def __init__(self):
self.criterias = {
"track": ['Khomri', 'Paris', 'Travail', 'Myriam', 'ElKhomri'],
"locations": [2.138472,48.774288,2.518187,48.965338],
"lang": ["fr"]
}
FilteredStream.__init__(self, self.criterias, 10, "config.json")
def __init__(self):
self.criterias = {
"track": user_cat,
"locations": [-0.6389644,44.8111222,-0.5334955,44.9163535],
"lang": ["fr"]
}
FilteredStream.__init__(self, self.criterias, 10, "config.json")
def __init__(self):
self.criterias = {
"track": ['Khomri', 'Paris', 'Travail', 'Myriam', 'ElKhomri'],
"locations": [2.138472, 48.774288, 2.518187, 48.965338],
"lang": ["fr"]
}
FilteredStream.__init__(self, self.criterias, 10, "config.json")
def __init__(self):
# Tweets from Bordeaux OR mentioning 'Paris'
self.criterias = {
"track": ['remaniement', 'Paris'],
"locations": [-0.6389644,44.8111222,-0.5334955,44.9163535]
}
FilteredStream.__init__(self, self.criterias, "config.json")
def __init__(self):
# Tweets from Bordeaux OR mentioning 'Paris'
self.criterias = {
"track": ['remaniement', 'Paris'],
"locations": [-0.6389644, 44.8111222, -0.5334955, 44.9163535]
}
FilteredStream.__init__(self, self.criterias, "config.json")
def __init__(self):
self.criterias = {
#You can choose some key words to look for by chaging the track's content
"track": ['remaniement', 'Paris'],
"locations": [-0.6389644,44.8111222,-0.5334955,44.9163535],
"lang": [*]
}
FilteredStream.__init__(self, self.criterias, 10, "config.json")
def __init__(self):
# Tweets from Bordeaux OR mentioning 'Paris', in ANY language
self.criterias = {
"track": ["Paris"], # Keywords filter
"locations": [-0.6389644,44.8111222,-0.5334955,44.9163535], # Bordeaux bounding box
# "lang": ["fr", "en"] # In French OR English
"lang": ["*"] # Any language
}
FilteredStream.__init__(self, self.criterias, 5, config)
def __init__(self):
# Tweets from Bordeaux OR mentioning 'Paris', in ANY language
self.criterias = {
"track": ["Paris"], # Keywords filter
"locations": [-0.6389644, 44.8111222, -0.5334955,
44.9163535], # Bordeaux bounding box
# "lang": ["fr", "en"] # In French OR English
"lang": ["*"] # Any language
}
FilteredStream.__init__(self, self.criterias, 5, config)
def __init__(self):
# Search criterias
self.criterias = {
"track": ["Paris", "Bordeaux"],
"locations": [-0.6389644, 44.8111222, -0.5334955, 44.9163535],
"lang": ["fr"]
}
FilteredStream.__init__(self, self.criterias, SIZE, "../config.json")
self.tweets = []
self.num_export = 0
def __init__(self):
# Search criterias
self.criterias = {
"track": ["Paris", "Bordeaux"],
"locations": [-0.6389644,44.8111222,-0.5334955,44.9163535],
"lang": ["fr"]
}
FilteredStream.__init__(self, self.criterias, SIZE, "../config.json")
self.tweets = []
self.num_export = 0
def __init__(self):
# Tweets from Paris OR mentioning 'Paris', in French
self.criterias = {
"track": ["Paris"], # Keywords filter
"locations": [2.138472, 48.774288, 2.518187,
48.965338], # Paris bounding box
"lang": ["fr"] # In French
}
self.corpus_tot = []
self.labels = np.asarray([])
# Creates the classificator : threshold affect the clustering
self.brc = birch.clf_init(threshold=1.5)
self.mon_fichier = open("FinalDictionary.txt", "r")
self.vectorizer = CountVectorizer()
X = self.vectorizer.fit_transform(self.mon_fichier)
FilteredStream.__init__(self, self.criterias, 10, config)
