def main(params):
natural_language_understanding = NaturalLanguageUnderstandingV1(
username=params["username"],
password=params["password"],
version=params["version"])
response = natural_language_understanding.analyze(
url=params["url"],
features=[
Features.Concepts(limit=1),
Features.Entities(limit=1),
Features.Keywords(limit=1),
Features.Categories(),
Features.Emotion(),
Features.Sentiment(),
Features.MetaData(),
Features.Relations(),
Features.SemanticRoles(limit=1)
])
return response
def handle_message(conversation_client, slack_client, workspace_id, context,
message, channel, user):
"""Handler for messages coming from Watson Conversation using context.
Fields in context will trigger various actions in this application.
:param str message: text from UI
:param SlackSender sender: used for send_message, hard-coded as Slack
:returns: True if UI input is required, False if we want app
processing and no input
:rtype: Bool
"""
global gv_nlu, gv_cortical_client, gv_bot_deafault_channel_name, gv_bot_deafault_channel_id, gv_ai
url_list = []
response = ""
cortical_response_text = ""
nlu_analyzed_text = ""
nlu_responce_text = ""
nlu_keyword = None
nlu_entities = None
context = None
# extract URLs from the message of the post
url_list = get_urls(slack_client, message)
if url_list is not None:
# send the message to user indicating that teh process of analysis started
slack_client.api_call("chat.postMessage",
channel=channel,
text="analyzing . . . ",
as_user=True)
for i in range(len(url_list)):
try:
# Analyze the URL article using WATSON Natural Language Understanding
nlu_response = gv_nlu.analyze(url=url_list[i],
return_analyzed_text=True,
features=[
features.Categories(),
features.Concepts(),
features.Emotion(),
features.Entities(),
features.Keywords(),
features.MetaData(),
features.Relations(),
features.Sentiment()
])
# get information from JSON format resulted by NLU
nlu_responce_text, nlu_sentiment, nlu_categoties, nlu_entities, nlu_keyword, nlu_concepts, nlu_analyzed_text = convert_nlujson(
url_list[i], nlu_response)
except WatsonException:
# print(json.dumps(nlu_response, indent=2))
nlu_responce_text = "Sentiments can not be retrieved from the URL"
# performs CORTICAL SEMANTIC analysis and returns results as a response text
cortical_response_text = cortical_analyze(nlu_analyzed_text,
nlu_keyword,
nlu_entities)
# build response text
title = "\n\n\n ===== Watson Sentiment Analysis =====\n"
response = response + title + nlu_responce_text + cortical_response_text ## Uncomment to add URL to the response text <+ url_list[i]>
i = i + 1
else:
response = "No valid URL found !!!"
return response
natural_language_understanding = NaturalLanguageUnderstandingV1(
username="******",
password="******",
version="2017-02-27")
with open('IBMjson', 'w') as ibmoutfile:
try:
response = natural_language_understanding.analyze(
url=qqll,
features=[
Features.Emotion(),
Features.Sentiment(),
Features.Concepts(limit=1),
Features.Keywords(limit=1, sentiment=False, emotion=False),
Features.Categories(),
Features.Entities(limit=1, sentiment=False, emotion=False),
Features.MetaData()
]
)
#print(json.dumps(response, indent=2))
json.dump(response, ibmoutfile)
except WatsonException as e: # This is the correct syntax
json.dump(qqll, ibmoutfile)
#==============================================================================
# URL
#==============================================================================
#==============================================================================
# Leesbaarheid APi
#==============================================================================
def test_metadata(self):
m = features.MetaData()
assert(m.name() == 'metadata')
def handle_message(conversation_client, slack_client, workspace_id, context,
message, channel, user):
"""Handler for messages coming from Watson Conversation using context.
Fields in context will trigger various actions in this application.
:param str message: text from UI
:param SlackSender sender: used for send_message, hard-coded as Slack
:returns: True if UI input is required, False if we want app
processing and no input
:rtype: Bool
"""
global gv_nlu, gv_cortical_client, gv_bot_deafault_channel_name, gv_bot_deafault_channel_id
url_list = []
response = ""
cortical_response_text = ""
nlu_analyzed_text = ""
nlu_responce_text = ""
nlu_keyword = None
nlu_entities = None
context = None
# extract URLs from the message of the post
url_list = get_urls(slack_client, message)
if url_list is not None:
# send the message to user indicating that teh process of analysis started
slack_client.api_call("chat.postMessage",
channel=channel,
text="analyzing . . . ",
as_user=True)
for i in range(len(url_list)):
try:
# Analyze the URL article using WATSON Natural Language Understanding
nlu_response = gv_nlu.analyze(url=url_list[i],
return_analyzed_text=True,
features=[
features.Categories(),
features.Concepts(),
features.Emotion(),
features.Entities(),
features.Keywords(),
features.MetaData(),
features.Relations(),
features.Sentiment()
])
# get information from JSON format resulted by NLU
nlu_responce_text, nlu_sentiment, nlu_categoties, nlu_entities, nlu_keyword, nlu_concepts, nlu_analyzed_text = convert_nlujson(
url_list[i], nlu_response)
except WatsonException:
# print(json.dumps(nlu_response, indent=2))
nlu_responce_text = "Sentiments can not be retrieved from the URL"
# performs CORTICAL SEMANTIC analysis and returns results as a response text
cortical_response_text = cortical_analyze(nlu_analyzed_text,
nlu_keyword,
nlu_entities)
# build response text
title = "\n\n\n ===== Watson Sentiment Analysis =====\n"
response = url_list[
i] + title + nlu_responce_text + cortical_response_text
# slack_client.api_call("chat.postMessage", channel=gv_bot_deafault_channel_id, text=response, as_user=True) ## uncomment to post responses at Default channel
slack_client.api_call(
"chat.postMessage",
channel=channel,
text=response,
as_user=True
) ## uncomment to post responses at the sender's channel
i = i + 1
# post receipt of the messate on the channel if it is not recevied from the default channel
# if channel != gv_bot_deafault_channel_id : ## uncomment to send receipt of the url to the sender
# slack_client.api_call("chat.postMessage", channel=channel, text="Thanks, new post has been received !!!", as_user=True)
else:
slack_client.api_call(
"chat.postMessage",
channel=channel,
text=
"No URL found!!!. \nI am trained to read text from URL, conduct sentiment analysis and classify it using semantic comparison with points of interests: Technologies, Companies and Interests",
as_user=True)
return True
def eval_metadata():
response = nlu.analyze(
url='https://www.ibm.com/blogs/think/2017/01/cognitive-grid/',
features=[features.MetaData()])
return jsonify(response)
def main(args):
natural_language_understanding = NaturalLanguageUnderstandingV1(
version='2017-02-27',
username='******',
password='******')
features_list = [
features.Categories(),
features.Concepts(),
features.Emotion(),
features.Entities(),
features.Keywords(),
features.MetaData(),
features.Relations(),
features.Sentiment()
]
input_param_list = [
'Categories', 'Concepts', 'Emotion', 'Entities', 'Keywords',
'MetaData', 'Relations', 'Sentiment'
]
input_param = args.get("type", "Emotion")
response = natural_language_understanding.analyze(
text=args.get("text", None),
url=args.get("url", None),
html=args.get("html", None),
features=[features_list[input_param_list.index(input_param)]])
if (args.get("type", "Emotion") == "Emotion"):
result = emotion2result(response)
return result
if (args.get("type", "Emotion") == "Categories"):
result = categories2result(response)
return result
if (args.get("type", "Emotion") == "Concepts"):
result = concepts2result(response)
return result
if (args.get("type", "Emotion") != "Entities"):
return response
itemlist = dic2item(response)
wiki_query = "http://en.wikipedia.org/w/api.php?action=query&" \
"prop=extracts&format=json&exintro=&titles="
count = 0
index = 0
extractlist = {}
while (count < 3 and index < len(itemlist)):
temp = itemlist[index][0].encode("utf8")
item = temp.split(" ")
string = ""
for i in item:
string += i + "+"
string = string[:len(string) - 1]
res = try_url(wiki_query + string)
# print res
res_json = json.loads(res)
extract = res_json["query"]["pages"]
pagenum = extract.keys()[0]
if (pagenum != "-1"):
count += 1
extract = extract[pagenum]
extract = extract["extract"]
extract = extract.encode("utf8")
slist = extract.split(". ")
if (slist is not None):
extract = slist[0] + "."
extract = clean(extract)
extractlist[itemlist[index][0]] = extract
index += 1
if (extractlist == {}):
return {"NULL": "NULL"}
return extractlist