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