def _get_sentiments(self, d):
sent_word_net = load_sent_word_net()
poscache_filename = "poscache.json"
try:
poscache = json.load(open(poscache_filename, "r"))
except IOError:
poscache = {}
# http://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html
#import pdb;pdb.set_trace()
sent = tuple(nltk.word_tokenize(d))
#pos_tag tags tokens with part of speech (noun, verb etc)
if poscache is not None:
if d in poscache:
tagged = poscache[d]
else:
poscache[d] = tagged = nltk.pos_tag(sent)
else:
tagged = nltk.pos_tag(sent)
pos_vals = []
neg_vals = []
nouns = 0.
adjectives = 0.
verbs = 0.
adverbs = 0.
for w,t in tagged:
p, n = 0,0
sent_pos_type = None
if t.startswith("NN"):
sent_pos_type = "n"
nouns += 1
elif t.startswith("JJ"):
sent_pos_type = "a"
adjectives += 1
elif t.startswith("VB"):
sent_pos_type = "v"
verbs += 1
elif t.startswith("RB"):
sent_pos_type = "r"
adverbs += 1
if sent_pos_type is not None:
sent_word = "%s/%s"%(sent_pos_type, w)
if sent_word in sent_word_net:
p,n = sent_word_net[sent_word]
pos_vals.append(p)
neg_vals.append(n)
l = len(sent)
avg_pos_val = np.mean(pos_vals)
avg_neg_val = np.mean(neg_vals)
#import pdb;pdb.set_trace()
return [1-avg_pos_val-avg_neg_val, avg_pos_val, avg_neg_val,
nouns/l, adjectives/l, verbs/l, adverbs/l]
from sklearn.metrics import precision_recall_curve, roc_curve, auc
from sklearn.cross_validation import ShuffleSplit
from sklearn.pipeline import Pipeline
from utils import plot_pr
from utils import load_sanders_data
from utils import tweak_labels
from utils import log_false_positives
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from utils import load_sent_word_net
sent_word_net = load_sent_word_net()
phase = "03"
emo_repl = {
# positive emoticons
"<3": " good ",
":d": " good ", # :D in lower case
":dd": " good ", # :DD in lower case
"8)": " good ",
":-)": " good ",
":)": " good ",
";)": " good ",
"(-:": " good ",
"(:": " good ",
from utils import plot_pr
from utils import load_sanders_data
from utils import tweak_labels
from utils import log_false_positives
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import f1_score
from sklearn.base import BaseEstimator
from sklearn.naive_bayes import MultinomialNB
from utils import load_sent_word_net
sent_word_net = load_sent_word_net()
import json
poscache_filename = "poscache.json"
try:
poscache = json.load(open(poscache_filename, "r"))
except IOError:
poscache = {}
class StructCounter(BaseEstimator):
def get_feature_names(self):
return np.array([
'sent_neut', 'sent_pos', 'sent_neg', 'nouns', 'adjectives',
'verbs', 'adverbs', 'allcaps', 'exclamation', 'question',
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",
}
sent_words = load_sent_word_net()
def create_ngram_model(params=None):
def preprocessor(tweet):
tweet = tweet.lower()
global emoticons_reversed
for emoticon in emoticons_reversed:
tweet = tweet.replace(emoticon, emoticons_replacements[emoticon])
for regex, replacement in regex_replace.iteritems():
tweet = re.sub(regex, replacement, tweet)
return tweet
