pipeline/nltk/sentiment/util.py

#
# Natural Language Toolkit: Sentiment Analyzer
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Pierpaolo Pantone <[email protected]>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT

"""
Utility methods for Sentiment Analysis.
"""

import codecs
import csv
import json
import pickle
import random
import re
import sys
import time
from copy import deepcopy

import nltk
from nltk.corpus import CategorizedPlaintextCorpusReader
from nltk.data import load
from nltk.tokenize.casual import EMOTICON_RE

# ////////////////////////////////////////////////////////////
# { Regular expressions
# ////////////////////////////////////////////////////////////

# Regular expression for negation by Christopher Potts
NEGATION = r"""
    (?:
        ^(?:never|no|nothing|nowhere|noone|none|not|
            havent|hasnt|hadnt|cant|couldnt|shouldnt|
            wont|wouldnt|dont|doesnt|didnt|isnt|arent|aint
        )$
    )
    |
    n't"""

NEGATION_RE = re.compile(NEGATION, re.VERBOSE)

CLAUSE_PUNCT = r"^[.:;!?]$"
CLAUSE_PUNCT_RE = re.compile(CLAUSE_PUNCT)

# Happy and sad emoticons

HAPPY = {
    ":-)",
    ":)",
    ";)",
    ":o)",
    ":]",
    ":3",
    ":c)",
    ":>",
    "=]",
    "8)",
    "=)",
    ":}",
    ":^)",
    ":-D",
    ":D",
    "8-D",
    "8D",
    "x-D",
    "xD",
    "X-D",
    "XD",
    "=-D",
    "=D",
    "=-3",
    "=3",
    ":-))",
    ":'-)",
    ":')",
    ":*",
    ":^*",
    ">:P",
    ":-P",
    ":P",
    "X-P",
    "x-p",
    "xp",
    "XP",
    ":-p",
    ":p",
    "=p",
    ":-b",
    ":b",
    ">:)",
    ">;)",
    ">:-)",
    "<3",
}

SAD = {
    ":L",
    ":-/",
    ">:/",
    ":S",
    ">:[",
    ":@",
    ":-(",
    ":[",
    ":-||",
    "=L",
    ":<",
    ":-[",
    ":-<",
    "=\\",
    "=/",
    ">:(",
    ":(",
    ">.<",
    ":'-(",
    ":'(",
    ":\\",
    ":-c",
    ":c",
    ":{",
    ">:\\",
    ";(",
}


def timer(method):
    """
    A timer decorator to measure execution performance of methods.
    """

    def timed(*args, **kw):
        start = time.time()
        result = method(*args, **kw)
        end = time.time()
        tot_time = end - start
        hours = tot_time // 3600
        mins = tot_time // 60 % 60
        # in Python 2.x round() will return a float, so we convert it to int
        secs = int(round(tot_time % 60))
        if hours == 0 and mins == 0 and secs < 10:
            print(f"[TIMER] {method.__name__}(): {method.__name__:.3f} seconds")
        else:
            print(f"[TIMER] {method.__name__}(): {hours}h {mins}m {secs}s")
        return result

    return timed


# ////////////////////////////////////////////////////////////
# { Feature extractor functions
# ////////////////////////////////////////////////////////////
"""
Feature extractor functions are declared outside the SentimentAnalyzer class.
Users should have the possibility to create their own feature extractors
without modifying SentimentAnalyzer.
"""


def extract_unigram_feats(document, unigrams, handle_negation=False):
    """
    Populate a dictionary of unigram features, reflecting the presence/absence in
    the document of each of the tokens in `unigrams`.

    :param document: a list of words/tokens.
    :param unigrams: a list of words/tokens whose presence/absence has to be
        checked in `document`.
    :param handle_negation: if `handle_negation == True` apply `mark_negation`
        method to `document` before checking for unigram presence/absence.
    :return: a dictionary of unigram features {unigram : boolean}.

    >>> words = ['ice', 'police', 'riot']
    >>> document = 'ice is melting due to global warming'.split()
    >>> sorted(extract_unigram_feats(document, words).items())
    [('contains(ice)', True), ('contains(police)', False), ('contains(riot)', False)]
    """
    features = {}
    if handle_negation:
        document = mark_negation(document)
    for word in unigrams:
        features[f"contains({word})"] = word in set(document)
    return features


def extract_bigram_feats(document, bigrams):
    """
    Populate a dictionary of bigram features, reflecting the presence/absence in
    the document of each of the tokens in `bigrams`. This extractor function only
    considers contiguous bigrams obtained by `nltk.bigrams`.

    :param document: a list of words/tokens.
    :param unigrams: a list of bigrams whose presence/absence has to be
        checked in `document`.
    :return: a dictionary of bigram features {bigram : boolean}.

    >>> bigrams = [('global', 'warming'), ('police', 'prevented'), ('love', 'you')]
    >>> document = 'ice is melting due to global warming'.split()
    >>> sorted(extract_bigram_feats(document, bigrams).items()) # doctest: +NORMALIZE_WHITESPACE
    [('contains(global - warming)', True), ('contains(love - you)', False),
    ('contains(police - prevented)', False)]
    """
    features = {}
    for bigr in bigrams:
        features[f"contains({bigr[0]} - {bigr[1]})"] = bigr in nltk.bigrams(document)
    return features


# ////////////////////////////////////////////////////////////
# { Helper Functions
# ////////////////////////////////////////////////////////////


def mark_negation(document, double_neg_flip=False, shallow=False):
    """
    Append _NEG suffix to words that appear in the scope between a negation
    and a punctuation mark.

    :param document: a list of words/tokens, or a tuple (words, label).
    :param shallow: if True, the method will modify the original document in place.
    :param double_neg_flip: if True, double negation is considered affirmation
        (we activate/deactivate negation scope every time we find a negation).
    :return: if `shallow == True` the method will modify the original document
        and return it. If `shallow == False` the method will return a modified
        document, leaving the original unmodified.

    >>> sent = "I didn't like this movie . It was bad .".split()
    >>> mark_negation(sent)
    ['I', "didn't", 'like_NEG', 'this_NEG', 'movie_NEG', '.', 'It', 'was', 'bad', '.']
    """
    if not shallow:
        document = deepcopy(document)
    # check if the document is labeled. If so, do not consider the label.
    labeled = document and isinstance(document[0], (tuple, list))
    if labeled:
        doc = document[0]
    else:
        doc = document
    neg_scope = False
    for i, word in enumerate(doc):
        if NEGATION_RE.search(word):
            if not neg_scope or (neg_scope and double_neg_flip):
                neg_scope = not neg_scope
                continue
            else:
                doc[i] += "_NEG"
        elif neg_scope and CLAUSE_PUNCT_RE.search(word):
            neg_scope = not neg_scope
        elif neg_scope and not CLAUSE_PUNCT_RE.search(word):
            doc[i] += "_NEG"

    return document


def output_markdown(filename, **kwargs):
    """
    Write the output of an analysis to a file.
    """
    with codecs.open(filename, "at") as outfile:
        text = "\n*** \n\n"
        text += "{} \n\n".format(time.strftime("%d/%m/%Y, %H:%M"))
        for k in sorted(kwargs):
            if isinstance(kwargs[k], dict):
                dictionary = kwargs[k]
                text += f"  - **{k}:**\n"
                for entry in sorted(dictionary):
                    text += f"    - {entry}: {dictionary[entry]} \n"
            elif isinstance(kwargs[k], list):
                text += f"  - **{k}:**\n"
                for entry in kwargs[k]:
                    text += f"    - {entry}\n"
            else:
                text += f"  - **{k}:** {kwargs[k]} \n"
        outfile.write(text)


def split_train_test(all_instances, n=None):
    """
    Randomly split `n` instances of the dataset into train and test sets.

    :param all_instances: a list of instances (e.g. documents) that will be split.
    :param n: the number of instances to consider (in case we want to use only a
        subset).
    :return: two lists of instances. Train set is 8/10 of the total and test set
        is 2/10 of the total.
    """
    random.seed(12345)
    random.shuffle(all_instances)
    if not n or n > len(all_instances):
        n = len(all_instances)
    train_set = all_instances[: int(0.8 * n)]
    test_set = all_instances[int(0.8 * n) : n]

    return train_set, test_set


def _show_plot(x_values, y_values, x_labels=None, y_labels=None):
    try:
        import matplotlib.pyplot as plt
    except ImportError as e:
        raise ImportError(
            "The plot function requires matplotlib to be installed."
            "See https://matplotlib.org/"
        ) from e

    plt.locator_params(axis="y", nbins=3)
    axes = plt.axes()
    axes.yaxis.grid()
    plt.plot(x_values, y_values, "ro", color="red")
    plt.ylim(ymin=-1.2, ymax=1.2)
    plt.tight_layout(pad=5)
    if x_labels:
        plt.xticks(x_values, x_labels, rotation="vertical")
    if y_labels:
        plt.yticks([-1, 0, 1], y_labels, rotation="horizontal")
    # Pad margins so that markers are not clipped by the axes
    plt.margins(0.2)
    plt.show()


# ////////////////////////////////////////////////////////////
# { Parsing and conversion functions
# ////////////////////////////////////////////////////////////


def json2csv_preprocess(
    json_file,
    outfile,
    fields,
    encoding="utf8",
    errors="replace",
    gzip_compress=False,
    skip_retweets=True,
    skip_tongue_tweets=True,
    skip_ambiguous_tweets=True,
    strip_off_emoticons=True,
    remove_duplicates=True,
    limit=None,
):
    """
    Convert json file to csv file, preprocessing each row to obtain a suitable
    dataset for tweets Semantic Analysis.

    :param json_file: the original json file containing tweets.
    :param outfile: the output csv filename.
    :param fields: a list of fields that will be extracted from the json file and
        kept in the output csv file.
    :param encoding: the encoding of the files.
    :param errors: the error handling strategy for the output writer.
    :param gzip_compress: if True, create a compressed GZIP file.

    :param skip_retweets: if True, remove retweets.
    :param skip_tongue_tweets: if True, remove tweets containing ":P" and ":-P"
        emoticons.
    :param skip_ambiguous_tweets: if True, remove tweets containing both happy
        and sad emoticons.
    :param strip_off_emoticons: if True, strip off emoticons from all tweets.
    :param remove_duplicates: if True, remove tweets appearing more than once.
    :param limit: an integer to set the number of tweets to convert. After the
        limit is reached the conversion will stop. It can be useful to create
        subsets of the original tweets json data.
    """
    with codecs.open(json_file, encoding=encoding) as fp:
        (writer, outf) = _outf_writer(outfile, encoding, errors, gzip_compress)
        # write the list of fields as header
        writer.writerow(fields)

        if remove_duplicates == True:
            tweets_cache = []
        i = 0
        for line in fp:
            tweet = json.loads(line)
            row = extract_fields(tweet, fields)
            try:
                text = row[fields.index("text")]
                # Remove retweets
                if skip_retweets == True:
                    if re.search(r"\bRT\b", text):
                        continue
                # Remove tweets containing ":P" and ":-P" emoticons
                if skip_tongue_tweets == True:
                    if re.search(r"\:\-?P\b", text):
                        continue
                # Remove tweets containing both happy and sad emoticons
                if skip_ambiguous_tweets == True:
                    all_emoticons = EMOTICON_RE.findall(text)
                    if all_emoticons:
                        if (set(all_emoticons) & HAPPY) and (set(all_emoticons) & SAD):
                            continue
                # Strip off emoticons from all tweets
                if strip_off_emoticons == True:
                    row[fields.index("text")] = re.sub(
                        r"(?!\n)\s+", " ", EMOTICON_RE.sub("", text)
                    )
                # Remove duplicate tweets
                if remove_duplicates == True:
                    if row[fields.index("text")] in tweets_cache:
                        continue
                    else:
                        tweets_cache.append(row[fields.index("text")])
            except ValueError:
                pass
            writer.writerow(row)
            i += 1
            if limit and i >= limit:
                break
        outf.close()


def parse_tweets_set(
    filename, label, word_tokenizer=None, sent_tokenizer=None, skip_header=True
):
    """
    Parse csv file containing tweets and output data a list of (text, label) tuples.

    :param filename: the input csv filename.
    :param label: the label to be appended to each tweet contained in the csv file.
    :param word_tokenizer: the tokenizer instance that will be used to tokenize
        each sentence into tokens (e.g. WordPunctTokenizer() or BlanklineTokenizer()).
        If no word_tokenizer is specified, tweets will not be tokenized.
    :param sent_tokenizer: the tokenizer that will be used to split each tweet into
        sentences.
    :param skip_header: if True, skip the first line of the csv file (which usually
        contains headers).

    :return: a list of (text, label) tuples.
    """
    tweets = []
    if not sent_tokenizer:
        sent_tokenizer = load("tokenizers/punkt/english.pickle")

    with codecs.open(filename, "rt") as csvfile:
        reader = csv.reader(csvfile)
        if skip_header == True:
            next(reader, None)  # skip the header
        i = 0
        for tweet_id, text in reader:
            # text = text[1]
            i += 1
            sys.stdout.write(f"Loaded {i} tweets\r")
            # Apply sentence and word tokenizer to text
            if word_tokenizer:
                tweet = [
                    w
                    for sent in sent_tokenizer.tokenize(text)
                    for w in word_tokenizer.tokenize(sent)
                ]
            else:
                tweet = text
            tweets.append((tweet, label))

    print(f"Loaded {i} tweets")
    return tweets


# ////////////////////////////////////////////////////////////
# { Demos
# ////////////////////////////////////////////////////////////


def demo_tweets(trainer, n_instances=None, output=None):
    """
    Train and test Naive Bayes classifier on 10000 tweets, tokenized using
    TweetTokenizer.
    Features are composed of:

    - 1000 most frequent unigrams
    - 100 top bigrams (using BigramAssocMeasures.pmi)

    :param trainer: `train` method of a classifier.
    :param n_instances: the number of total tweets that have to be used for
        training and testing. Tweets will be equally split between positive and
        negative.
    :param output: the output file where results have to be reported.
    """
    from nltk.corpus import stopwords, twitter_samples
    from nltk.sentiment import SentimentAnalyzer
    from nltk.tokenize import TweetTokenizer

    # Different customizations for the TweetTokenizer
    tokenizer = TweetTokenizer(preserve_case=False)
    # tokenizer = TweetTokenizer(preserve_case=True, strip_handles=True)
    # tokenizer = TweetTokenizer(reduce_len=True, strip_handles=True)

    if n_instances is not None:
        n_instances = int(n_instances / 2)

    fields = ["id", "text"]
    positive_json = twitter_samples.abspath("positive_tweets.json")
    positive_csv = "positive_tweets.csv"
    json2csv_preprocess(positive_json, positive_csv, fields, limit=n_instances)

    negative_json = twitter_samples.abspath("negative_tweets.json")
    negative_csv = "negative_tweets.csv"
    json2csv_preprocess(negative_json, negative_csv, fields, limit=n_instances)

    neg_docs = parse_tweets_set(negative_csv, label="neg", word_tokenizer=tokenizer)
    pos_docs = parse_tweets_set(positive_csv, label="pos", word_tokenizer=tokenizer)

    # We separately split subjective and objective instances to keep a balanced
    # uniform class distribution in both train and test sets.
    train_pos_docs, test_pos_docs = split_train_test(pos_docs)
    train_neg_docs, test_neg_docs = split_train_test(neg_docs)

    training_tweets = train_pos_docs + train_neg_docs
    testing_tweets = test_pos_docs + test_neg_docs

    sentim_analyzer = SentimentAnalyzer()
    # stopwords = stopwords.words('english')
    # all_words = [word for word in sentim_analyzer.all_words(training_tweets) if word.lower() not in stopwords]
    all_words = [word for word in sentim_analyzer.all_words(training_tweets)]

    # Add simple unigram word features
    unigram_feats = sentim_analyzer.unigram_word_feats(all_words, top_n=1000)
    sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)

    # Add bigram collocation features
    bigram_collocs_feats = sentim_analyzer.bigram_collocation_feats(
        [tweet[0] for tweet in training_tweets], top_n=100, min_freq=12
    )
    sentim_analyzer.add_feat_extractor(
        extract_bigram_feats, bigrams=bigram_collocs_feats
    )

    training_set = sentim_analyzer.apply_features(training_tweets)
    test_set = sentim_analyzer.apply_features(testing_tweets)

    classifier = sentim_analyzer.train(trainer, training_set)
    # classifier = sentim_analyzer.train(trainer, training_set, max_iter=4)
    try:
        classifier.show_most_informative_features()
    except AttributeError:
        print(
            "Your classifier does not provide a show_most_informative_features() method."
        )
    results = sentim_analyzer.evaluate(test_set)

    if output:
        extr = [f.__name__ for f in sentim_analyzer.feat_extractors]
        output_markdown(
            output,
            Dataset="labeled_tweets",
            Classifier=type(classifier).__name__,
            Tokenizer=tokenizer.__class__.__name__,
            Feats=extr,
            Results=results,
            Instances=n_instances,
        )


def demo_movie_reviews(trainer, n_instances=None, output=None):
    """
    Train classifier on all instances of the Movie Reviews dataset.
    The corpus has been preprocessed using the default sentence tokenizer and
    WordPunctTokenizer.
    Features are composed of:

    - most frequent unigrams

    :param trainer: `train` method of a classifier.
    :param n_instances: the number of total reviews that have to be used for
        training and testing. Reviews will be equally split between positive and
        negative.
    :param output: the output file where results have to be reported.
    """
    from nltk.corpus import movie_reviews
    from nltk.sentiment import SentimentAnalyzer

    if n_instances is not None:
        n_instances = int(n_instances / 2)

    pos_docs = [
        (list(movie_reviews.words(pos_id)), "pos")
        for pos_id in movie_reviews.fileids("pos")[:n_instances]
    ]
    neg_docs = [
        (list(movie_reviews.words(neg_id)), "neg")
        for neg_id in movie_reviews.fileids("neg")[:n_instances]
    ]
    # We separately split positive and negative instances to keep a balanced
    # uniform class distribution in both train and test sets.
    train_pos_docs, test_pos_docs = split_train_test(pos_docs)
    train_neg_docs, test_neg_docs = split_train_test(neg_docs)

    training_docs = train_pos_docs + train_neg_docs
    testing_docs = test_pos_docs + test_neg_docs

    sentim_analyzer = SentimentAnalyzer()
    all_words = sentim_analyzer.all_words(training_docs)

    # Add simple unigram word features
    unigram_feats = sentim_analyzer.unigram_word_feats(all_words, min_freq=4)
    sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
    # Apply features to obtain a feature-value representation of our datasets
    training_set = sentim_analyzer.apply_features(training_docs)
    test_set = sentim_analyzer.apply_features(testing_docs)

    classifier = sentim_analyzer.train(trainer, training_set)
    try:
        classifier.show_most_informative_features()
    except AttributeError:
        print(
            "Your classifier does not provide a show_most_informative_features() method."
        )
    results = sentim_analyzer.evaluate(test_set)

    if output:
        extr = [f.__name__ for f in sentim_analyzer.feat_extractors]
        output_markdown(
            output,
            Dataset="Movie_reviews",
            Classifier=type(classifier).__name__,
            Tokenizer="WordPunctTokenizer",
            Feats=extr,
            Results=results,
            Instances=n_instances,
        )


def demo_subjectivity(trainer, save_analyzer=False, n_instances=None, output=None):
    """
    Train and test a classifier on instances of the Subjective Dataset by Pang and
    Lee. The dataset is made of 5000 subjective and 5000 objective sentences.
    All tokens (words and punctuation marks) are separated by a whitespace, so
    we use the basic WhitespaceTokenizer to parse the data.

    :param trainer: `train` method of a classifier.
    :param save_analyzer: if `True`, store the SentimentAnalyzer in a pickle file.
    :param n_instances: the number of total sentences that have to be used for
        training and testing. Sentences will be equally split between positive
        and negative.
    :param output: the output file where results have to be reported.
    """
    from nltk.corpus import subjectivity
    from nltk.sentiment import SentimentAnalyzer

    if n_instances is not None:
        n_instances = int(n_instances / 2)

    subj_docs = [
        (sent, "subj") for sent in subjectivity.sents(categories="subj")[:n_instances]
    ]
    obj_docs = [
        (sent, "obj") for sent in subjectivity.sents(categories="obj")[:n_instances]
    ]

    # We separately split subjective and objective instances to keep a balanced
    # uniform class distribution in both train and test sets.
    train_subj_docs, test_subj_docs = split_train_test(subj_docs)
    train_obj_docs, test_obj_docs = split_train_test(obj_docs)

    training_docs = train_subj_docs + train_obj_docs
    testing_docs = test_subj_docs + test_obj_docs

    sentim_analyzer = SentimentAnalyzer()
    all_words_neg = sentim_analyzer.all_words(
        [mark_negation(doc) for doc in training_docs]
    )

    # Add simple unigram word features handling negation
    unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4)
    sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)

    # Apply features to obtain a feature-value representation of our datasets
    training_set = sentim_analyzer.apply_features(training_docs)
    test_set = sentim_analyzer.apply_features(testing_docs)

    classifier = sentim_analyzer.train(trainer, training_set)
    try:
        classifier.show_most_informative_features()
    except AttributeError:
        print(
            "Your classifier does not provide a show_most_informative_features() method."
        )
    results = sentim_analyzer.evaluate(test_set)

    if save_analyzer == True:
        sentim_analyzer.save_file(sentim_analyzer, "sa_subjectivity.pickle")

    if output:
        extr = [f.__name__ for f in sentim_analyzer.feat_extractors]
        output_markdown(
            output,
            Dataset="subjectivity",
            Classifier=type(classifier).__name__,
            Tokenizer="WhitespaceTokenizer",
            Feats=extr,
            Instances=n_instances,
            Results=results,
        )

    return sentim_analyzer


def demo_sent_subjectivity(text):
    """
    Classify a single sentence as subjective or objective using a stored
    SentimentAnalyzer.

    :param text: a sentence whose subjectivity has to be classified.
    """
    from nltk.classify import NaiveBayesClassifier
    from nltk.tokenize import regexp

    word_tokenizer = regexp.WhitespaceTokenizer()
    try:
        sentim_analyzer = load("sa_subjectivity.pickle")
    except LookupError:
        print("Cannot find the sentiment analyzer you want to load.")
        print("Training a new one using NaiveBayesClassifier.")
        sentim_analyzer = demo_subjectivity(NaiveBayesClassifier.train, True)

    # Tokenize and convert to lower case
    tokenized_text = [word.lower() for word in word_tokenizer.tokenize(text)]
    print(sentim_analyzer.classify(tokenized_text))


def demo_liu_hu_lexicon(sentence, plot=False):
    """
    Basic example of sentiment classification using Liu and Hu opinion lexicon.
    This function simply counts the number of positive, negative and neutral words
    in the sentence and classifies it depending on which polarity is more represented.
    Words that do not appear in the lexicon are considered as neutral.

    :param sentence: a sentence whose polarity has to be classified.
    :param plot: if True, plot a visual representation of the sentence polarity.
    """
    from nltk.corpus import opinion_lexicon
    from nltk.tokenize import treebank

    tokenizer = treebank.TreebankWordTokenizer()
    pos_words = 0
    neg_words = 0
    tokenized_sent = [word.lower() for word in tokenizer.tokenize(sentence)]

    x = list(range(len(tokenized_sent)))  # x axis for the plot
    y = []

    for word in tokenized_sent:
        if word in opinion_lexicon.positive():
            pos_words += 1
            y.append(1)  # positive
        elif word in opinion_lexicon.negative():
            neg_words += 1
            y.append(-1)  # negative
        else:
            y.append(0)  # neutral

    if pos_words > neg_words:
        print("Positive")
    elif pos_words < neg_words:
        print("Negative")
    elif pos_words == neg_words:
        print("Neutral")

    if plot == True:
        _show_plot(
            x, y, x_labels=tokenized_sent, y_labels=["Negative", "Neutral", "Positive"]
        )


def demo_vader_instance(text):
    """
    Output polarity scores for a text using Vader approach.

    :param text: a text whose polarity has to be evaluated.
    """
    from nltk.sentiment import SentimentIntensityAnalyzer

    vader_analyzer = SentimentIntensityAnalyzer()
    print(vader_analyzer.polarity_scores(text))


def demo_vader_tweets(n_instances=None, output=None):
    """
    Classify 10000 positive and negative tweets using Vader approach.

    :param n_instances: the number of total tweets that have to be classified.
    :param output: the output file where results have to be reported.
    """
    from collections import defaultdict

    from nltk.corpus import twitter_samples
    from nltk.metrics import accuracy as eval_accuracy
    from nltk.metrics import f_measure as eval_f_measure
    from nltk.metrics import precision as eval_precision
    from nltk.metrics import recall as eval_recall
    from nltk.sentiment import SentimentIntensityAnalyzer

    if n_instances is not None:
        n_instances = int(n_instances / 2)

    fields = ["id", "text"]
    positive_json = twitter_samples.abspath("positive_tweets.json")
    positive_csv = "positive_tweets.csv"
    json2csv_preprocess(
        positive_json,
        positive_csv,
        fields,
        strip_off_emoticons=False,
        limit=n_instances,
    )

    negative_json = twitter_samples.abspath("negative_tweets.json")
    negative_csv = "negative_tweets.csv"
    json2csv_preprocess(
        negative_json,
        negative_csv,
        fields,
        strip_off_emoticons=False,
        limit=n_instances,
    )

    pos_docs = parse_tweets_set(positive_csv, label="pos")
    neg_docs = parse_tweets_set(negative_csv, label="neg")

    # We separately split subjective and objective instances to keep a balanced
    # uniform class distribution in both train and test sets.
    train_pos_docs, test_pos_docs = split_train_test(pos_docs)
    train_neg_docs, test_neg_docs = split_train_test(neg_docs)

    training_tweets = train_pos_docs + train_neg_docs
    testing_tweets = test_pos_docs + test_neg_docs

    vader_analyzer = SentimentIntensityAnalyzer()

    gold_results = defaultdict(set)
    test_results = defaultdict(set)
    acc_gold_results = []
    acc_test_results = []
    labels = set()
    num = 0
    for i, (text, label) in enumerate(testing_tweets):
        labels.add(label)
        gold_results[label].add(i)
        acc_gold_results.append(label)
        score = vader_analyzer.polarity_scores(text)["compound"]
        if score > 0:
            observed = "pos"
        else:
            observed = "neg"
        num += 1
        acc_test_results.append(observed)
        test_results[observed].add(i)
    metrics_results = {}
    for label in labels:
        accuracy_score = eval_accuracy(acc_gold_results, acc_test_results)
        metrics_results["Accuracy"] = accuracy_score
        precision_score = eval_precision(gold_results[label], test_results[label])
        metrics_results[f"Precision [{label}]"] = precision_score
        recall_score = eval_recall(gold_results[label], test_results[label])
        metrics_results[f"Recall [{label}]"] = recall_score
        f_measure_score = eval_f_measure(gold_results[label], test_results[label])
        metrics_results[f"F-measure [{label}]"] = f_measure_score

    for result in sorted(metrics_results):
        print(f"{result}: {metrics_results[result]}")

    if output:
        output_markdown(
            output,
            Approach="Vader",
            Dataset="labeled_tweets",
            Instances=n_instances,
            Results=metrics_results,
        )


if __name__ == "__main__":
    from sklearn.svm import LinearSVC

    from nltk.classify import MaxentClassifier, NaiveBayesClassifier
    from nltk.classify.scikitlearn import SklearnClassifier
    from nltk.twitter.common import _outf_writer, extract_fields

    naive_bayes = NaiveBayesClassifier.train
    svm = SklearnClassifier(LinearSVC()).train
    maxent = MaxentClassifier.train

    demo_tweets(naive_bayes)
    # demo_movie_reviews(svm)
    # demo_subjectivity(svm)
    # demo_sent_subjectivity("she's an artist , but hasn't picked up a brush in a year . ")
    # demo_liu_hu_lexicon("This movie was actually neither that funny, nor super witty.", plot=True)
    # demo_vader_instance("This movie was actually neither that funny, nor super witty.")
    # demo_vader_tweets()