# coding: utf-8
# Natural Language Toolkit: vader
#
# Copyright (C) 2001-2017 NLTK Project
# Author: C.J. Hutto <Clayton.Hutto@gtri.gatech.edu>
#         Ewan Klein <ewan@inf.ed.ac.uk> (modifications)
#         Pierpaolo Pantone <24alsecondo@gmail.com> (modifications)
#         George Berry <geb97@cornell.edu> (modifications)
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
#
# Modifications to the original VADER code have been made in order to
# integrate it into NLTK. These have involved changes to
# ensure Python 3 compatibility, and refactoring to achieve greater modularity.

"""
If you use the VADER sentiment analysis tools, please cite:

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 codecs
import math
import re
import string
from itertools import product
import nltk.data

##Constants##

# (empirically derived mean sentiment intensity rating increase for booster words)
B_INCR = 0.293
B_DECR = -0.293

# (empirically derived mean sentiment intensity rating increase for using
# ALLCAPs to emphasize a word)
C_INCR = 0.733

N_SCALAR = -0.74

# for removing punctuation
REGEX_REMOVE_PUNCTUATION = re.compile('[{0}]'.format(re.escape(string.punctuation)))

PUNC_LIST = [".", "!", "?", ",", ";", ":", "-", "'", "\"",
             "!!", "!!!", "??", "???", "?!?", "!?!", "?!?!", "!?!?"]
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"]

# 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}

# 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}


##Static methods##

def negated(input_words, include_nt=True):
    """
    Determine if input contains negation words
    """
    neg_words = []
    neg_words.extend(NEGATE)
    for word in neg_words:
        if word in input_words:
            return True
    if include_nt:
        for word in input_words:
            if "n't" in word:
                return True
    if "least" in input_words:
        i = input_words.index("least")
        if i > 0 and input_words[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
    """
    norm_score = score/math.sqrt((score*score) + alpha)
    return norm_score


def allcap_differential(words):
    """
    Check whether just some words in the input are ALL CAPS

    :param list words: The words to inspect
    :returns: `True` if some but not all items in `words` are ALL CAPS
    """
    is_different = False
    allcap_words = 0
    for word in words:
        if word.isupper():
            allcap_words += 1
    cap_differential = len(words) - allcap_words
    if cap_differential > 0 and cap_differential < len(words):
        is_different = True
    return is_different


def scalar_inc_dec(word, valence, is_cap_diff):
    """
    Check if the preceding words increase, decrease, or negate/nullify the
    valence
    """
    scalar = 0.0
    word_lower = 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 word.isupper() and is_cap_diff:
            if valence > 0:
                scalar += C_INCR
            else: scalar -= C_INCR
    return scalar

class SentiText(object):
    """
    Identify sentiment-relevant string-level properties of input text.
    """
    def __init__(self, text):
        if not isinstance(text, str):
            text = str(text.encode('utf-8'))
        self.text = text
        self.words_and_emoticons = self._words_and_emoticons()
        # doesn't separate words from\
        # adjacent punctuation (keeps emoticons & contractions)
        self.is_cap_diff = allcap_differential(self.words_and_emoticons)

    def _words_plus_punc(self):
        """
        Returns mapping of form:
        {
            'cat,': 'cat',
            ',cat': 'cat',
        }
        """
        no_punc_text = REGEX_REMOVE_PUNCTUATION.sub('', self.text)
        # removes punctuation (but loses emoticons & contractions)
        words_only = no_punc_text.split()
        # remove singletons
        words_only = set( w for w in words_only if len(w) > 1 )
        # the product gives ('cat', ',') and (',', 'cat')
        punc_before = {''.join(p): p[1] for p in product(PUNC_LIST, words_only)}
        punc_after = {''.join(p): p[0] for p in product(words_only, PUNC_LIST)}
        words_punc_dict = punc_before
        words_punc_dict.update(punc_after)
        return words_punc_dict

    def _words_and_emoticons(self):
        """
        Removes leading and trailing puncutation
        Leaves contractions and most emoticons
            Does not preserve punc-plus-letter emoticons (e.g. :D)
        """
        wes = self.text.split()
        words_punc_dict = self._words_plus_punc()
        wes = [we for we in wes if len(we) > 1]
        for i, we in enumerate(wes):
            if we in words_punc_dict:
                wes[i] = words_punc_dict[we]
        return wes

class SentimentIntensityAnalyzer(object):
    """
    Give a sentiment intensity score to sentences.
    """
    def __init__(self, lexicon_file="sentiment/vader_lexicon.zip/vader_lexicon/vader_lexicon.txt"):
        self.lexicon_file = nltk.data.load(lexicon_file)
        self.lexicon = self.make_lex_dict()

    def make_lex_dict(self):
        """
        Convert lexicon file to a dictionary
        """
        lex_dict = {}
        for line in self.lexicon_file.split('\n'):
            (word, measure) = line.strip().split('\t')[0:2]
            lex_dict[word] = float(measure)
        return lex_dict

    def polarity_scores(self, text):
        """
        Return a float for sentiment strength based on the input text.
        Positive values are positive valence, negative value are negative
        valence.
        """
        sentitext = SentiText(text)
        #text, words_and_emoticons, is_cap_diff = self.preprocess(text)

        sentiments = []
        words_and_emoticons = sentitext.words_and_emoticons
        for item in words_and_emoticons:
            valence = 0
            i = words_and_emoticons.index(item)
            if (i < len(words_and_emoticons) - 1 and item.lower() == "kind" and \
                words_and_emoticons[i+1].lower() == "of") or \
                item.lower() in BOOSTER_DICT:
                sentiments.append(valence)
                continue

            sentiments = self.sentiment_valence(valence, sentitext, item, i, sentiments)

        sentiments = self._but_check(words_and_emoticons, sentiments)

        return self.score_valence(sentiments, text)

    def sentiment_valence(self, valence, sentitext, item, i, sentiments):
        is_cap_diff = sentitext.is_cap_diff
        words_and_emoticons = sentitext.words_and_emoticons
        item_lowercase = item.lower()
        if item_lowercase in self.lexicon:
            #get the sentiment valence
            valence = self.lexicon[item_lowercase]

            #check if sentiment laden word is in ALL CAPS (while others aren't)
            if item.isupper() and is_cap_diff:
                if valence > 0:
                    valence += C_INCR
                else:
                    valence -= C_INCR

            for start_i in range(0,3):
                if i > start_i and words_and_emoticons[i-(start_i+1)].lower() not in self.lexicon:
                    # dampen the scalar modifier of preceding words and emoticons
                    # (excluding the ones that immediately preceed the item) based
                    # on their distance from the current item.
                    s = scalar_inc_dec(words_and_emoticons[i-(start_i+1)], valence, is_cap_diff)
                    if start_i == 1 and s != 0:
                        s = s*0.95
                    if start_i == 2 and s != 0:
                        s = s*0.9
                    valence = valence+s
                    valence = self._never_check(valence, words_and_emoticons, start_i, i)
                    if start_i == 2:
                        valence = self._idioms_check(valence, words_and_emoticons, i)

                        # 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}

            valence = self._least_check(valence, words_and_emoticons, i)

        sentiments.append(valence)
        return sentiments

    def _least_check(self, valence, words_and_emoticons, i):
        # check for negation case using "least"
        if i > 1 and words_and_emoticons[i-1].lower() not in self.lexicon \
           and words_and_emoticons[i-1].lower() == "least":
            if words_and_emoticons[i-2].lower() != "at" and words_and_emoticons[i-2].lower() != "very":
                valence = valence*N_SCALAR
        elif i > 0 and words_and_emoticons[i-1].lower() not in self.lexicon \
             and words_and_emoticons[i-1].lower() == "least":
            valence = valence*N_SCALAR
        return valence

    def _but_check(self, words_and_emoticons, sentiments):
        # check for modification in sentiment due to contrastive conjunction 'but'
        if 'but' in words_and_emoticons or 'BUT' in words_and_emoticons:
            try:
                bi = words_and_emoticons.index('but')
            except ValueError:
                bi = words_and_emoticons.index('BUT')
            for sentiment in sentiments:
                si = sentiments.index(sentiment)
                if si < bi:
                    sentiments.pop(si)
                    sentiments.insert(si, sentiment*0.5)
                elif si > bi:
                    sentiments.pop(si)
                    sentiments.insert(si, sentiment*1.5)
        return sentiments

    def _idioms_check(self, valence, words_and_emoticons, i):
        onezero = "{0} {1}".format(words_and_emoticons[i-1], words_and_emoticons[i])

        twoonezero = "{0} {1} {2}".format(words_and_emoticons[i-2],
                                       words_and_emoticons[i-1], words_and_emoticons[i])

        twoone = "{0} {1}".format(words_and_emoticons[i-2], words_and_emoticons[i-1])

        threetwoone = "{0} {1} {2}".format(words_and_emoticons[i-3],
                                        words_and_emoticons[i-2], words_and_emoticons[i-1])

        threetwo = "{0} {1}".format(words_and_emoticons[i-3], words_and_emoticons[i-2])

        sequences = [onezero, twoonezero, twoone, threetwoone, threetwo]

        for seq in sequences:
            if seq in SPECIAL_CASE_IDIOMS:
                valence = SPECIAL_CASE_IDIOMS[seq]
                break

        if len(words_and_emoticons)-1 > i:
            zeroone = "{0} {1}".format(words_and_emoticons[i], words_and_emoticons[i+1])
            if zeroone in SPECIAL_CASE_IDIOMS:
                valence = SPECIAL_CASE_IDIOMS[zeroone]
        if len(words_and_emoticons)-1 > i+1:
            zeroonetwo = "{0} {1} {2}".format(words_and_emoticons[i], words_and_emoticons[i+1], words_and_emoticons[i+2])
            if zeroonetwo in SPECIAL_CASE_IDIOMS:
                valence = 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:
            valence = valence+B_DECR
        return valence

    def _never_check(self, valence, words_and_emoticons, start_i, i):
        if start_i == 0:
            if negated([words_and_emoticons[i-1]]):
                    valence = valence*N_SCALAR
        if start_i == 1:
            if words_and_emoticons[i-2] == "never" and\
               (words_and_emoticons[i-1] == "so" or
                words_and_emoticons[i-1] == "this"):
                valence = valence*1.5
            elif negated([words_and_emoticons[i-(start_i+1)]]):
                valence = valence*N_SCALAR
        if start_i == 2:
            if words_and_emoticons[i-3] == "never" and \
               (words_and_emoticons[i-2] == "so" or words_and_emoticons[i-2] == "this") or \
               (words_and_emoticons[i-1] == "so" or words_and_emoticons[i-1] == "this"):
                valence = valence*1.25
            elif negated([words_and_emoticons[i-(start_i+1)]]):
                valence = valence*N_SCALAR
        return valence

    def _punctuation_emphasis(self, sum_s, text):
        # add emphasis from exclamation points and question marks
        ep_amplifier = self._amplify_ep(text)
        qm_amplifier = self._amplify_qm(text)
        punct_emph_amplifier = ep_amplifier+qm_amplifier
        return punct_emph_amplifier

    def _amplify_ep(self, text):
        # check for added emphasis resulting from exclamation points (up to 4 of them)
        ep_count = text.count("!")
        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

    def _amplify_qm(self, text):
        # check for added emphasis resulting from question marks (2 or 3+)
        qm_count = text.count("?")
        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

    def _sift_sentiment_scores(self, sentiments):
        # 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
        return pos_sum, neg_sum, neu_count

    def score_valence(self, sentiments, text):
        if sentiments:
            sum_s = float(sum(sentiments))
            # compute and add emphasis from punctuation in text
            punct_emph_amplifier = self._punctuation_emphasis(sum_s, text)
            if sum_s > 0:
                sum_s += punct_emph_amplifier
            elif  sum_s < 0:
                sum_s -= punct_emph_amplifier

            compound = normalize(sum_s)
            # discriminate between positive, negative and neutral sentiment scores
            pos_sum, neg_sum, neu_count = self._sift_sentiment_scores(sentiments)

            if pos_sum > math.fabs(neg_sum):
                pos_sum += (punct_emph_amplifier)
            elif pos_sum < math.fabs(neg_sum):
                neg_sum -= (punct_emph_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

        sentiment_dict = \
            {"neg" : round(neg, 3),
             "neu" : round(neu, 3),
             "pos" : round(pos, 3),
             "compound" : round(compound, 4)}

        return sentiment_dict
