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from __future__ import print_function
from __future__ import unicode_literals
from builtins import str, bytes, dict, int
from builtins import range
import os
import sys
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
from pattern.web import Twitter
from pattern.graph import Graph
# This example demonstrates a simple Twitter miner + visualizer.
# We collect tweets containing "A is the new B",
# mine A and B and use them as connected nodes in a graph.
# Then we export the graph as a browser visualization.
comparisons = []
for i in range(1, 10):
# Set cached=False for live results:
for result in Twitter(language="en").search("\"is the new\"", start=i, count=100, cached=True):
s = result.text
s = s.replace("\n", " ")
s = s.lower()
s = s.replace("is the new", "NEW")
s = s.split(" ")
try:
i = s.index("NEW")
A = s[i - 1].strip("?!.:;,#@\"'")
B = s[i + 1].strip("?!.:;,#@\"'")
# Exclude common phrases such as "this is the new thing".
if A and B and A not in ("it", "this", "here", "what", "why", "where"):
comparisons.append((A, B))
except:
pass
g = Graph()
for A, B in comparisons:
e = g.add_edge(B, A) # "A is the new B": A <= B
e.weight += 0.1
print(B, "=>", A)
# Not all nodes will be connected, there will be multiple subgraphs.
# Simply take the largest subgraph for our visualization.
g = g.split()[0]
g.export("trends", weighted=True, directed=True)
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