]), str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1])) + threetwo = "{} {}".format(str(wordsAndEmoticons[i-3]), str(wordsAndEmoticons[i-2])) + if onezero in special_case_idioms: v = special_case_idioms[onezero] + elif twoonezero in special_case_idioms: v = special_case_idioms[twoonezero] + elif twoone in special_case_idioms: v = special_case_idioms[twoone] + elif threetwoone in special_case_idioms: v = special_case_idioms[threetwoone] + elif threetwo in special_case_idioms: v = special_case_idioms[threetwo] + if len(wordsAndEmoticons)-1 > i: + zeroone = "{} {}".format(str(wordsAndEmoticons[i]), str(wordsAndEmoticons[i+1])) + if zeroone in special_case_idioms: v = special_case_idioms[zeroone] + if len(wordsAndEmoticons)-1 > i+1: + zeroonetwo = "{} {}".format(str(wordsAndEmoticons[i]), str(wordsAndEmoticons[i+1]), str(wordsAndEmoticons[i+2])) + if zeroonetwo in special_case_idioms: v = special_case_idioms[zeroonetwo] + + # check for booster/dampener bi-grams such as 'sort of' or 'kind of' + if threetwo in booster_dict or twoone in booster_dict: + v = v+b_decr + + # check for negation case using "least" + if i > 1 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict \ + and str(wordsAndEmoticons[i-1]).lower() == "least": + if (str(wordsAndEmoticons[i-2]).lower() != "at" and str(wordsAndEmoticons[i-2]).lower() != "very"): + v = v*n_scalar + elif i > 0 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict \ + and str(wordsAndEmoticons[i-1]).lower() == "least": + v = v*n_scalar + sentiments.append(v) + + # check for modification in sentiment due to contrastive conjunction 'but' + if 'but' in wordsAndEmoticons or 'BUT' in wordsAndEmoticons: + try: bi = wordsAndEmoticons.index('but') + except: bi = wordsAndEmoticons.index('BUT') + for s in sentiments: + si = sentiments.index(s) + if si < bi: + sentiments.pop(si) + sentiments.insert(si, s*0.5) + elif si > bi: + sentiments.pop(si) + sentiments.insert(si, s*1.5) + + if sentiments: + sum_s = float(sum(sentiments)) + #print sentiments, sum_s + + # check for added emphasis resulting from exclamation points (up to 4 of them) + ep_count = str(text).count("!") + if ep_count > 4: ep_count = 4 + ep_amplifier = ep_count*0.292 #(empirically derived mean sentiment intensity rating increase for exclamation points) + if sum_s > 0: sum_s += ep_amplifier + elif sum_s < 0: sum_s -= ep_amplifier + + # check for added emphasis resulting from question marks (2 or 3+) + qm_count = str(text).count("?") + qm_amplifier = 0 + if qm_count > 1: + if qm_count <= 3: qm_amplifier = qm_count*0.18 + else: qm_amplifier = 0.96 + if sum_s > 0: sum_s += qm_amplifier + elif sum_s < 0: sum_s -= qm_amplifier + + compound = normalize(sum_s) + + # want separate positive versus negative sentiment scores + pos_sum = 0.0 + neg_sum = 0.0 + neu_count = 0 + for sentiment_score in sentiments: + if sentiment_score > 0: + pos_sum += (float(sentiment_score) +1) # compensates for neutral words that are counted as 1 + if sentiment_score < 0: + neg_sum += (float(sentiment_score) -1) # when used with math.fabs(), compensates for neutrals + if sentiment_score == 0: + neu_count += 1 + + if pos_sum > math.fabs(neg_sum): pos_sum += (ep_amplifier+qm_amplifier) + elif pos_sum < math.fabs(neg_sum): neg_sum -= (ep_amplifier+qm_amplifier) + + total = pos_sum + math.fabs(neg_sum) + neu_count + pos = math.fabs(pos_sum / total) + neg = math.fabs(neg_sum / total) + neu = math.fabs(neu_count / total) + + else: + compound = 0.0; pos = 0.0; neg = 0.0; neu = 0.0 + + s = {"neg" : round(neg, 3), + "neu" : round(neu, 3), + "pos" : round(pos, 3), + "compound" : round(compound, 4)} + return s + + +if __name__ == '__main__': + # --- examples ------- + sentences = [ + "VADER is smart, handsome, and funny.", # positive sentence example + "VADER is smart, handsome, and funny!", # punctuation emphasis handled correctly (sentiment intensity adjusted) + "VADER is very smart, handsome, and funny.", # booster words handled correctly (sentiment intensity adjusted) + "VADER is VERY SMART, handsome, and FUNNY.", # emphasis for ALLCAPS handled + "VADER is VERY SMART, handsome, and FUNNY!!!",# combination of signals - VADER appropriately adjusts intensity + "VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!!",# booster words & punctuation make this close to ceiling for score + "The book was good.", # positive sentence + "The book was kind of good.", # qualified positive sentence is handled correctly (intensity adjusted) + "The plot was good, but the characters are uncompelling and the dialog is not great.", # mixed negation sentence + "A really bad, horrible book.", # negative sentence with booster words + "At least it isn't a horrible book.", # negated negative sentence with contraction + ":) and :D", # emoticons handled + "", # an empty string is correctly handled + "Today sux", # negative slang handled + "Today sux!", # negative slang with punctuation emphasis handled + "Today SUX!", # negative slang with capitalization emphasis + "Today kinda sux! But I'll get by, lol" # mixed sentiment example with slang and constrastive conjunction "but" + ] + paragraph = "It was one of the worst movies I've seen, despite good reviews. \ + Unbelievably bad acting!! Poor direction. VERY poor production. \ + The movie was bad. Very bad movie. VERY bad movie. VERY BAD movie. VERY BAD movie!" + + from nltk import tokenize + lines_list = tokenize.sent_tokenize(paragraph) + sentences.extend(lines_list) + + tricky_sentences = [ + "Most automated sentiment analysis tools are shit.", + "VADER sentiment analysis is the shit.", + "Sentiment analysis has never been good.", + "Sentiment analysis with VADER has never been this good.", + "Warren Beatty has never been so entertaining.", + "I won't say that the movie is astounding and I wouldn't claim that the movie is too banal either.", + "I like to hate Michael Bay films, but I couldn't fault this one", + "It's one thing to watch an Uwe Boll film, but another thing entirely to pay for it", + "The movie was too good", + "This movie was actually neither that funny, nor super witty.", + "This movie doesn't care about cleverness, wit or any other kind of intelligent humor.", + "Those who find ugly meanings in beautiful things are corrupt without being charming.", + "There are slow and repetitive parts, BUT it has just enough spice to keep it interesting.", + "The script is not fantastic, but the acting is decent and the cinematography is EXCELLENT!", + "Roger Dodger is one of the most compelling variations on this theme.", + "Roger Dodger is one of the least compelling variations on this theme.", + "Roger Dodger is at least compelling as a variation on the theme.", + "they fall in love with the product", + "but then it breaks", + "usually around the time the 90 day warranty expires", + "the twin towers collapsed today", + "However, Mr. Carter solemnly argues, his client carried out the kidnapping under orders and in the ''least offensive way possible.''" + ] + sentences.extend(tricky_sentences) + for sentence in sentences: + print sentence, + ss = sentiment(sentence) + print "\t" + str(ss) + + print "\n\n Done!" diff --git a/vader_sentiment_lexicon.txt b/vader_sentiment_lexicon.txt new file mode 100644 index 0000000..dba1012 --- /dev/null +++ b/vader_sentiment_lexicon.txt @@ -0,0 +1,7517 @@ +$: -1.5 0.80623 [-1, -1, -1, -1, -3, -1, -3, -1, -2, -1] +%) -0.4 1.0198 [-1, 0, -1, 0, 0, -2, -1, 2, -1, 0] +%-) -1.5 1.43178 [-2, 0, -2, -2, -1, 2, -2, -3, -2, -3] +&-: -0.4 1.42829 [-3, -1, 0, 0, -1, -1, -1, 2, -1, 2] +&: -0.7 0.64031 [0, -1, -1, -1, 1, -1, -1, -1, -1, -1] +( '}{' ) 1.6 0.66332 [1, 2, 2, 1, 1, 2, 2, 1, 3, 1] +(% -0.9 0.9434 [0, 0, 1, -1, -1, -1, -2, -2, -1, -2] +('-: 2.2 1.16619 [4, 1, 4, 3, 1, 2, 3, 1, 2, 1] +(': 2.3 0.9 [1, 3, 3, 2, 2, 4, 2, 3, 1, 2] +((-: 2.1 0.53852 [2, 2, 2, 1, 2, 3, 2, 2, 3, 2] +(* 1.1 1.13578 [2, 1, 1, -1, 1, 2, 2, -1, 2, 2] +(-% -0.7 1.26886 [-1, 2, 0, -1, -1, -2, 0, 0, -3, -1] +(-* 1.3 1.26886 [4, 1, 2, 0, 2, -1, 1, 2, 1, 1] +(-: 1.6 0.8 [2, 2, 1, 3, 1, 1, 1, 3, 1, 1] +(-:0 2.8 0.87178 [3, 2, 3, 4, 3, 2, 3, 1, 4, 3] +(-:< -0.4 2.15407 [-3, 3, -1, -1, 2, -1, -2, 3, -3, -1] +(-:o 1.5 0.67082 [3, 1, 1, 2, 2, 2, 1, 1, 1, 1] +(-:O 1.5 0.67082 [3, 1, 1, 2, 2, 2, 1, 1, 1, 1] +(-:{ -0.1 1.57797 [-2, -3, 1, -2, 1, 1, 0, 0, 2, 1] +(-:|>* 1.9 0.83066 [3, 2, 2, 1, 0, 2, 3, 2, 2, 2] +(-; 1.3 1.18743 [3, 2, 3, 0, 1, -1, 1, 2, 1, 1] +(-;| 2.1 1.13578 [3, 2, 2, 4, 1, 1, 1, 4, 2, 1] +(8 2.6 1.0198 [4, 2, 1, 3, 3, 3, 3, 1, 2, 4] +(: 2.2 1.16619 [3, 1, 1, 2, 1, 2, 4, 3, 4, 1] +(:0 2.4 1.11355 [0, 2, 3, 4, 3, 2, 3, 3, 1, 3] +(:< -0.2 2.03961 [-2, -3, 1, 1, 2, -1, 2, 1, -4, 1] +(:o 2.5 0.92195 [3, 3, 1, 3, 3, 1, 2, 2, 4, 3] +(:O 2.5 0.92195 [3, 3, 1, 3, 3, 1, 2, 2, 4, 3] +(; 1.1 1.22066 [3, 1, 1, -1, 1, 2, 2, -1, 1, 2] +(;< 0.3 1.00499 [1, 2, -1, -1, 0, 0, 1, -1, 1, 1] +(= 2.2 1.16619 [3, 1, 2, 2, 1, 1, 4, 3, 4, 1] +(?: 2.1 0.83066 [2, 2, 1, 3, 2, 2, 4, 1, 2, 2] +(^: 1.5 0.67082 [1, 2, 2, 1, 3, 2, 1, 1, 1, 1] +(^; 1.5 0.5 [1, 2, 2, 1, 2, 1, 2, 1, 1, 2] +(^;0 2.0 0.7746 [2, 2, 1, 2, 1, 4, 2, 2, 2, 2] +(^;o 1.9 0.83066 [2, 2, 1, 2, 1, 4, 2, 1, 2, 2] +(o: 1.6 0.8 [2, 1, 3, 1, 1, 1, 2, 3, 1, 1] +)': -2.0 0.44721 [-2, -2, -2, -2, -1, -3, -2, -2, -2, -2] +)-': -2.1 0.53852 [-2, -2, -3, -2, -1, -2, -3, -2, -2, -2] +)-: -2.1 0.9434 [-3, -2, -4, -1, -3, -2, -2, -2, -1, -1] +)-:< -2.2 0.4 [-2, -2, -2, -2, -2, -2, -3, -3, -2, -2] +)-:{ -2.1 0.9434 [-1, -3, -2, -1, -2, -2, -3, -4, -1, -2] +): -1.8 0.87178 [-1, -3, -1, -2, -1, -3, -1, -3, -1, -2] +):< -1.9 0.53852 [-1, -3, -2, -2, -2, -1, -2, -2, -2, -2] +):{ -2.3 0.78102 [-1, -2, -3, -3, -2, -2, -4, -2, -2, -2] +);< -2.6 0.8 [-2, -2, -2, -3, -2, -3, -2, -2, -4, -4] +*) 0.6 1.42829 [1, -1, 1, -3, 1, 1, 2, 1, 1, 2] +*-) 0.3 1.61555 [1, -3, -2, 2, 1, 1, -1, 2, 1, 1] +*-: 2.1 1.51327 [2, 2, 4, 4, 2, 1, -1, 4, 1, 2] +*-; 2.4 1.62481 [2, 3, 4, 4, 2, 1, -1, 4, 1, 4] +*: 1.9 1.04403 [2, 1, 1, 3, 1, 2, 4, 3, 1, 1] +*<|:-) 1.6 1.28062 [0, 1, 3, 1, 1, 2, 3, 0, 4, 1] +*\0/* 2.3 1.00499 [2, 0, 3, 1, 3, 3, 2, 3, 3, 3] +*^: 1.6 1.42829 [2, 2, 1, 3, 2, 2, 3, 3, -1, -1] +,-: 1.2 0.4 [1, 1, 2, 1, 1, 1, 1, 1, 2, 1] +---'-;-{@ 2.3 1.18743 [0, 1, 3, 4, 2, 3, 2, 2, 2, 4] +--<--<@ 2.2 1.249 [0, 1, 2, 4, 2, 1, 3, 2, 3, 4] +.-: -1.2 0.4 [-1, -1, -1, -1, -1, -1, -2, -1, -2, -1] +..###-: -1.7 0.78102 [-2, -3, -3, -2, -1, -1, -1, -1, -1, -2] +..###: -1.9 1.04403 [-4, -1, -3, -1, -2, -2, -1, -3, -1, -1] +/-: -1.3 0.64031 [-1, -1, -1, -1, -1, -1, -1, -2, -3, -1] +/: -1.3 0.45826 [-2, -1, -1, -1, -2, -1, -1, -2, -1, -1] +/:< -1.4 0.4899 [-1, -2, -2, -1, -1, -1, -1, -1, -2, -2] +/= -0.9 0.53852 [-1, -1, -1, 0, -1, -2, -1, -1, -1, 0] +/^: -1.0 0.7746 [-2, -1, -2, 1, -1, -1, -1, -1, -1, -1] +/o: -1.4 0.66332 [0, -2, -1, -1, -2, -2, -1, -2, -1, -2] +0-8 0.1 1.44568 [2, -1, -2, 0, 2, 0, 2, 0, -2, 0] +0-| -1.2 0.4 [-2, -1, -1, -1, -1, -1, -1, -1, -2, -1] +0:) 1.9 1.04403 [2, 2, 2, 1, 0, 2, 4, 1, 3, 2] +0:-) 1.4 0.91652 [2, 1, 0, 1, 2, 3, 2, 1, 2, 0] +0:-3 1.5 0.92195 [2, 1, 0, 2, 2, 3, 2, 1, 2, 0] +0:03 1.9 1.22066 [2, 3, 2, 0, 0, 1, 4, 2, 3, 2] +0;^) 1.6 0.91652 [0, 1, 3, 1, 2, 1, 2, 1, 2, 3] +0_o -0.3 0.78102 [0, -2, 0, 1, 0, 0, -1, 0, -1, 0] +10q 2.1 1.22066 [1, 3, 1, 2, 1, 4, 3, 4, 1, 1] +1337 2.1 1.13578 [3, 1, 4, 0, 2, 3, 1, 2, 2, 3] +143 3.2 0.74833 [4, 4, 2, 3, 2, 3, 4, 3, 4, 3] +1432 2.6 0.8 [4, 3, 3, 2, 2, 4, 2, 2, 2, 2] +14aa41 2.4 0.91652 [3, 2, 2, 4, 2, 2, 1, 2, 4, 2] +182 -2.9 1.3 [-4, 0, -3, -3, -1, -3, -4, -4, -4, -3] +187 -3.1 1.22066 [-4, 0, -4, -3, -2, -4, -3, -3, -4, -4] +2g2b4g 2.8 0.6 [4, 2, 3, 2, 3, 3, 3, 3, 2, 3] +2g2bt -0.1 1.57797 [-1, 2, -1, 1, 0, 2, 0, -3, -2, 1] +2qt 2.1 0.83066 [3, 3, 3, 3, 2, 1, 2, 1, 2, 1] +3:( -2.2 0.87178 [-4, -3, -2, -3, -2, -1, -1, -2, -2, -2] +3:) 0.5 1.28452 [-2, 1, -2, 1, 1, 1, 1, 2, 1, 1] +3:-( -2.3 0.78102 [-2, -3, -2, -2, -2, -2, -4, -1, -3, -2] +3:-) -1.4 1.35647 [-1, -2, 1, 1, -2, -2, -3, -1, -3, -2] +4col -2.2 1.16619 [-2, -3, -1, -3, -4, -1, -2, -1, -4, -1] +4q -3.1 1.51327 [-3, -3, -4, -2, -4, -4, -4, 1, -4, -4] +5fs 1.5 1.11803 [1, 2, 1, 1, 2, 3, 2, 3, -1, 1] +8) 1.9 0.7 [2, 2, 2, 1, 1, 2, 2, 3, 3, 1] +8-d 1.7 0.64031 [1, 2, 0, 2, 2, 2, 2, 2, 2, 2] +8-o -0.3 0.78102 [1, -1, 0, 0, 0, -1, 0, -2, 0, 0] +86 -1.6 1.0198 [-1, -1, -1, -1, -1, -4, -1, -2, -1, -3] +8d 2.9 0.53852 [3, 3, 4, 2, 3, 3, 3, 3, 2, 3] +:###.. -2.4 0.91652 [-3, -2, -4, -3, -1, -2, -2, -3, -1, -3] +:$ -0.2 1.83303 [-2, -1, 0, 0, -1, 1, 4, -3, 1, -1] +:& -0.6 1.0198 [-2, -1, 0, 0, -1, -1, 1, -2, 1, -1] +:'( -2.2 0.74833 [-2, -1, -2, -2, -2, -2, -4, -3, -2, -2] +:') 2.3 0.78102 [3, 1, 3, 2, 2, 2, 2, 4, 2, 2] +:'-( -2.4 0.66332 [-2, -1, -2, -3, -2, -3, -3, -3, -2, -3] +:'-) 2.7 0.64031 [2, 1, 3, 3, 3, 3, 3, 3, 3, 3] +:( -1.9 1.13578 [-2, -3, -2, 0, -1, -1, -2, -3, -1, -4] +:) 2.0 1.18322 [2, 2, 1, 1, 1, 1, 4, 3, 4, 1] +:* 2.5 1.0247 [3, 2, 1, 1, 2, 3, 4, 3, 4, 2] +:-###.. -2.5 0.92195 [-3, -2, -3, -2, -4, -3, -1, -3, -1, -3] +:-& -0.5 0.92195 [-1, -1, 0, -1, -1, -1, -1, 0, 2, -1] +:-( -1.5 0.5 [-2, -1, -1, -1, -2, -2, -2, -1, -2, -1] +:-) 1.3 0.45826 [1, 1, 1, 1, 2, 1, 2, 1, 2, 1] +:-)) 2.8 1.07703 [3, 4, 4, 1, 2, 2, 4, 2, 4, 2] +:-* 1.7 0.64031 [1, 2, 1, 1, 1, 3, 2, 2, 2, 2] +:-, 1.1 0.53852 [1, 1, 1, 0, 1, 1, 1, 1, 2, 2] +:-. -0.9 0.53852 [-1, -1, 0, -1, 0, -1, -1, -1, -2, -1] +:-/ -1.2 0.6 [0, -1, -1, -1, -1, -2, -2, -1, -1, -2] +:-< -1.5 0.5 [-2, -1, -1, -2, -1, -2, -2, -1, -2, -1] +:-d 2.3 0.45826 [2, 2, 3, 3, 2, 3, 2, 2, 2, 2] +:-D 2.3 0.45826 [2, 2, 3, 3, 2, 3, 2, 2, 2, 2] +:-o 0.1 1.3 [2, -1, -2, 0, 1, 1, 2, 0, -1, -1] +:-p 1.2 0.4 [1, 2, 1, 1, 1, 1, 2, 1, 1, 1] +:-[ -1.6 0.4899 [-1, -2, -1, -2, -2, -1, -2, -1, -2, -2] +:-\ -0.9 0.3 [-1, -1, -1, -1, -1, -1, -1, 0, -1, -1] +:-c -1.3 0.45826 [-1, -1, -1, -2, -2, -1, -2, -1, -1, -1] +:-p 1.5 0.5 [1, 1, 1, 1, 1, 2, 2, 2, 2, 2] +:-| -0.7 0.64031 [-1, -1, 0, 0, 0, -1, -1, -2, 0, -1] +:-|| -2.5 0.67082 [-2, -2, -2, -3, -2, -3, -3, -2, -2, -4] +:-Þ 0.9 1.04403 [1, -1, 1, 2, 1, -1, 1, 2, 2, 1] +:/ -1.4 0.66332 [-1, -1, -1, -1, -1, -1, -3, -2, -2, -1] +:3 2.3 1.26886 [4, 1, 1, 1, 2, 2, 4, 3, 4, 1] +:< -2.1 0.7 [-3, -1, -2, -2, -2, -2, -3, -3, -2, -1] +:> 2.1 1.22066 [3, 1, 1, 1, 1, 2, 4, 3, 4, 1] +:?) 1.3 0.64031 [3, 1, 1, 1, 1, 2, 1, 1, 1, 1] +:?c -1.6 0.4899 [-1, -2, -1, -1, -2, -2, -1, -2, -2, -2] +:@ -2.5 0.80623 [-1, -3, -3, -2, -1, -3, -3, -3, -3, -3] +:d 2.3 1.1 [4, 2, 2, 1, 2, 1, 4, 3, 3, 1] +:D 2.3 1.1 [4, 2, 2, 1, 2, 1, 4, 3, 3, 1] +:l -1.7 0.9 [-1, -3, -1, -1, -1, -3, -2, -3, -1, -1] +:o -0.4 1.35647 [2, -1, -2, 0, 1, 0, -3, 0, -1, 0] +:p 1.0 0.7746 [-1, 1, 1, 1, 1, 1, 2, 1, 2, 1] +:s -1.2 0.9798 [-2, -2, -1, -1, -1, 1, -3, -1, -1, -1] +:[ -2.0 0.63246 [-2, -2, -1, -2, -2, -3, -3, -2, -1, -2] +:\ -1.3 0.45826 [-2, -1, -1, -1, -1, -1, -2, -1, -1, -2] +:] 2.2 1.16619 [3, 1, 1, 1, 3, 1, 4, 2, 2, 4] +:^) 2.1 1.13578 [3, 2, 4, 1, 1, 1, 1, 2, 4, 2] +:^* 2.6 0.91652 [2, 1, 2, 3, 4, 4, 3, 2, 3, 2] +:^/ -1.2 0.6 [-2, -1, -2, 0, -1, -1, -1, -1, -2, -1] +:^\ -1.0 0.44721 [-1, -1, -1, -1, -1, -2, 0, -1, -1, -1] +:^| -1.0 0.0 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] +:c -2.1 0.83066 [-3, -2, -1, -2, -2, -1, -3, -3, -3, -1] +:c) 2.0 1.18322 [2, 1, 1, 1, 1, 2, 3, 4, 4, 1] +:o) 2.1 0.9434 [1, 3, 3, 1, 1, 3, 2, 3, 1, 3] +:o/ -1.4 0.4899 [-1, -1, -1, -2, -1, -1, -2, -2, -1, -2] +:o\ -1.1 0.3 [-1, -1, -1, -2, -1, -1, -1, -1, -1, -1] +:o| -0.6 1.0198 [0, 0, 0, 0, -1, 0, 0, -3, 0, -2] +:p 1.4 0.8 [3, 1, 0, 2, 1, 1, 2, 2, 1, 1] +:{ -1.9 0.83066 [-2, -1, -1, -2, -2, -1, -3, -3, -3, -1] +:| -0.4 1.11355 [-1, -1, 0, -1, -1, -1, 1, -2, 2, 0] +:} 2.1 1.22066 [3, 1, 1, 1, 2, 1, 4, 3, 4, 1] +:Þ 1.1 0.53852 [1, 1, 1, 1, 0, 1, 1, 2, 2, 1] +;) 0.9 1.04403 [2, -1, 1, 1, 1, 1, -1, 2, 2, 1] +;-) 1.0 1.73205 [1, -2, 1, -2, 1, 4, 2, 1, 2, 2] +;-* 2.2 0.74833 [2, 2, 1, 3, 4, 2, 2, 2, 2, 2] +;-] 0.7 1.67631 [1, -2, 1, -3, 1, 2, 2, 1, 2, 2] +;d 0.8 1.249 [2, -1, 2, 1, 1, 1, -2, 2, 1, 1] +;D 0.8 1.249 [2, -1, 2, 1, 1, 1, -2, 2, 1, 1] +;] 0.6 1.11355 [1, -1, 1, 1, 1, 1, -2, 2, 1, 1] +;^) 1.4 0.91652 [2, 2, 1, 2, 1, 2, -1, 1, 2, 2] +-: -2.0 0.89443 [-2, -3, -2, -4, -1, -1, -1, -2, -2, -2] +>.< -1.3 0.45826 [-1, -2, -1, -2, -2, -1, -1, -1, -1, -1] +>: -2.1 1.13578 [-4, -1, -1, -4, -2, -3, -1, -1, -2, -2] +>:( -2.7 0.64031 [-2, -3, -2, -3, -3, -2, -4, -2, -3, -3] +>:) 0.4 1.42829 [1, 1, 2, 1, -1, -2, 1, 2, -2, 1] +>:-( -2.7 0.78102 [-3, -2, -3, -2, -4, -2, -3, -2, -2, -4] +>:-) -0.4 1.68523 [1, 2, 1, -2, -2, -1, -1, -3, -1, 2] +>:/ -1.6 0.8 [-1, -2, -1, -3, -1, -1, -1, -1, -2, -3] +>:o -1.2 1.16619 [-3, -1, -2, 0, -2, -2, 0, -1, 1, -2] +>:p 1.0 0.7746 [-1, 1, 1, 2, 1, 2, 1, 1, 1, 1] +>:[ -2.1 0.53852 [-2, -2, -2, -2, -3, -3, -2, -1, -2, -2] +>:\ -1.7 0.64031 [-1, -2, -1, -2, -2, -3, -1, -1, -2, -2] +>;( -2.9 0.7 [-3, -4, -3, -2, -2, -3, -3, -3, -2, -4] +>;) 0.1 1.04403 [-1, 1, 0, -1, 2, 0, -1, 1, 1, -1] +>_>^ 2.1 0.9434 [2, 2, 1, 4, 3, 2, 1, 3, 1, 2] +@: -2.1 0.9434 [-3, -2, -3, -1, -2, -4, -1, -2, -1, -2] +@>-->-- 2.1 1.22066 [1, 1, 0, 2, 4, 2, 4, 2, 3, 2] +@}-;-'--- 2.2 1.32665 [0, 1, 3, 2, 1, 4, 4, 1, 3, 3] +aas 2.5 0.80623 [2, 3, 3, 4, 1, 2, 3, 2, 2, 3] +aayf 2.7 0.78102 [2, 3, 2, 4, 3, 2, 2, 3, 4, 2] +afu -2.9 0.83066 [-3, -3, -3, -3, -3, -1, -4, -4, -2, -3] +alol 2.8 0.74833 [2, 2, 2, 3, 3, 2, 3, 4, 4, 3] +ambw 2.9 0.7 [2, 3, 4, 2, 3, 2, 3, 3, 4, 3] +aml 3.4 0.66332 [4, 3, 2, 4, 3, 3, 4, 4, 3, 4] +atab -1.9 1.22066 [-2, 0, -1, -2, -1, -1, -2, -4, -4, -2] +awol -1.3 0.78102 [0, -1, -1, -1, -1, -1, -2, -2, -3, -1] +ayc 0.2 0.9798 [0, 1, -1, 1, 0, 1, 0, -1, 2, -1] +ayor -1.2 0.6 [-1, -1, -2, -2, -1, -1, -1, 0, -2, -1] +aug-00 0.3 1.18743 [2, 0, -2, 0, 0, 1, -1, 2, 1, 0] +bfd -2.7 0.78102 [-3, -2, -4, -2, -3, -2, -3, -2, -4, -2] +bfe -2.6 1.35647 [-3, -3, -4, -2, -3, -2, 1, -3, -4, -3] +bff 2.9 0.83066 [3, 3, 4, 2, 4, 2, 2, 3, 4, 2] +bffn 1.0 0.89443 [2, 1, -1, 1, 0, 1, 2, 1, 2, 1] +bl 2.3 1.1 [2, 1, 4, 1, 2, 2, 4, 3, 1, 3] +bsod -2.2 1.07703 [-1, -4, -3, -3, 0, -2, -3, -2, -2, -2] +btd -2.1 0.83066 [-1, -2, -3, -3, -3, -1, -3, -2, -1, -2] +btdt -0.1 1.22066 [0, -1, 0, -1, 0, 3, 1, -1, -1, -1] +bz 0.4 1.35647 [-1, 0, 0, 0, 4, 1, -1, 1, 0, 0] +b^d 2.6 0.8 [3, 2, 2, 4, 3, 1, 3, 3, 3, 2] +cwot -2.3 0.45826 [-3, -2, -2, -2, -2, -3, -2, -3, -2, -2] +d-': -2.5 0.67082 [-3, -3, -2, -2, -2, -4, -2, -3, -2, -2] +d8 -3.2 0.6 [-3, -3, -3, -3, -4, -4, -2, -3, -3, -4] +d: -2.9 0.83066 [-3, -3, -3, -3, -2, -4, -1, -3, -3, -4] +d:< -3.2 0.9798 [-4, -4, -4, -1, -3, -3, -4, -2, -3, -4] +d; -2.9 0.83066 [-1, -3, -3, -3, -3, -4, -2, -3, -3, -4] +d= -3.0 0.89443 [-4, -3, -3, -3, -2, -4, -1, -3, -3, -4] +doa -2.3 1.00499 [-2, -3, -3, -2, -2, -2, -4, 0, -2, -3] +dx -3.0 0.63246 [-3, -2, -3, -3, -4, -3, -4, -2, -3, -3] +ez 1.5 0.67082 [3, 2, 2, 1, 1, 1, 2, 1, 1, 1] +fav 2.4 0.91652 [3, 1, 3, 2, 2, 3, 1, 2, 3, 4] +fcol -1.8 0.74833 [-2, -2, -1, -2, -1, -2, -1, -3, -3, -1] +ff 1.8 1.249 [4, 2, 1, 2, 1, 3, 3, 0, 2, 0] +ffs -2.8 0.9798 [-2, -2, -3, -3, -2, -4, -4, -4, -1, -3] +fkm -2.4 1.35647 [-4, -1, -4, -2, -2, -3, -1, 0, -3, -4] +foaf 1.8 1.249 [2, 1, 2, 0, 4, 1, 1, 1, 2, 4] +ftw 2.0 0.7746 [2, 1, 1, 2, 2, 2, 3, 3, 1, 3] +fu -3.7 0.45826 [-3, -4, -4, -3, -3, -4, -4, -4, -4, -4] +fubar -3.0 1.09545 [-4, -3, -3, -4, -3, -3, -3, -4, 0, -3] +fwb 2.5 1.43178 [2, 3, 4, 0, 1, 2, 4, 1, 4, 4] +fyi 0.8 1.66132 [0, 1, 0, -1, 0, 0, 4, 4, 0, 0] +fysa 0.4 0.91652 [0, 0, 0, 1, 0, 3, 0, 0, 0, 0] +g1 1.4 0.4899 [2, 1, 1, 1, 2, 1, 2, 1, 1, 2] +gg 1.2 0.74833 [0, 2, 2, 1, 0, 1, 2, 2, 1, 1] +gga 1.7 0.45826 [2, 2, 1, 2, 2, 1, 2, 2, 1, 2] +gigo -0.6 1.11355 [-2, -1, 1, 0, 0, 0, -1, -2, -2, 1] +gj 2.0 1.0 [2, 1, 2, 1, 1, 3, 4, 2, 3, 1] +gl 1.3 0.64031 [1, 1, 1, 1, 3, 1, 1, 2, 1, 1] +gla 2.5 0.92195 [1, 2, 2, 4, 2, 4, 2, 3, 3, 2] +gn 1.2 0.74833 [1, 1, 1, 1, 3, 1, 1, 2, 1, 0] +gr8 2.7 0.78102 [1, 3, 3, 4, 3, 2, 3, 2, 3, 3] +grrr -0.4 1.42829 [-2, -1, 0, 1, -2, -1, -1, 3, 0, -1] +gt 1.1 0.53852 [1, 1, 1, 1, 1, 1, 2, 1, 0, 2] +h&k 2.3 0.78102 [2, 2, 2, 3, 4, 2, 3, 2, 1, 2] +hagd 2.2 0.87178 [2, 2, 3, 2, 1, 3, 4, 1, 2, 2] +hagn 2.2 0.87178 [2, 2, 3, 2, 1, 3, 4, 1, 2, 2] +hago 1.2 0.4 [1, 2, 1, 1, 1, 2, 1, 1, 1, 1] +hak 1.9 0.7 [3, 1, 2, 2, 1, 2, 3, 2, 1, 2] +hand 2.2 0.87178 [2, 2, 1, 3, 2, 3, 4, 1, 2, 2] +hho1/2k 1.4 1.11355 [1, -1, 2, 3, 1, 1, 1, 2, 3, 1] +hhoj 2.0 1.09545 [4, 2, 1, 1, 2, 1, 1, 4, 2, 2] +hhok 0.9 0.9434 [1, 2, 1, 0, -1, 0, 2, 1, 1, 2] +hugz 2.0 0.7746 [2, 3, 1, 3, 1, 3, 1, 2, 2, 2] +hi5 1.9 0.53852 [2, 2, 2, 1, 3, 2, 1, 2, 2, 2] +idk -0.4 0.66332 [0, 0, 0, 0, -1, -2, 0, 0, 0, -1] +ijs 0.7 1.84662 [0, -1, 0, -1, 0, 4, 0, 4, -1, 2] +ilu 3.4 0.66332 [3, 4, 3, 4, 2, 3, 4, 3, 4, 4] +iluaaf 2.7 1.1 [3, 3, 3, 2, 3, 0, 4, 3, 2, 4] +ily 3.4 0.66332 [3, 4, 3, 4, 2, 3, 4, 3, 4, 4] +ily2 2.6 0.66332 [3, 2, 3, 2, 3, 2, 3, 4, 2, 2] +iou 0.7 1.34536 [0, 0, -1, 2, 0, 0, 0, 4, 1, 1] +iyq 2.3 1.18743 [3, 3, 1, 1, 2, 1, 4, 4, 3, 1] +j/j 2.0 1.34164 [4, 1, 1, 1, 1, 4, 4, 1, 2, 1] +j/k 1.6 1.2 [1, 2, 1, 3, 0, 0, 2, 2, 1, 4] +j/p 1.4 0.66332 [1, 1, 0, 2, 1, 2, 2, 2, 1, 2] +j/t -0.2 1.46969 [1, -1, -1, -2, 1, 1, 2, -2, 1, -2] +j/w 1.0 1.0 [1, 1, 1, 3, 0, 0, 0, 2, 0, 2] +j4f 1.4 0.8 [2, 1, 1, 0, 3, 1, 1, 1, 2, 2] +j4g 1.7 1.18743 [1, 4, 1, 1, 3, 1, 3, 0, 2, 1] +jho 0.8 0.4 [1, 1, 1, 1, 0, 1, 1, 1, 0, 1] +jhomf 1.0 0.63246 [1, 1, 1, 0, 1, 0, 2, 2, 1, 1] +jj 1.0 0.63246 [1, 1, 1, 1, 2, 0, 2, 1, 1, 0] +jk 0.9 1.22066 [1, 0, 0, 1, 0, 0, 2, 1, 4, 0] +jp 0.8 0.74833 [1, 1, 1, 0, 2, 0, 2, 0, 1, 0] +jt 0.9 0.83066 [1, 1, 0, 2, 2, 0, 2, 0, 1, 0] +jw 1.6 1.68523 [3, 0, 0, 0, 0, 0, 3, 4, 2, 4] +jealz -1.2 0.9798 [-1, -1, -1, 1, -2, -2, -1, -3, -1, -1] +k4y 2.3 1.00499 [2, 1, 1, 2, 4, 2, 3, 4, 2, 2] +kfy 2.3 0.64031 [2, 2, 2, 1, 3, 2, 3, 3, 2, 3] +kia -3.2 0.6 [-3, -3, -3, -4, -3, -2, -3, -3, -4, -4] +kk 1.5 1.0247 [2, 1, 0, 0, 1, 2, 3, 3, 2, 1] +kmuf 2.2 1.4 [2, 2, 2, 3, 4, 3, -1, 1, 4, 2] +l 2.0 0.7746 [2, 1, 2, 3, 2, 3, 1, 3, 2, 1] +l&r 2.2 0.74833 [3, 2, 2, 3, 1, 3, 1, 3, 2, 2] +laoj 1.3 1.73494 [1, -2, -1, 3, 3, 2, 4, 1, 1, 1] +lmao 2.0 1.18322 [3, 0, 3, 0, 3, 1, 3, 2, 3, 2] +lmbao 1.8 1.77764 [3, 2, 2, 2, 1, 3, -3, 2, 4, 2] +lmfao 2.5 1.28452 [3, 2, 3, 3, 3, -1, 4, 2, 3, 2] +lmso 2.7 0.78102 [3, 3, 4, 3, 3, 1, 3, 3, 2, 2] +lol 2.9 0.83066 [4, 2, 2, 2, 4, 2, 3, 3, 4, 3] +lolz 2.7 0.78102 [2, 3, 3, 2, 2, 4, 4, 3, 2, 2] +lts 1.6 0.66332 [1, 1, 2, 2, 1, 3, 1, 1, 2, 2] +ly 2.6 0.91652 [2, 2, 1, 3, 4, 4, 3, 2, 2, 3] +ly4e 2.7 0.78102 [3, 3, 3, 2, 1, 3, 3, 4, 2, 3] +lya 3.3 0.78102 [3, 4, 4, 4, 2, 2, 3, 4, 3, 4] +lyb 3.0 0.63246 [3, 3, 4, 3, 2, 3, 2, 4, 3, 3] +lyl 3.1 0.7 [4, 3, 4, 3, 2, 3, 3, 2, 4, 3] +lylab 2.7 0.78102 [3, 3, 3, 1, 3, 4, 2, 2, 3, 3] +lylas 2.6 0.8 [3, 3, 3, 1, 3, 4, 2, 2, 2, 3] +lylb 1.6 1.56205 [2, 2, 3, -2, 4, 1, 3, 1, 1, 1] +m8 1.4 1.0198 [3, 0, 1, 0, 1, 3, 2, 2, 1, 1] +mia -1.2 0.4 [-2, -1, -1, -2, -1, -1, -1, -1, -1, -1] +mml 2.0 1.0 [1, 1, 2, 3, 3, 2, 1, 2, 4, 1] +mofo -2.4 2.2 [-4, -4, -4, 0, -3, -2, -2, -4, 3, -4] +muah 2.8 1.07703 [1, 2, 4, 4, 4, 2, 4, 2, 2, 3] +mubar -1.0 2.36643 [-4, -2, -3, -2, -2, -2, 1, 4, 2, -2] +musm 0.9 2.07123 [-1, 1, 1, 1, 4, 3, 1, -4, 1, 2] +mwah 2.5 0.80623 [2, 2, 2, 4, 2, 3, 2, 2, 4, 2] +n1 1.9 1.04403 [1, 1, 3, 2, 2, 3, 4, 1, 1, 1] +nbd 1.3 1.34536 [2, 1, 0, 0, 0, 4, 2, 0, 3, 1] +nbif -0.5 0.67082 [-1, -2, 0, 0, 0, 0, -1, -1, 0, 0] +nfc -2.7 0.9 [-3, -2, -2, -3, -1, -2, -4, -3, -4, -3] +nfw -2.4 1.0198 [-2, -2, -1, -3, -1, -2, -4, -3, -4, -2] +nh 2.2 0.6 [2, 2, 2, 2, 1, 3, 3, 3, 2, 2] +nimby -0.8 0.6 [0, 0, -1, 0, -1, -2, -1, -1, -1, -1] +nimjd -0.7 0.78102 [0, -2, -1, -2, 0, -1, 0, 0, 0, -1] +nimq -0.2 0.6 [0, 0, 0, 0, 0, 0, 0, 0, -2, 0] +nimy -1.4 1.68523 [-1, -2, -3, -2, -1, 2, -3, 0, 0, -4] +nitl -1.5 0.92195 [-1, -1, -2, -3, -1, -3, -1, -2, 0, -1] +nme -2.1 1.13578 [-1, -2, -2, -1, -4, -2, -3, -3, 0, -3] +noyb -0.7 1.67631 [-1, -2, 0, -1, -1, -2, -2, -1, 4, -1] +np 1.4 1.0198 [0, 1, 1, 1, 1, 2, 2, 4, 1, 1] +ntmu 1.4 0.66332 [1, 1, 0, 1, 2, 2, 2, 2, 2, 1] +o-8 -0.5 1.5 [2, -1, 0, 0, -2, -2, 0, -2, 2, -2] +o-: -0.3 1.18743 [2, -1, 0, 0, -1, -2, 0, -2, 1, 0] +o-| -1.1 0.53852 [-1, -1, -1, 0, -1, -1, -1, -2, -2, -1] +o.o -0.6 0.8 [-1, -1, -2, 0, 1, 0, -1, 0, -1, -1] +O.o -0.6 0.8 [-1, -1, -2, 0, 1, 0, -1, 0, -1, -1] +o.O -0.6 0.8 [-1, -1, -2, 0, 1, 0, -1, 0, -1, -1] +o: -0.2 0.87178 [-1, 0, -1, -2, 0, 1, 0, 1, 0, 0] +o:) 1.5 0.67082 [3, 1, 1, 2, 2, 2, 1, 1, 1, 1] +o:-) 2.0 1.18322 [1, 4, 1, 2, 4, 1, 1, 2, 3, 1] +o:-3 2.2 0.9798 [1, 4, 2, 3, 3, 2, 1, 2, 3, 1] +o:3 2.3 0.78102 [3, 3, 2, 2, 1, 2, 4, 2, 2, 2] +o:< -0.3 1.1 [-1, -1, -2, 0, -1, 0, 1, 2, 0, -1] +o;^) 1.6 0.8 [1, 2, 1, 2, 1, 2, 2, 0, 3, 2] +ok 1.6 1.42829 [0, 0, 1, 1, 1, 4, 3, 4, 1, 1] +o_o -0.5 0.92195 [0, -1, 0, -2, -2, 0, -1, 1, 0, 0] +O_o -0.5 0.92195 [0, -1, 0, -2, -2, 0, -1, 1, 0, 0] +o_O -0.5 0.92195 [0, -1, 0, -2, -2, 0, -1, 1, 0, 0] +pita -2.4 1.2 [-2, -1, -1, -4, -4, -2, -4, -2, -3, -1] +pls 0.3 0.45826 [0, 1, 1, 1, 0, 0, 0, 0, 0, 0] +plz 0.3 0.45826 [0, 1, 1, 1, 0, 0, 0, 0, 0, 0] +pmbi 0.8 1.32665 [3, 0, 0, 1, 1, -2, 2, 2, 0, 1] +pmfji 0.3 0.78102 [0, 0, 1, 0, 2, -1, 0, 1, 0, 0] +pmji 0.7 1.00499 [1, 2, 0, -1, 0, 0, 2, 2, 1, 0] +po -2.6 0.91652 [-2, -3, -4, -3, -3, -3, -1, -3, -1, -3] +ptl 2.6 1.11355 [3, 4, 2, 4, 1, 2, 3, 1, 4, 2] +pu -1.1 1.3 [-3, -1, -3, -2, -1, -1, -1, -1, 1, 1] +qq -2.2 0.6 [-2, -2, -1, -3, -3, -2, -2, -3, -2, -2] +qt 1.8 0.6 [2, 2, 1, 2, 1, 3, 2, 1, 2, 2] +r&r 2.4 1.0198 [2, 4, 2, 3, 1, 4, 2, 2, 1, 3] +rofl 2.7 0.78102 [3, 2, 2, 2, 4, 4, 2, 3, 3, 2] +roflmao 2.5 1.11803 [4, 2, 2, 4, 1, 1, 2, 4, 3, 2] +rotfl 2.6 0.66332 [3, 2, 3, 3, 1, 3, 3, 3, 2, 3] +rotflmao 2.8 1.07703 [4, 3, 2, 4, 1, 1, 4, 3, 3, 3] +rotflmfao 2.5 1.11803 [3, 4, 1, 3, 3, 3, 0, 3, 2, 3] +rotflol 3.0 1.09545 [1, 4, 4, 4, 2, 2, 2, 3, 4, 4] +rotgl 2.9 0.7 [4, 3, 2, 2, 3, 3, 3, 2, 4, 3] +rotglmao 1.8 2.4 [3, 3, 4, 3, -1, 1, 4, -4, 2, 3] +s: -1.1 0.83066 [-1, -1, -2, -2, -1, -1, -2, -1, 1, -1] +sapfu -1.1 1.57797 [-2, 0, -3, -1, -1, 1, -2, 2, -2, -3] +sete 2.8 0.87178 [3, 3, 3, 2, 3, 3, 4, 4, 1, 2] +sfete 2.7 0.78102 [4, 3, 3, 3, 2, 4, 2, 2, 2, 2] +sgtm 2.4 1.0198 [2, 1, 1, 2, 3, 3, 2, 2, 4, 4] +slap 0.6 2.15407 [2, -1, 1, -1, 0, 4, -3, 4, 1, -1] +slaw 2.1 1.04403 [3, 2, 0, 2, 2, 2, 3, 1, 4, 2] +smh -1.3 0.64031 [-2, -1, 0, -1, -1, -2, -2, -1, -2, -1] +snafu -2.5 1.11803 [-3, -4, -3, -3, -1, 0, -2, -3, -3, -3] +sob -2.8 0.9798 [-3, -4, -3, -2, -2, -1, -2, -4, -4, -3] +swak 2.3 1.00499 [2, 2, 2, 1, 4, 2, 3, 2, 1, 4] +tgif 2.3 1.34536 [1, 3, 3, 3, -1, 2, 4, 2, 3, 3] +thks 1.4 0.4899 [1, 2, 1, 2, 1, 2, 1, 1, 2, 1] +thx 1.5 0.92195 [0, 1, 3, 2, 1, 2, 1, 1, 3, 1] +tia 2.3 0.9 [3, 1, 2, 1, 4, 3, 2, 3, 2, 2] +tmi -0.3 1.61555 [-1, -1, 2, -1, 1, -2, -2, -1, 3, -1] +tnx 1.1 0.53852 [2, 1, 1, 0, 1, 1, 2, 1, 1, 1] +true 1.8 1.32665 [2, 1, 1, 0, 1, 4, 3, 1, 4, 1] +tx 1.5 0.92195 [3, 2, 1, 0, 2, 1, 3, 1, 1, 1] +txs 1.1 0.7 [1, 2, 0, 1, 2, 0, 1, 2, 1, 1] +ty 1.6 0.66332 [1, 2, 3, 1, 2, 2, 1, 2, 1, 1] +tyvm 2.5 1.11803 [2, 2, 1, 3, 1, 4, 2, 4, 2, 4] +urw 1.9 1.13578 [1, 2, 1, 2, 4, 2, 4, 1, 1, 1] +vbg 2.1 1.75784 [2, 3, 3, 3, 3, -3, 3, 2, 2, 3] +vbs 3.1 0.53852 [2, 3, 3, 3, 4, 4, 3, 3, 3, 3] +vip 2.3 1.00499 [2, 1, 1, 3, 4, 2, 2, 4, 2, 2] +vwd 2.6 0.91652 [4, 2, 4, 2, 1, 3, 3, 2, 3, 2] +vwp 2.1 0.7 [3, 1, 2, 2, 3, 2, 2, 3, 1, 2] +wag -0.2 0.74833 [-1, 0, 0, 0, 0, 0, -2, 1, 0, 0] +wd 2.7 1.1 [3, 1, 4, 3, 4, 2, 1, 3, 2, 4] +wilco 0.9 0.9434 [1, 3, 1, 0, 1, 0, 2, 1, 0, 0] +wp 1.0 0.0 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] +wtf -2.8 0.74833 [-4, -3, -2, -3, -2, -2, -2, -4, -3, -3] +wtg 2.1 0.7 [1, 3, 2, 3, 2, 2, 2, 1, 2, 3] +wth -2.4 0.4899 [-2, -3, -2, -3, -2, -2, -2, -3, -3, -2] +x-d 2.7 0.78102 [1, 3, 4, 2, 3, 3, 3, 2, 3, 3] +x-p 1.8 0.87178 [2, 1, 3, 1, 3, 1, 3, 1, 2, 1] +xd 2.7 0.9 [1, 4, 4, 3, 2, 2, 3, 3, 2, 3] +xlnt 3.0 0.89443 [4, 3, 3, 1, 4, 4, 3, 3, 3, 2] +xoxo 3.0 0.7746 [2, 2, 4, 2, 3, 3, 4, 3, 3, 4] +xoxozzz 2.3 0.78102 [3, 1, 2, 2, 2, 2, 3, 2, 4, 2] +xp 1.2 0.4 [1, 1, 1, 1, 2, 1, 2, 1, 1, 1] +xqzt 1.6 1.42829 [0, 2, 1, 2, 4, -1, 3, 1, 1, 3] +xtc 0.8 1.93907 [2, 0, -3, 3, 3, -1, 3, 1, -1, 1] +yolo 1.1 0.83066 [0, 1, 1, 2, 1, 1, 1, 3, 0, 1] +yoyo 0.4 1.85472 [-1, 0, -1, -1, 4, 2, -2, 2, 2, -1] +yvw 1.6 0.4899 [1, 2, 1, 1, 2, 2, 2, 1, 2, 2] +yw 1.8 1.32665 [1, 1, 1, 4, 1, 1, 4, 0, 3, 2] +ywia 2.5 1.11803 [3, 2, 3, 4, 1, 1, 1, 3, 3, 4] +zzz -1.2 0.87178 [0, -1, 0, -1, -3, -1, -1, -2, -2, -1] +[-; 0.5 1.28452 [1, -1, -1, 1, 1, 1, 2, -2, 2, 1] +[: 1.3 0.45826 [1, 1, 2, 1, 2, 2, 1, 1, 1, 1] +[; 1.0 1.34164 [2, 1, 2, 2, 1, 2, 2, -2, -1, 1] +[= 1.7 0.64031 [2, 2, 1, 1, 1, 2, 2, 3, 2, 1] +\-: -1.0 1.18322 [-3, -1, -1, -1, -1, -1, 2, -2, -1, -1] +\: -1.0 0.0 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] +\:< -1.7 1.18743 [-1, -3, -2, -2, -3, -3, -2, -1, 1, -1] +\= -1.1 0.3 [-1, -1, -1, -1, -1, -1, -1, -2, -1, -1] +\^: -1.3 0.45826 [-1, -1, -1, -2, -1, -1, -1, -2, -2, -1] +\o/ 2.2 0.9798 [2, 1, 1, 2, 4, 2, 2, 4, 2, 2] +\o: -1.2 0.4 [-1, -1, -1, -1, -2, -1, -1, -2, -1, -1] +]-: -2.1 0.53852 [-2, -3, -3, -2, -2, -2, -1, -2, -2, -2] +]: -1.6 0.66332 [-1, -2, -1, -2, -3, -2, -1, -1, -1, -2] +]:< -2.5 0.80623 [-2, -2, -2, -3, -4, -2, -2, -2, -2, -4] +^<_< 1.4 1.11355 [3, 1, 3, 2, 1, 1, 1, -1, 2, 1] +^urs -2.8 0.6 [-2, -3, -3, -2, -3, -3, -2, -3, -4, -3] +abandon -1.9 0.53852 [-1, -2, -2, -2, -2, -3, -2, -2, -1, -2] +abandoned -2.0 1.09545 [-1, -1, -3, -2, -1, -4, -1, -3, -3, -1] +abandoner -1.9 0.83066 [-1, -1, -3, -2, -1, -3, -1, -2, -3, -2] +abandoners -1.9 0.83066 [-2, -3, -2, -3, -2, -1, -2, -2, 0, -2] +abandoning -1.6 0.8 [-3, -2, -3, -2, -1, -1, -1, -1, -1, -1] +abandonment -2.4 1.0198 [-4, -2, -1, -4, -2, -1, -2, -3, -3, -2] +abandonments -1.7 0.45826 [-2, -1, -2, -2, -1, -2, -1, -2, -2, -2] +abandons -1.3 0.9 [-2, -1, -1, -2, -1, -2, -1, -2, 1, -2] +abducted -2.3 1.18743 [-3, -1, 0, -3, -1, -3, -4, -2, -3, -3] +abduction -2.8 0.87178 [-4, -3, -3, -4, -1, -3, -2, -2, -3, -3] +abductions -2.0 1.41421 [-3, -4, -1, -3, -1, -3, 1, -2, -1, -3] +abhor -2.0 1.09545 [-3, -3, -1, -1, -2, -1, -3, -3, 0, -3] +abhorred -2.4 1.49666 [-4, -4, 0, -3, -2, -1, -4, -3, -3, 0] +abhorrent -3.1 1.3 [-4, -4, -4, -2, 0, -4, -2, -3, -4, -4] +abhors -2.9 1.51327 [0, -4, -3, -3, -4, -4, 0, -4, -3, -4] +abilities 1.0 0.63246 [1, 2, 0, 1, 0, 1, 1, 1, 1, 2] +ability 1.3 0.64031 [1, 1, 1, 0, 1, 2, 2, 2, 2, 1] +aboard 0.1 0.3 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0] +absentee -1.1 0.53852 [-1, -1, 0, -2, -1, -1, -2, -1, -1, -1] +absentees -0.8 0.6 [-1, 0, 0, -1, -1, 0, -2, -1, -1, -1] +absolve 1.2 1.46969 [2, -3, 2, 2, 1, 1, 2, 1, 2, 2] +absolved 1.5 0.92195 [3, 1, 2, 1, 0, 2, 3, 1, 1, 1] +absolves 1.3 1.00499 [3, 1, 1, 0, 0, 2, 3, 1, 1, 1] +absolving 1.6 0.8 [3, 1, 2, 1, 1, 2, 3, 1, 1, 1] +abuse -3.2 0.6 [-4, -2, -3, -4, -3, -4, -3, -3, -3, -3] +abused -2.3 0.64031 [-2, -2, -3, -2, -2, -4, -2, -2, -2, -2] +abuser -2.6 0.4899 [-3, -2, -3, -3, -2, -3, -2, -2, -3, -3] +abusers -2.6 1.0198 [-2, -3, -3, -3, -3, -2, -3, -4, -3, 0] +abuses -2.6 0.66332 [-3, -2, -3, -3, -3, -3, -1, -2, -3, -3] +abusing -2.0 1.41421 [-1, -2, -2, -4, -4, -2, -3, -1, 1, -2] +abusive -3.2 0.74833 [-4, -3, -3, -4, -4, -3, -4, -2, -3, -2] +abusively -2.8 0.6 [-3, -4, -3, -2, -3, -2, -2, -3, -3, -3] +abusiveness -2.5 0.92195 [-2, -4, -2, -3, -2, -3, -4, -2, -1, -2] +abusivenesses -3.0 0.63246 [-3, -3, -4, -3, -4, -2, -2, -3, -3, -3] +accept 1.6 0.91652 [2, 1, 2, 1, 1, 2, 4, 1, 1, 1] +acceptabilities 1.6 0.66332 [0, 2, 2, 2, 1, 2, 2, 2, 1, 2] +acceptability 1.1 0.53852 [1, 0, 1, 2, 1, 2, 1, 1, 1, 1] +acceptable 1.3 0.45826 [1, 2, 1, 1, 1, 2, 1, 1, 2, 1] +acceptableness 1.3 0.9 [1, 0, 2, 1, 2, 1, 1, 0, 2, 3] 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-1, -3, -2, -1, -1, 2, -2, -1] +accuse -0.8 1.53623 [-3, -1, -1, -2, 1, -2, 1, -2, 2, -1] +accused -1.2 1.46969 [-2, -1, -2, 2, -2, -3, -2, -2, -1, 1] +accuses -1.4 1.0198 [-2, -1, -2, 1, -2, -3, -1, -2, -1, -1] +accusing -0.7 1.34536 [-2, -1, -1, 1, -3, -1, -1, 2, -1, 0] +ache -1.6 1.2 [-1, -2, -2, -2, -1, -4, -1, 1, -2, -2] +ached -1.6 0.8 [-2, -2, -1, -2, -1, -2, -3, 0, -1, -2] +aches -1.0 0.7746 [-1, -2, -1, -1, -1, 1, -2, -1, -1, -1] +achievable 1.3 0.45826 [2, 1, 1, 1, 1, 1, 1, 2, 2, 1] +aching -2.2 0.74833 [-2, -3, -2, -1, -3, -3, -2, -3, -1, -2] +acquit 0.8 1.72047 [-3, 3, -1, 3, 2, 1, 1, 1, 0, 1] +acquits 0.1 1.37477 [1, -3, -1, 0, 2, 0, -1, 1, 1, 1] +acquitted 1.0 0.89443 [2, 2, 1, 1, 2, 0, 1, 1, -1, 1] +acquitting 1.3 0.78102 [3, 2, 0, 1, 1, 1, 2, 1, 1, 1] +acrimonious -1.7 1.73494 [-1, -3, -2, -3, 3, -3, -1, -2, -2, -3] +active 1.7 1.26886 [1, 2, 1, 1, 1, 4, 2, 4, 0, 1] +actively 1.3 0.78102 [0, 1, 0, 2, 2, 1, 1, 2, 2, 2] +activeness 0.6 0.8 [0, 2, 0, 0, 1, 0, 1, 0, 2, 0] 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2, 1, 1, 1] +admiringly 2.3 0.64031 [1, 3, 3, 2, 2, 2, 2, 3, 3, 2] +admit 0.8 1.07703 [0, 0, 0, 0, 0, 1, 3, 2, 2, 0] +admits 1.2 0.87178 [1, 2, 2, 2, 0, 0, 1, 2, 0, 2] +admitted 0.4 0.66332 [0, 1, 0, 1, 0, 0, 2, 0, 0, 0] +admonished -1.9 0.9434 [-2, -2, -2, -1, -2, -3, -1, -1, -1, -4] +adopt 0.7 0.64031 [0, 0, 1, 1, 1, 0, 1, 0, 1, 2] +adopts 0.7 0.64031 [0, 0, 1, 2, 1, 0, 1, 1, 0, 1] +adorability 2.2 0.74833 [2, 2, 2, 2, 1, 2, 3, 2, 4, 2] +adorable 2.2 0.6 [3, 2, 2, 3, 2, 2, 1, 3, 2, 2] +adorableness 2.5 0.67082 [2, 3, 3, 2, 3, 2, 1, 3, 3, 3] +adorably 2.1 0.7 [3, 1, 2, 3, 2, 2, 1, 3, 2, 2] +adoration 2.9 0.7 [3, 3, 3, 2, 3, 3, 4, 2, 4, 2] +adorations 2.2 0.87178 [2, 2, 3, 1, 3, 1, 3, 3, 1, 3] +adore 2.6 0.91652 [3, 3, 1, 2, 3, 3, 3, 4, 1, 3] +adored 1.8 0.87178 [2, 3, 3, 2, 2, 1, 1, 0, 2, 2] +adorer 1.7 1.1 [2, 4, 3, 1, 2, 1, 1, 0, 2, 1] +adorers 2.1 0.7 [3, 2, 1, 2, 2, 2, 3, 2, 3, 1] +adores 1.6 0.66332 [2, 1, 3, 2, 2, 1, 1, 1, 2, 1] +adoring 2.6 0.66332 [2, 3, 3, 3, 1, 3, 3, 2, 3, 3] +adoringly 2.4 0.8 [2, 3, 2, 3, 3, 3, 3, 1, 1, 3] +adorn 0.9 0.53852 [1, 1, 1, 0, 2, 1, 1, 0, 1, 1] +adorned 0.8 1.249 [1, 1, 0, 2, -1, 3, -1, 2, 1, 0] +adorner 1.3 0.78102 [1, 1, 1, 2, 1, 3, 1, 2, 1, 0] +adorners 0.9 0.9434 [2, 2, 0, 1, -1, 2, 1, 1, 0, 1] +adorning 1.0 0.7746 [0, 0, 1, 1, 1, 2, 2, 1, 0, 2] +adornment 1.3 0.78102 [1, 3, 1, 0, 2, 2, 1, 1, 1, 1] +adornments 0.8 1.16619 [2, -1, 0, 0, 2, 1, 2, -1, 1, 2] +adorns 0.5 1.56525 [3, -1, 1, 0, 2, -1, 3, -1, 0, -1] +advanced 1.0 0.63246 [1, 0, 1, 1, 1, 0, 1, 2, 1, 2] +advantage 1.0 0.63246 [1, 2, 1, 1, 2, 0, 1, 0, 1, 1] +advantaged 1.4 0.91652 [1, 0, 3, 0, 1, 1, 2, 2, 2, 2] +advantageous 1.5 0.67082 [2, 0, 2, 2, 2, 1, 1, 1, 2, 2] +advantageously 1.9 0.53852 [2, 2, 2, 3, 2, 2, 2, 1, 1, 2] +advantageousness 1.6 1.28062 [-2, 2, 3, 1, 2, 2, 2, 2, 2, 2] +advantages 1.5 0.80623 [1, 0, 3, 1, 1, 1, 2, 2, 2, 2] +advantaging 1.6 0.66332 [3, 1, 1, 2, 1, 1, 2, 2, 2, 1] +adventure 1.3 0.45826 [1, 2, 1, 1, 2, 1, 1, 1, 1, 2] +adventured 1.3 0.45826 [1, 2, 1, 2, 1, 2, 1, 1, 1, 1] +adventurer 1.2 0.6 [1, 2, 0, 2, 1, 2, 1, 1, 1, 1] +adventurers 0.9 0.9434 [0, 1, 0, 1, 0, 1, 0, 1, 3, 2] +adventures 1.4 1.2 [2, 2, 1, 2, -2, 2, 2, 1, 2, 2] +adventuresome 1.7 1.1 [0, 3, 0, 1, 2, 2, 3, 1, 2, 3] +adventuresomeness 1.3 1.00499 [1, 0, 0, 2, 3, 2, 2, 0, 1, 2] +adventuress 0.8 1.72047 [3, -1, 2, 2, 0, 0, 1, 2, -3, 2] +adventuresses 1.4 1.11355 [1, 0, 0, 3, 2, 2, 3, 0, 1, 2] +adventuring 2.3 0.78102 [2, 3, 2, 3, 1, 3, 3, 1, 2, 3] +adventurism 1.5 0.67082 [1, 0, 2, 2, 2, 2, 1, 1, 2, 2] +adventurist 1.4 0.4899 [1, 1, 2, 1, 2, 2, 2, 1, 1, 1] +adventuristic 1.7 0.64031 [2, 1, 1, 2, 2, 1, 3, 2, 2, 1] +adventurists 1.2 0.9798 [3, 1, 0, 0, 1, 2, 1, 0, 2, 2] +adventurous 1.4 1.11355 [0, 1, 2, 1, 2, 0, 3, 2, 3, 0] +adventurously 1.3 0.9 [0, 1, 2, 2, 1, 2, 1, 1, 0, 3] +adventurousness 1.8 0.87178 [0, 1, 3, 2, 1, 3, 2, 2, 2, 2] +adversarial -1.5 0.92195 [-2, 0, -1, -3, -2, -2, 0, -1, -2, -2] +adversaries -1.0 0.63246 [-1, -1, -1, -1, 0, -2, -2, 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0.83066 [-3, -1, -2, -1, -2, -1, -3, -3, -2, -3] +infuriate -2.2 0.87178 [-2, -1, -3, -3, -2, -3, -3, -1, -1, -3] +infuriated -3.0 0.7746 [-1, -4, -3, -3, -3, -3, -3, -3, -3, -4] +infuriates -2.6 0.8 [-4, -2, -1, -3, -3, -3, -2, -3, -3, -2] +infuriating -2.4 1.42829 [-1, -3, -3, -3, -3, -4, 0, 0, -4, -3] +inhibin -0.2 0.4 [-1, 0, 0, 0, 0, 0, 0, 0, -1, 0] +inhibit -1.6 0.4899 [-1, -2, -1, -2, -1, -2, -2, -2, -1, -2] +inhibited -0.4 0.4899 [0, 0, -1, 0, -1, -1, 0, -1, 0, 0] +inhibiting -0.4 1.42829 [1, -1, -1, -1, -2, -1, 2, -2, 2, -1] +inhibition -1.5 0.67082 [-1, -1, -1, -1, -2, -1, -1, -2, -2, -3] +inhibitions -0.8 0.74833 [-2, -1, -2, 0, 0, 0, 0, -1, -1, -1] +inhibitive -1.4 0.4899 [-2, -2, -1, -1, -2, -1, -2, -1, -1, -1] +inhibitor -0.3 1.00499 [-2, 1, 1, -1, 1, 0, -1, -1, 0, -1] +inhibitors -1.0 0.7746 [-1, 0, -2, -1, -2, -2, 0, 0, -1, -1] +inhibitory -1.0 0.7746 [0, -1, -2, -2, -1, -2, 0, 0, -1, -1] +inhibits -0.9 0.53852 [-1, -1, -2, -1, -1, 0, -1, -1, -1, 0] +injured -1.7 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4, 2, 2] +intellectuality 1.7 1.1 [3, 2, 2, 1, 2, 1, 0, 3, 0, 3] +intellectualization 1.5 1.11803 [2, 1, 2, 1, 4, 2, 0, 0, 2, 1] +intellectualize 1.5 0.92195 [1, 2, 1, 1, 3, 2, 3, 0, 1, 1] +intellectualized 1.2 0.74833 [1, 0, 1, 1, 2, 0, 1, 2, 2, 2] +intellectualizes 1.8 0.87178 [2, 3, 2, 0, 2, 3, 2, 2, 1, 1] +intellectualizing 0.8 1.77764 [0, 1, 2, 1, 0, 2, 2, -4, 2, 2] +intellectually 1.4 0.8 [2, 0, 0, 1, 2, 2, 2, 2, 2, 1] +intellectualness 1.5 0.80623 [2, 2, 2, 2, 0, 0, 1, 2, 2, 2] +intellectuals 1.6 0.8 [0, 1, 2, 1, 3, 2, 1, 2, 2, 2] +intelligence 2.1 0.9434 [3, 2, 2, 1, 3, 3, 3, 2, 2, 0] +intelligencer 1.5 0.80623 [2, 0, 0, 2, 2, 2, 2, 1, 2, 2] +intelligencers 1.6 0.91652 [2, 2, 0, 2, 2, 0, 3, 2, 2, 1] +intelligences 1.6 0.91652 [3, 0, 0, 2, 2, 2, 2, 1, 2, 2] +intelligent 2.0 0.7746 [1, 2, 2, 1, 4, 2, 2, 2, 2, 2] +intelligential 1.9 0.9434 [3, 2, 2, 1, 3, 2, 2, 3, 0, 1] +intelligently 2.0 0.63246 [2, 3, 2, 3, 1, 2, 2, 1, 2, 2] +intelligentsia 1.5 1.20416 [0, 1, 2, 0, 4, 3, 2, 1, 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1, 1] +promisers 1.6 0.4899 [2, 1, 2, 1, 2, 1, 2, 1, 2, 2] +promises 1.6 0.8 [2, 1, 1, 0, 2, 1, 3, 2, 2, 2] +promising 1.7 0.45826 [1, 2, 2, 1, 2, 2, 1, 2, 2, 2] +promisingly 1.2 0.6 [2, 2, 1, 1, 1, 2, 1, 0, 1, 1] +promisor 1.0 0.63246 [2, 2, 0, 1, 1, 1, 0, 1, 1, 1] +promisors 0.4 0.8 [0, 0, 0, 2, 0, 2, 0, 0, 0, 0] +promissory 0.9 1.13578 [2, 0, 0, 3, 0, 2, 2, 0, 0, 0] +promote 1.6 0.8 [2, 1, 2, 3, 0, 1, 2, 2, 1, 2] +promoted 1.8 0.74833 [2, 2, 1, 1, 1, 2, 2, 1, 3, 3] +promotes 1.4 0.91652 [1, 2, 0, 0, 1, 2, 2, 1, 3, 2] +promoting 1.5 0.67082 [1, 2, 2, 1, 2, 1, 2, 0, 2, 2] +propaganda -1.0 1.54919 [-2, -3, -2, -3, -2, 1, -1, 1, 0, 1] +prosecute -1.7 1.00499 [-2, -2, -2, -1, -1, -2, 0, -1, -4, -2] +prosecuted -1.6 1.95959 [-2, -2, -2, -3, -3, -3, -4, 2, 2, -1] +prosecutes -1.8 1.53623 [-2, -2, -2, -3, -2, -2, -4, 1, 1, -3] +prosecution -2.2 1.07703 [-4, -1, -2, -2, -3, -2, -1, -4, -1, -2] +prospect 1.2 0.87178 [1, 3, 1, 0, 2, 2, 1, 0, 1, 1] +prospects 1.2 0.6 [2, 1, 1, 1, 1, 0, 1, 2, 2, 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1.80278 [-2, -2, -1, -1, 3, -1, 1, -3, 2, -1] +rebellions -1.1 1.57797 [-2, -4, -3, 0, 0, -1, 1, -2, -1, 1] +rebellious -1.2 1.249 [-2, -1, -3, -1, -2, -2, 1, -1, 1, -2] +rebelliously -1.8 0.87178 [-3, 0, -2, -2, -1, -2, -3, -1, -2, -2] +rebelliousness -1.2 1.16619 [0, -3, -1, -2, 1, -1, -2, 0, -2, -2] +rebels -0.8 1.07703 [-1, 0, 0, -2, 0, -3, 0, 0, 0, -2] +recession -1.8 1.07703 [-3, -1, -4, -2, -1, -1, -1, -1, -3, -1] +reckless -1.7 0.64031 [-2, -1, -1, -3, -1, -2, -1, -2, -2, -2] +recommend 1.5 0.67082 [1, 1, 1, 1, 2, 3, 2, 2, 1, 1] +recommended 0.8 1.07703 [1, 1, 0, -2, 1, 2, 1, 2, 1, 1] +recommends 0.9 0.9434 [1, 1, 2, 0, 0, 2, 1, -1, 2, 1] +redeemed 1.3 0.9 [2, 1, 2, 2, 1, -1, 2, 1, 2, 1] +reek -2.4 0.66332 [-3, -3, -2, -3, -3, -2, -2, -2, -1, -3] +reeked -2.0 1.09545 [-4, -3, -2, -3, -1, -2, 0, -1, -2, -2] +reeker -1.7 1.1 [0, -2, -1, 0, -3, -3, -2, -1, -3, -2] +reekers -1.5 1.0247 [-3, 0, 0, 0, -2, -2, -2, -2, -2, -2] +reeking -2.0 1.48324 [2, -2, -2, -3, -3, -3, -1, -3, -3, -2] +refuse -1.2 0.4 [-1, -1, -1, -1, -1, -1, -1, -2, -2, -1] +refused -1.2 0.74833 [0, -1, -1, -1, -1, -1, -2, -1, -3, -1] +refusing -1.7 0.64031 [-1, -1, -1, -2, -2, -2, -3, -1, -2, -2] +regret -1.8 0.6 [-2, -2, -2, -2, -1, -3, -1, -1, -2, -2] +regretful -1.9 0.83066 [-1, -2, -1, -2, -1, -2, -1, -3, -3, -3] +regretfully -1.9 0.83066 [-1, -1, -1, -3, -2, -3, -1, -3, -2, -2] +regretfulness -1.6 0.66332 [-1, -3, -1, -1, -1, -2, -1, -2, -2, -2] +regrets -1.5 0.5 [-2, -2, -2, -1, -2, -1, -1, -2, -1, -1] +regrettable -2.3 0.78102 [-3, -1, -2, -1, -3, -2, -3, -3, -2, -3] +regrettably -2.0 0.63246 [-2, -3, -1, -3, -2, -1, -2, -2, -2, -2] +regretted -1.6 0.4899 [-2, -1, -2, -2, -2, -2, -1, -1, -1, -2] +regretter -1.6 0.66332 [-2, -1, -2, -2, -3, -2, -1, -1, -1, -1] +regretters -2.0 0.89443 [-1, -2, -2, -2, -4, -3, -1, -1, -2, -2] +regretting -1.7 0.78102 [-3, -2, -2, -1, -3, -1, -1, -1, -2, -1] +reinvigorate 2.3 0.78102 [3, 3, 3, 2, 3, 1, 2, 1, 2, 3] +reinvigorated 1.9 1.13578 [2, 2, 3, 1, 2, 3, 3, -1, 2, 2] +reinvigorates 1.8 0.9798 [2, 2, 3, 0, 2, 2, 3, 0, 2, 2] +reinvigorating 1.7 0.64031 [1, 2, 1, 1, 2, 2, 2, 1, 3, 2] +reinvigoration 2.2 0.4 [2, 2, 2, 3, 2, 3, 2, 2, 2, 2] +reject -1.7 0.64031 [-1, -2, -2, -2, -1, -3, -1, -1, -2, -2] +rejected -2.3 0.45826 [-3, -2, -3, -3, -2, -2, -2, -2, -2, -2] +rejectee -2.3 0.45826 [-3, -2, -2, -3, -2, -3, -2, -2, -2, -2] +rejectees -1.8 0.4 [-2, -2, -1, -2, -2, -2, -2, -2, -2, -1] +rejecter -1.6 0.66332 [-2, -1, -1, -3, -2, -2, -1, -1, -1, -2] +rejecters -1.8 0.6 [-2, -3, -1, -1, -2, -1, -2, -2, -2, -2] +rejecting -2.0 0.7746 [-1, -2, -3, -1, -2, -3, -2, -1, -3, -2] +rejectingly -1.7 0.64031 [-1, -2, -2, -2, -1, -2, -2, -1, -3, -1] +rejection -2.5 0.67082 [-3, -3, -2, -4, -3, -2, -2, -2, -2, -2] +rejections -2.1 0.53852 [-3, -2, -2, -2, -1, -2, -3, -2, -2, -2] +rejective -1.8 0.6 [-3, -2, -2, -2, -1, -1, -2, -2, -1, -2] +rejector -1.8 0.74833 [-2, -1, -2, -3, -1, -2, -1, -2, -3, -1] +rejects -2.2 0.4 [-3, -2, -2, -2, -2, -3, -2, -2, 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[3, 3, 1, 4, 4, 2, 1, 1, 2, 2] +woot 1.8 1.07703 [2, 0, 2, 2, 2, 2, 0, 4, 2, 2] +worn -1.2 0.4 [-1, -1, -1, -1, -1, -1, -2, -1, -2, -1] +worried -1.2 0.74833 [-1, -1, -1, -1, -1, -2, -3, 0, -1, -1] +worriedly -2.0 0.44721 [-2, -2, -3, -2, -2, -2, -2, -1, -2, -2] +worrier -1.8 0.6 [-2, -2, -1, -2, -1, -3, -2, -2, -1, -2] +worriers -1.7 0.45826 [-2, -1, -2, -2, -2, -2, -1, -2, -1, -2] +worries -1.8 0.6 [-2, -2, -1, -2, -1, -2, -2, -3, -1, -2] +worriment -1.5 0.67082 [-1, -2, -1, -1, -1, -2, -1, -3, -1, -2] +worriments -1.9 0.7 [-2, -1, -2, -3, -1, -2, -3, -1, -2, -2] +worrisome -1.7 0.64031 [-1, -1, -1, -2, -1, -2, -3, -2, -2, -2] +worrisomely -2.0 0.63246 [-1, -2, -1, -2, -2, -3, -2, -2, -3, -2] +worrisomeness -1.9 0.53852 [-2, -2, -3, -1, -2, -2, -2, -1, -2, -2] +worrit -2.1 0.53852 [-2, -2, -1, -2, -2, -3, -3, -2, -2, -2] +worrits -1.2 0.9798 [-1, -2, -2, -1, 0, 0, -1, -3, 0, -2] +worry -1.9 0.7 [-2, -3, -1, -3, -1, -2, -1, -2, -2, -2] +worrying -1.4 0.66332 [-2, -1, -2, -2, -1, 0, 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-1, -1, -1, -1, -1, -1, -1, -3] +|-: -0.8 0.74833 [-1, -2, 0, -1, 0, -2, -1, -1, 0, 0] +|-:> -1.6 0.4899 [-1, -2, -2, -2, -2, -1, -1, -2, -2, -1] +|-o -1.2 0.9798 [-1, 0, -1, -1, -1, -1, -1, -4, -1, -1] +|: -0.5 1.68819 [2, -3, -1, 0, -1, -1, -1, -2, -1, 3] +|;-) 2.2 1.32665 [4, 1, 1, 1, 3, 2, 4, 1, 4, 1] +|= -0.4 1.56205 [2, -2, -1, 0, -1, -1, -1, -2, -1, 3] +|^: -1.1 0.7 [-2, 0, -1, -1, 0, -1, -1, -2, -2, -1] +|o: -0.9 0.53852 [-1, 0, -1, -2, -1, 0, -1, -1, -1, -1] +||-: -2.3 0.45826 [-2, -2, -2, -3, -3, -3, -2, -2, -2, -2] +}: -2.1 0.83066 [-1, -1, -3, -2, -3, -2, -2, -1, -3, -3] +}:( -2.0 0.63246 [-3, -1, -2, -1, -3, -2, -2, -2, -2, -2] +}:) 0.4 1.42829 [1, 1, -2, 1, 2, -2, 1, -1, 2, 1] +}:-( -2.1 0.7 [-2, -1, -2, -2, -2, -4, -2, -2, -2, -2] +}:-) 0.3 1.61555 [1, 1, -2, 1, -1, -3, 2, 2, 1, 1] \ No newline at end of file