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import nltk
nltk.download()
## use nltk.download() within a Python prompt to
## download the `punkt` data
## Anaconda is recommended, to pick up NumPy, NLTK, etc.
## http://continuum.io/downloads
## this also requires TextBlob/PerceptronTagger
## pip install -U textblob textblob-aptagger
## PySpark impl for TextRank by Mihalcea, et al.
## http://www.cse.unt.edu/~rada/papers/mihalcea.emnlp04.pdf
import re
from operator import add
from itertools import tee, izip
import numpy as np
import nltk.data
from nltk import stem
from text.blob import TextBlob as tb
from textblob_aptagger import PerceptronTagger
TOKENIZER = nltk.data.load('tokenizers/punkt/english.pickle')
TAGGER = PerceptronTagger()
STEMMER = stem.porter.PorterStemmer()
def pos_tag (s):
"""high-performance part-of-speech tagger"""
global TAGGER
return TAGGER.tag(s)
def wrap_words (pair):
"""wrap each (word, tag) pair as an object with fully indexed metadata"""
global STEMMER
index = pair[0]
result = []
for word, tag in pair[1]:
word = word.lower()
stem = STEMMER.stem(word)
keep = tag in ('JJ', 'NN', 'NNS', 'NNP',)
result.append({ "index": index, "stem": stem, "word": word, "tag": tag, "keep": keep })
index += 1
return result
def sliding_window (iterable, size):
"""apply a sliding window to produce 'size' tiles"""
iters = tee(iterable, size)
for i in xrange(1, size):
for each in iters[i:]:
next(each, None)
return list(izip(*iters))
def keep_pair (pair):
"""filter the relevant linked word pairs"""
return pair[0]["keep"] and pair[1]["keep"] and (pair[0]["word"] != pair[1]["word"])
def link_words (seq):
"""attempt to link words in a sentence"""
return [ (seq[0], word) for word in seq[1:] ]
def compute_contribs (links, rank):
"""calculate link contributions to the rank of other links"""
num_links = len(links)
for link in links:
yield (link, rank / num_links,)
def text_rank_graph (neighbors, iter=20):
"""run the TextRank algorithm on the `neighbors` graph of the document"""
links = neighbors.distinct().groupByKey().cache()
ranks = links.map(lambda (link, neighbors): (link, 1.0))
for iteration in xrange(iter):
contribs = links.join(ranks).flatMap(
lambda (link, (links, rank)): compute_contribs(links, rank))
ranks = contribs.reduceByKey(add).mapValues(lambda rank: rank * 0.85 + 0.15)
for (rank, key) in ranks.map(lambda x: (x[1], x[0],)).sortByKey(False).collect():
yield key, rank
def glean_rank (pair):
"reorder the word metdata into (K,V) pairs for sorting"
key = pair[0]
w = pair[1][0]
rank = pair[1][1]
return w["index"], (w["word"], rank,)
def extract_phrases (doc):
"""extract the top-ranked keyphrases from the given tagged doc"""
last_index = -1000
last_rank = 0.0
result = []
for index, (word, rank) in doc:
if index - last_index > 1:
if len(result) > 0:
yield last_rank * len(result), ' '.join(result)
last_rank = 0.0
result = []
last_rank += rank
last_index = index
result.append(word)
TEXT = """
Compatibility of systems of linear constraints over the set of natural numbers.
Criteria of compatibility of a system of linear Diophantine equations, strict
inequations, and nonstrict inequations are considered. Upper bounds for
components of a minimal set of solutions and algorithms of construction of
minimal generating sets of solutions for all types of systems are given.
These criteria and the corresponding algorithms for constructing a minimal
supporting set of solutions can be used in solving all the considered types
systems and systems of mixed types.
"""
# construct a fully tagged document that is segmented into sentences
sent = sc.parallelize(TOKENIZER.tokenize(TEXT)).map(pos_tag).cache()
base = list(np.cumsum(np.array(sent.map(len).collect())))
base.insert(0, 0)
base.pop()
lens = sc.parallelize(base)
tagged_doc = lens.zip(sent).map(wrap_words).cache()
tagged_doc.collect()
# apply a sliding window to construct a graph of the relevant word pairs
tiled = tagged_doc.flatMap(lambda s: sliding_window(s, 3)).flatMap(link_words).filter(keep_pair)
t0 = tiled.map(lambda link: (link[0]["stem"], link[1]["stem"],))
t1 = tiled.map(lambda link: (link[1]["stem"], link[0]["stem"],))
neighbors = t0.union(t1)
# visualize in a graph
import matplotlib.pyplot as plt
import networkx as nx
G = nx.Graph()
for a, b in neighbors.collect():
G.add_edge(a, b, weight=1.0)
edges = [ (u,v) for (u,v,d) in G.edges(data=True) ]
pos = nx.spring_layout(G)
nx.draw_networkx_nodes(G, pos, node_size=700)
nx.draw_networkx_edges(G, pos, edgelist=edges, width=6)
nx.draw_networkx_labels(G, pos, font_size=20, font_family='sans-serif')
plt.axis('off')
plt.savefig("weighted_graph.png")
plt.show()
# run TextRank, then extract the top-ranked keyphrases
rank = sc.parallelize(text_rank_graph(neighbors))
tags = tagged_doc.flatMap(lambda x: x).map(lambda w: (w["stem"], w,))
l = tags.join(rank).map(glean_rank).sortByKey().collect()
for rank, phrase in sorted(set(extract_phrases(l)), reverse=True):
print "%0.2f %s" % (rank, phrase,)
## also try monitoring http://localhost:4040/ while this is running,
## to see the dynamics of Spark in action
## NLTK has some bugs in terms of word segmentation, and its PoS taggers
## could be much better, but this gives a general idea of how TextRank works
## In practice, a combination of TF-IDF and TextRank make a good blend
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@akovacsBK akovacsBK commented Aug 14, 2014

\

\\\``````

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@ceteri ceteri commented Aug 15, 2014

Looks like a markdown for cuneiform? :)

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@ashish2303 ashish2303 commented Jun 26, 2015

hi, can we do unsupervised sentiment analysis using nltk or textbob packages of python over spark that is pyspark . i mean suppose i have different rows of sentence then with entire pre processing like tokenization ,stop word removal ,pos tagging etc.. can we tag different sentence as postive,negative or neutral in pyspark.? standalone we can use python with textblob or ntlk package we can do this task. will it possible to do this task over spark with python.,? please reply if you have any idea

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