def analyzeText(text, model, num_cats):
global index_count
print("Classifying text #%s" % str(index_count))
# this is where we are going to store our results
formatted_categories = ""
try:
# We are going to make a request to the Deep Categorization API
request = meaningcloud.DeepCategorizationRequest(
license_key, model=model, txt=text, server=server
)
setRequestSource(request)
response = meaningcloud.DeepCategorizationResponse(request.sendReq())
if response.isSuccessful():
categories = response.getCategories()
formatted_categories = [
response.getCategoryLabel(cat) for cat in categories[:num_cats]
]
else:
if isBlockingErrorType(response.getStatusCode()):
raise ValueError(
"Something went wrong in the MeaningCloud request!: ("
+ response.getStatusCode()
+ ") "
+ response.getStatusMsg()
)
else:
print(
"Oops! The request to Deep Categorization for text #"
+ str(index_count)
+ " was not succesful: ("
+ response.getStatusCode()
+ ") "
+ response.getStatusMsg()
)
formatted_categories = [
"ERROR ("
+ response.getStatusCode()
+ "): "
+ response.getStatusMsg()
]
except ValueError as e:
raise ValueError(str(e))
index_count += 1
formatted_categories = formatted_categories + [""] * (
num_cats - len(formatted_categories)
)
return pd.Series(formatted_categories)
def analyzeText(text):
global index_count
print("Identifying language for text #%s" % str(index_count))
# this is where we are going to store our results
language_code = ""
language_name = ""
try:
# We are going to make a request to the Language Identification API
request = meaningcloud.LanguageRequest(license_key, txt=text, server=server)
setRequestSource(request)
response = meaningcloud.LanguageResponse(request.sendReq())
if response.isSuccessful():
lang = response.getFirstLanguage()
if lang:
language_code = response.getLanguageCode(lang)
language_name = response.getLanguageName(lang)
else:
if isBlockingErrorType(response.getStatusCode()):
raise ValueError(
"Something went wrong in the MeaningCloud request!: ("
+ response.getStatusCode()
+ ") "
+ response.getStatusMsg()
)
else:
print(
"Oops! The request to Language Identification for text #"
+ str(index_count)
+ " was not succesful: ("
+ response.getStatusCode()
+ ") "
+ response.getStatusMsg()
)
language_code = (
"ERROR ("
+ response.getStatusCode()
+ "): "
+ response.getStatusMsg()
)
except ValueError as e:
raise ValueError(str(e))
index_count += 1
return pd.Series([language_code, language_name])
def analyzeText(text, lang):
global index_count
print("Analyzing sentiment for text #%s" % str(index_count))
# this is where we are going to store our results
polarity = ""
subjectivity = ""
irony = ""
agreement = ""
confidence = ""
try:
# We are going to make a request to the Sentiment Analysis API
request = meaningcloud.SentimentRequest(license_key,
lang=lang,
txt=text,
server=server)
setRequestSource(request)
response = meaningcloud.SentimentResponse(request.sendReq())
if response.isSuccessful():
polarity = response.scoreTagToString(response.getGlobalScoreTag())
subjectivity = response.getSubjectivity()
irony = response.getIrony()
agreement = response.getGlobalAgreement()
confidence = response.getGlobalConfidence()
else:
if isBlockingErrorType(response.getStatusCode()):
raise ValueError(
"Something went wrong in the MeaningCloud request!: (" +
response.getStatusCode() + ") " + response.getStatusMsg())
else:
print("Oops! The request to Sentiment Analysis for text #" +
str(index_count) + " was not succesful: (" +
response.getStatusCode() + ") " +
response.getStatusMsg())
polarity = ("ERROR (" + response.getStatusCode() + "): " +
response.getStatusMsg())
except ValueError as e:
raise ValueError(str(e))
index_count += 1
return pd.Series([polarity, subjectivity, irony, agreement, confidence])
def analyzeText(text):
global index_count
print("Extracting summary for text #%s" % str(index_count))
# this is where we are going to store our results
summary = ""
try:
# We are going to make a request to the Summarization API
request = meaningcloud.SummarizationRequest(license_key,
sentences=sentences,
txt=text,
server=server)
setRequestSource(request)
response = meaningcloud.SummarizationResponse(request.sendReq())
if response.isSuccessful():
summary = response.getSummary()
else:
if isBlockingErrorType(response.getStatusCode()):
raise ValueError(
"Something went wrong in the MeaningCloud request!: (" +
response.getStatusCode() + ") " + response.getStatusMsg())
else:
print("Oops! The request to Summarization for text #" +
str(index_count) + " was not succesful: (" +
response.getStatusCode() + ") " +
response.getStatusMsg())
summary = ("ERROR (" + response.getStatusCode() + "): " +
response.getStatusMsg())
except ValueError as e:
raise ValueError(str(e))
index_count += 1
return pd.Series([summary])
def analyzeText(text, language, threshold, tt, ud):
global index_count
print("Extracting topics for text #%s" % str(index_count))
# this is where we are going to store our results
topics = {
"person": [],
"organization": [],
"location": [],
"product": [],
"id": [],
"event": [],
"other": [],
"quantity": [],
}
try:
# We are going to make a request to the Topics Extraction API
request = meaningcloud.TopicsRequest(
license_key,
txt=text,
lang=language,
topicType=tt,
server=server,
otherparams={"ud": ud},
)
setRequestSource(request)
response = meaningcloud.TopicsResponse(request.sendReq())
if response.isSuccessful():
if "e" in tt:
entity_list = response.getEntities()
if entity_list:
for entity in entity_list:
if int(response.getTopicRelevance(entity)) >= threshold:
first_node = response.getTypeFirstNode(
response.getOntoType(entity)
).lower()
form = str(response.getTopicForm(entity))
insertInList(topics.get("other"), form) if topics.get(
first_node
) is None else insertInList(topics.get(first_node), form)
if "c" in tt:
concept_list = response.getConcepts()
if concept_list:
for concept in concept_list:
if int(response.getTopicRelevance(concept)) >= threshold:
first_node = response.getTypeFirstNode(
response.getOntoType(concept)
).lower()
form = str(response.getTopicForm(concept))
insertInList(topics.get("other"), form) if topics.get(
first_node
) is None else insertInList(topics.get(first_node), form)
if "m" in tt:
money_expression_list = response.getMoneyExpressions()
if money_expression_list:
[
insertInList(
topics.get("quantity"), str(response.getTopicForm(money))
)
for money in money_expression_list
]
if "n" in tt:
quantity_expression_list = response.getQuantityExpressions()
if quantity_expression_list:
[
insertInList(
topics.get("quantity"), str(response.getTopicForm(quantity))
)
for quantity in quantity_expression_list
]
else:
if isBlockingErrorType(response.getStatusCode()):
raise ValueError(
"Something went wrong in the MeaningCloud request!: ("
+ response.getStatusCode()
+ ") "
+ response.getStatusMsg()
)
else:
print(
"Oops! The request to Topics Extraction for text #"
+ str(index_count)
+ " was not succesful: ("
+ response.getStatusCode()
+ ") "
+ response.getStatusMsg()
)
topics = {
"person": "ERROR ("
+ response.getStatusCode()
+ "): "
+ response.getStatusMsg(),
"organization": "",
"location": "",
"product": "",
"id": "",
"event": "",
"other": "",
"quantity": "",
}
except ValueError as e:
raise ValueError(str(e))
index_count += 1
return pd.Series(topics)