# Natural Language Toolkit: vader
#
# Copyright (C) 2001-2025 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)
#         Malavika Suresh <malavika.suresh0794@gmail.com> (modifications)
# URL: <https://www.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 math
import re
import string
from itertools import product

import nltk.data
from nltk.util import pairwise


class VaderConstants:
    """
    A class to keep the Vader lists and constants.
    """

    ##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

    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'
    # https://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,
    }

    # for removing punctuation
    REGEX_REMOVE_PUNCTUATION = re.compile(f"[{re.escape(string.punctuation)}]")

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

    def __init__(self):
        pass

    def negated(self, input_words, include_nt=True):
        """
        Determine if input contains negation words
        """
        neg_words = self.NEGATE
        if any(word.lower() in neg_words for word in input_words):
            return True
        if include_nt:
            if any("n't" in word.lower() for word in input_words):
                return True
        for first, second in pairwise(input_words):
            if second.lower() == "least" and first.lower() != "at":
                return True
        return False

    def normalize(self, 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 scalar_inc_dec(self, 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 self.BOOSTER_DICT:
            scalar = self.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 += self.C_INCR
                else:
                    scalar -= self.C_INCR
        return scalar


class SentiText:
    """
    Identify sentiment-relevant string-level properties of input text.
    """

    def __init__(self, text, punc_list, regex_remove_punctuation):
        if not isinstance(text, str):
            text = str(text.encode("utf-8"))
        self.text = text
        self.PUNC_LIST = punc_list
        self.REGEX_REMOVE_PUNCTUATION = regex_remove_punctuation
        self.words_and_emoticons = self._words_and_emoticons()
        # doesn't separate words from
        # adjacent punctuation (keeps emoticons & contractions)
        self.is_cap_diff = self.allcap_differential(self.words_and_emoticons)

    def _words_plus_punc(self):
        """
        Returns mapping of form:
        {
            'cat,': 'cat',
            ',cat': 'cat',
        }
        """
        no_punc_text = self.REGEX_REMOVE_PUNCTUATION.sub("", self.text)
        # removes punctuation (but loses emoticons & contractions)
        words_only = no_punc_text.split()
        # remove singletons
        words_only = {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(self.PUNC_LIST, words_only)}
        punc_after = {"".join(p): p[0] for p in product(words_only, self.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

    def allcap_differential(self, 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 0 < cap_differential < len(words):
            is_different = True
        return is_different


class SentimentIntensityAnalyzer:
    """
    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()
        self.constants = VaderConstants()

    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.

        :note: Hashtags are not taken into consideration (e.g. #BAD is neutral). If you
            are interested in processing the text in the hashtags too, then we recommend
            preprocessing your data to remove the #, after which the hashtag text may be
            matched as if it was a normal word in the sentence.
        """
        # text, words_and_emoticons, is_cap_diff = self.preprocess(text)
        sentitext = SentiText(
            text, self.constants.PUNC_LIST, self.constants.REGEX_REMOVE_PUNCTUATION
        )
        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 self.constants.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 += self.constants.C_INCR
                else:
                    valence -= self.constants.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 = self.constants.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 * self.constants.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 * self.constants.N_SCALAR
        return valence

    def _but_check(self, words_and_emoticons, sentiments):
        words_and_emoticons = [w_e.lower() for w_e in words_and_emoticons]
        but = {"but"} & set(words_and_emoticons)
        if but:
            bi = words_and_emoticons.index(next(iter(but)))
            for sidx, sentiment in enumerate(sentiments):
                if sidx < bi:
                    sentiments[sidx] = sentiment * 0.5
                elif sidx > bi:
                    sentiments[sidx] = sentiment * 1.5
        return sentiments

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

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

        twoone = f"{words_and_emoticons[i - 2]} {words_and_emoticons[i - 1]}"

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

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

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

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

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

        # check for booster/dampener bi-grams such as 'sort of' or 'kind of'
        if (
            threetwo in self.constants.BOOSTER_DICT
            or twoone in self.constants.BOOSTER_DICT
        ):
            valence = valence + self.constants.B_DECR
        return valence

    def _never_check(self, valence, words_and_emoticons, start_i, i):
        if start_i == 0:
            if self.constants.negated([words_and_emoticons[i - 1]]):
                valence = valence * self.constants.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 self.constants.negated([words_and_emoticons[i - (start_i + 1)]]):
                valence = valence * self.constants.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 self.constants.negated([words_and_emoticons[i - (start_i + 1)]]):
                valence = valence * self.constants.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 = self.constants.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
