def getTags(self):
tags = []
datum_box = DatumBox(API_KEY)
#If PASSED_STRING is the name of a PDF document, fetch the text from the pdf document and set the
#string to be evaluated to the text contents of the pdf.
if self.PASSED_STRING[-4::]==".pdf":
self.PASSED_STRING = pydfconversion.getPDFText(self.PASSED_STRING)
#Set first tag to be one of 12 topics
tags.append(datum_box.topic_classification(self.PASSED_STRING))
#Add "Educational" tag if educational and "Not Educational" tag if not educational
if datum_box.is_educational(self.PASSED_STRING):
tags.append("Educational")
elif datum_box.is_educational(self.PASSED_STRING)!=True:
tags.append("Not Educational")
#Assign readability/difficulty of study to the third tag for each string
tags.append(datum_box.readability_assessment(self.PASSED_STRING))
list = datum_box.keyword_extract(self.PASSED_STRING)
for keyword in list:
tags.append(keyword)
#Remove 100 most commonly used words and individual letters from tag returns to help reduce clutter
for n in tags:
if n == ("time") or n == ("person") or n == ("year") or n == ("way") or n == ("day") or n == ("thing") or n == ("man")or n == ("world") or n == ("life") or n == ("hand") or n == ("part") or n == ("child") or n == ("eye") or n == ("woman") or n == ("place") or n == ("work") or n == ("week") or n == ("case") or n == ("point") or n == ("man") or n == ("company") or n == ("group")or n == ("problem") or n == ("fact") or n == ("be") or n == ("have") or n == ("do") or n == ("say") or n == ("get") or n == ("make") or n == ("go") or n == ("know") or n == ("take") or n == ("see") or n == ("come") or n == ("think") or n == ("look") or n == ("want") or n == ("give") or n == ("use") or n == ("find") or n == ("tell") or n == ("ask") or n == ("work") or n == ("seem") or n == ("feel") or n == ("try") or n == ("leave") or n == ("call") or n == ("good") or n == ("new") or n == ("first") or n == ("last") or n == ("long") or n == ("great") or n == ("little") or n == ("own") or n == ("other") or n == ("old") or n == ("right") or n == ("big") or n == ("high") or n == ("different") or n == ("small") or n == ("large") or n == ("next") or n == ("early") or n == ("young") or n == ("important") or n == ("few") or n == ("public") or n == ("bad")or n == ("same") or n == ("able") or n == ("to") or n == ("of") or n == ("in") or n == ("for") or n == ("on") or n == ("with") or n == ("at") or n == ("by") or n == ("from") or n == ("up") or n == ("about") or n == ("into") or n == ("over") or n == ("after") or n == ("beneath") or n == ("under") or n == ("above") or n == ("the") or n == ("and") or n == ("a") or n == ("that") or n == ("I") or n == ("it") or n == ("not") or n == ("he") or n == ("as") or n == ("you") or n == ("this") or n == ("but") or n == ("his") or n == ("they") or n == ("her") or n == ("she") or n == ("or") or n == ("an") or n == ("will") or n == ("my") or n == ("one") or n == ("all") or n == ("would") or n == ("there") or n == ("their") or n == ("is") or n == ("a") or n == ("b") or n == ("c") or n == ("d") or n == ("e") or n == ("f") or n == ("g") or n == ("h") or n == ("i") or n == ("j") or n == ("k") or n == ("l") or n == ("m") or n == ("n") or n == ("o") or n == ("p") or n == ("q") or n == ("r") or n == ("s") or n == ("t") or n == ("u") or n == ("v") or n == ("w") or n == ("x") or n == ("y") or n == ("z"):
tags.remove(n)
return tags
from DatumBox import DatumBox
datum_box = DatumBox("4e9f0a6e14a83e38d9d9fac895c84e68")
#print datum_box.twitter_sentiment_analysis("I love my cat")
print datum_box.topic_classification(
"datumbox api wrapper text python code commit sign")
#!/usr/bin/python
import praw
from DatumBox import DatumBox
import time
user_agent = "A continuous sentiment analysis of the front-page"
r = praw.Reddit(user_agent=user_agent)
r.login('LowLanding', 'Lespaul94', disable_warning=True)
datum_box = DatumBox('c3ac3635add7e6d13ce0218ff033674f')
# Array for submissions already analyzed
already_done = []
while True:
submissions = r.get_top()
for submission in submissions:
op_text = submission
print "\n"
print op_text
print "Sentiment: {}".format(datum_box.sentiment_analysis(op_text))
print "Topic: {}".format(datum_box.topic_classification(op_text))
print "Sleeping"
time.sleep(20)
def get_topic_category(api_key, text): datum_box = DatumBox(api_key) return datum_box.topic_classification(text)
