def __mean_no_friends(cls, df):
exists_nobody_likes_me = Nlp_util.are_words1_words2_words3_in_order(
df, ["nobody", "no one"], ["like", "love"], ["me"])
exists_friends_dont_like_me = Nlp_util.are_words1_words2_words3_in_order(
df, ["friend", "they", "everybody"],
["not like", "not love", "hate"], ["me"])
exists_have_no_friend = Nlp_util.are_words1_words2_words3_in_order(
df, ["i"], ["not have", "have no"], ["friend"])
return exists_nobody_likes_me or exists_friends_dont_like_me or exists_have_no_friend
def __is_jullie_useless(df):
if df is None:
return False
c1 = Nlp_util.are_words1_words2_words3_in_order(
df, ["you", "this"], ["be not", "be never"],
["helpful", "help", "helping"])
c2 = Nlp_util.are_words1_words2_words3_in_order(
df, ["you"], ["be"], ['useless', 'helpless'])
return c1 or c2
def __is_anxious(cls, df_by_sentence: DataFrame):
try:
word1_1 = ['anxious']
word1_2 = ['be', 'being', 'am', 'was', 'been', 'feel', 'feeling']
word2_1 = ['anxiety']
word2_2 = ['have', 'having', 'had', 'feel', 'feeling']
return Nlp_util.are_words1_words2_words3_in_order(df_by_sentence, word1_1, word1_2) \
or Nlp_util.are_words1_words2_words3_in_order(df_by_sentence, word2_1, word2_2)
except:
logging.exception('')
return False
def __is_anxious(cls, df_by_sentence: DataFrame):
word1_1 = ['anxious']
word1_2 = ['be', 'being', 'am', 'was', 'been', 'feel', 'feeling']
word2_1 = ['anxiety']
word2_2 = ['have', 'having', 'had', 'feel', 'feeling']
pattern_1 = Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, word1_1, word1_2)
pattern_2 = Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, word2_1, word2_2)
return pattern_1 or pattern_2
def __is_jullie_useless(df):
try:
if df is None:
return False
is_useless = Nlp_util.are_words1_words2_words3_in_order(
df, ["you", "this"], ["be not", "be never"],
["helpful", "help", "helping"
]) or Nlp_util.are_words1_words2_words3_in_order(
df, ["you"], ["be"], ['useless', 'helpless'])
return is_useless
except:
logging.exception('')
return False
def __like_someone(cls, df):
df_ex_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
target_list = [{
"subjects": ["guy", "friend", "boy", "man"],
"word1": ["i"],
"word2": ["like"],
"exceptions": []
}, {
"subjects": ["i"],
"word1": ["like"],
"word2": ["guy", "friend", "boy", "man"],
"exceptions": ["feel like"]
}, {
"subjects": ["i"],
"word1": ["be", "fall"],
"word2": ["in love"],
"exceptions": []
}]
return any(
Nlp_util.are_words1_words2_words3_in_order(
df_ex_adverb,
target["subjects"],
target["word1"],
target["word2"],
exception_list=target["exceptions"]) for target in target_list)
def __is_about_money(cls, df_by_sentence: DataFrame):
money_words = ['money', 'finance', 'financial', 'financially']
if any(i in df_by_sentence.word.values for i in money_words):
prp = ['they', 'he', 'she']
have = ['have']
if Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, prp, have, money_words):
return False
else:
return True
elif Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, ['i'], ['poor']):
return True
else:
return False
def __is_call_me_names(cls, df_by_sentence):
word1 = ['call']
word2 = ['me']
word3 = ['names']
return Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, word1, word2, word3)
def __is_no_idea(cls, df):
df_ex_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
noun_list = Nlp_util.make_noun_list(df)
verb_list = Nlp_util.make_verb_list(df, type="normal")
is_subj_himself = Nlp_util.is_first_subject_in(["i"], noun_list,
verb_list)
exist_subj_for_first_verb = Nlp_util.exist_subj_for_first_verb(
noun_list, verb_list)
is_idk_what_to_do = Df_util.anything_isin(
{"do not know", "not sure", "have no idea"},
df_ex_adverb["base_form"]) and Df_util.anything_isin(
{"what to do", "how to do", "what to deal", "how to deal"},
df_ex_adverb["base_form"])
is_want_advice = Df_util.anything_isin(
{"want", "need", "give me"},
df_ex_adverb["base_form"]) and Df_util.anything_isin(
{"advice", "suggestion"}, df_ex_adverb["word"])
is_give_me_advice = Df_util.anything_isin(
{"need", "want", "give me"},
df_ex_adverb["base_form"]) and Df_util.anything_isin(
{"advice", "suggestion"}, df_ex_adverb["word"])
what_should_i_do = Nlp_util.are_words1_words2_words3_in_order(
df_ex_adverb, ["what"], ["should"], ["i"])
return (is_subj_himself and (is_idk_what_to_do or is_want_advice)) or (
not exist_subj_for_first_verb
and is_give_me_advice) or what_should_i_do
def __has_what_to_do(df):
try:
df_ex_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
return Nlp_util.are_words1_words2_words3_in_order(df_ex_adverb, ["i"], ["not know", "not sure"],
["what to", "how to"])
except:
logging.exception('')
return False
def __lack_of_confidence(cls, df):
df_ex_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
target_list = [
[["i"], ["hate", "not like"], ["myself"]],
[["i"], ["be"],
["fat", "w***e", "s**t", "bitch", "stupid", "ugly", "burden"]],
[["i"], ["bother"], ["people", "everyone", "friends"]],
]
return any(
Nlp_util.are_words1_words2_words3_in_order(
df_ex_adverb,
target[0],
target[1],
target[2],
exception_list=["say", "tell"]) for target in
target_list) or Nlp_util.are_words1_words2_words3_in_order(
df, ["i"], ["be"], ["not good enough"])
def __has_wish_S_V(target_df):
if Df_util.anything_isin(["wish"], target_df["base_form"]):
wish_idx = Nlp_util.get_idx_list_of_word("wish", target_df["base_form"])[0]
if Nlp_util.are_words1_words2_words3_in_order(target_df.loc[wish_idx:,:], Nlp_util.pos_NOUNs+Nlp_util.pos_PRPs, Nlp_util.pos_VERBs, df_column="pos"):
return True
else:
return False
else:
return False
def __mean_no_friends(cls, df):
try:
exists_nobody_likes_me = Nlp_util.are_words1_words2_words3_in_order(
df, ["nobody", "no one"], ["like", "love"], ["me"])
exists_friends_dont_like_me = Nlp_util.are_words1_words2_words3_in_order(
df, ["friend", "they", "everybody"],
["not like", "not love", "hate"], ["me"])
exists_have_no_friend = Nlp_util.are_words1_words2_words3_in_order(
df, ["i"], ["not have", "have no"], ["friend"])
if exists_nobody_likes_me or exists_friends_dont_like_me or exists_have_no_friend:
return True
else:
return False
except:
logging.exception('')
return False
def __has_say_plus_bad_word(df):
if any([
Nlp_util.are_words1_words2_words3_in_order(
df, ["say", "tell"], ["i be", "i look"], [negative_word])
for negative_word in KWDF[KWDF['Type'] ==
'n'].keyword.tolist()
]):
return True
elif any([
Nlp_util.are_words1_words2_words3_in_order(
df, ["say", "tell"], ["i be not", "i do not look"],
[positive_word]) for positive_word in KWDF[
KWDF['Type'] == 'p'].keyword.tolist()
]):
return True
else:
return False
def __is_about_missing(cls, df_by_sentence):
try:
phrase1 = ['i']
phrase2 = ['miss']
return Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, phrase1, phrase2)
except:
logging.exception('')
return False
def __is_call_me_names(cls, df_by_sentence):
try:
word1 = ['call']
word2 = ['me']
word3 = ['names']
return Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, word1, word2, word3)
except:
logging.exception('')
return False
def __cant_get_attention_from_bf(cls, df):
df_ex_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
target_list = [
[["he", "boyfriend"],
["not pay", "never pay", "not give me", "never give me"],
["attention"]],
[["he", "boyfriend"],
[
"not respond", "never respond", "not reply", "never reply",
"not answer", "never answer", "not call", "never call",
"not text", "never text"
], []],
[["he", "boyfriend"],
["not listen", "never listen", "not care", "never care"], ["me"]]
]
return any(
Nlp_util.are_words1_words2_words3_in_order(df_ex_adverb, target[0],
target[1], target[2])
for target in target_list)
def __is_about_breakup(cls, df):
df_ex_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
target_list = [
[["i", "we", "boyfriend", "girlfriend"],
["brokeup", "broke up", "did breakup", "did breakup"], []],
[[
"she",
"he",
"boyfriend",
"girlfriend",
], ["brokeup", "broke up", "did breakup", "did breakup"],
["with me"]],
]
return any(
Nlp_util.are_words1_words2_words3_in_order(df_ex_adverb,
target[0],
target[1],
target[2],
df_column="word")
for target in target_list)
def __has_what_to_do(df):
df_ex_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
return Nlp_util.are_words1_words2_words3_in_order(
df_ex_adverb, ["i"], ["not know", "not sure"],
["what to", "how to"])
def __is_about_missing(cls, df_by_sentence):
phrase1 = ['i']
phrase2 = ['miss']
return Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, phrase1, phrase2)