def submission(submission_id):
r = praw.Reddit(user_agent=user_agent)
s = ServiceClient(config.TOKEN, config.ENDPOINT)
submission = r.get_submission(submission_id=submission_id,
comment_sort="top")
comments = praw.helpers.flatten_tree(submission.comments)
comments = sorted(comments, reverse=True, key=lambda x: x.score)[0:20]
for comment in comments:
res = s.request(input=comment.body,
intype="direct",
informat="text",
outformat="json-ld")
print res
comment.results = res
comment.polarity = 0
num = 0
for entry in res["entries"]:
for opinion in entry["opinions"]:
print "Polarity: %s" % comment.polarity
num+=1
comment.polarity+=opinion["marl:polarityValue"]
comment.polarity = comment.polarity / num
return render_template('comments.html', comments=comments)
class PositiveWordsMatcher:
def __init__(self):
self.language_detector = ServiceClient(LANG_DETECTION_URL, TOKEN)
self.domain_detector = ServiceClient(DOMAIN_DETECTION_URL, TOKEN)
self.resource_client = ResourceClient(RESOURCES_URL, TOKEN)
def positive_words(self, text):
lang_result = self.language_detector.request({"text": text})
language = lang_result.get("dc:language", None)
domain_result = self.domain_detector.request({"text": text})
domain = domain_result["domain"].split(":")[1]
query = sparql(POSITIVE_ENTRIES, language, domain)
input = {"query": query,
"format": "application/json"}
resources_result = self.resource_client.request(input)
sentiment_words = self.__extract_words_from_response(resources_result)
return matches_count(text, sentiment_words)
def __extract_words_from_response(self, resources_response):
words = set()
for word in resources_response.get("results", {}).get("bindings", []):
words.add(word.get("sentimentEntryWR", {}).get("value", None))
return words
def __init__(self):
self.language_detector = ServiceClient(LANG_DETECTION_URL, TOKEN)
self.domain_detector = ServiceClient(DOMAIN_DETECTION_URL, TOKEN)
self.resource_client = ResourceClient(RESOURCES_URL, TOKEN)
