exp_replace.py

"""
Used from Mathew Cliche's github: https://github.com/MathieuCliche/Sarcasm_detector
Cretor of thesarcasmdetector.com, our base project.
"""

""" These functions are used to replace tweet slang by words which are more easy
to be recognized by the sentiment analysis and to reduce the number of dimensions of
the features dictionnary.  """

import nltk
import re

# dictionnary to sentiment analysis
emo_repl = {
    # good emotions
    "<3": " good ",
    ":d": " good ",
    ":dd": " good ",
    ":p": " good ",
    "8)": " good ",
    ":-)": " good ",
    ":)": " good ",
    ";)": " good ",
    "(-:": " good ",
    "(:": " good ",

    "yay!": " good ",
    "yay": " good ",
    "yaay": " good ",
    "yaaay": " good ",
    "yaaaay": " good ",
    "yaaaaay": " good ",
    # bad emotions
    ":/": " bad ",
    ":>": " sad ",
    ":')": " sad ",
    ":-(": " bad ",
    ":(": " bad ",
    ":s": " bad ",
    ":-s": " bad "
}

# dictionnary for general (i.e. topic modeler)
emo_repl2 = {
    # good emotions
    "<3": " heart ",
    ":d": " smile ",
    ":p": " smile ",
    ":dd": " smile ",
    "8)": " smile ",
    ":-)": " smile ",
    ":)": " smile ",
    ";)": " smile ",
    "(-:": " smile ",
    "(:": " smile ",

    # bad emotions
    ":/": " worry ",
    ":>": " angry ",
    ":')": " sad ",
    ":-(": " sad ",
    ":(": " sad ",
    ":s": " sad ",
    ":-s": " sad "
}

# general
re_repl = {
    r"\br\b": "are",
    r"\bu\b": "you",
    r"\bhaha\b": "ha",
    r"\bhahaha\b": "ha",
    r"\bdon't\b": "do not",
    r"\bdoesn't\b": "does not",
    r"\bdidn't\b": "did not",
    r"\bhasn't\b": "has not",
    r"\bhaven't\b": "have not",
    r"\bhadn't\b": "had not",
    r"\bwon't\b": "will not",
    r"\bwouldn't\b": "would not",
    r"\bcan't\b": "can not",
    r"\bcannot\b": "can not"
}

emo_repl_order = [k for (k_len, k) in
                  reversed(sorted([(len(k), k) for k in emo_repl.keys()]))]
emo_repl_order2 = [k for (k_len, k) in
                   reversed(sorted([(len(k), k) for k in emo_repl2.keys()]))]


def replace_emo(sentence):
    sentence2 = sentence
    for k in emo_repl_order:
        sentence2 = sentence2.replace(k, emo_repl[k])
    for r, repl in re_repl.items():
        sentence2 = re.sub(r, repl, sentence2)
    return sentence2


def replace_reg(sentence):
    sentence2 = sentence
    for k in emo_repl_order2:
        sentence2 = sentence2.replace(k, emo_repl2[k])
    for r, repl in re_repl.iteritems():
        sentence2 = re.sub(r, repl, sentence2)
    return sentence2