def convert(
self,
analyzer_response: TextPayload,
base_payload: Optional[Dict[str, Any]] = None,
**kwargs,
) -> Dict[str, Any]:
base_payload = base_payload or {}
merged_dict = {**base_payload, **analyzer_response.to_dict()}
return flatten_dict(merged_dict)
def convert(
self,
analyzer_response: AnalyzerResponse,
base_payload: Optional[Dict[str, Any]] = None,
**kwargs,
) -> Dict[str, Any]:
request_payload = base_payload or {}
if analyzer_response.source_name != "Twitter":
return {**request_payload, **analyzer_response.to_dict()}
source_information = kwargs["source_information"]
user_url = ""
positive = 0.0
negative = 0.0
text = ""
tweet_id = None
created_at_str = None
classification_list = []
flat_dict = flatten_dict(analyzer_response.to_dict())
for k, v in flat_dict.items():
if "username" in k:
user_url = TWITTER_URL_PREFIX + v
elif "text" in k:
text = str(v).replace("\n", " ")
elif "positive" in k:
positive = float(v)
elif "negative" in k:
negative = float(v)
elif "meta_id" in k:
tweet_id = v
elif "created_at" in k:
created_at_str = v
elif "segmented_data" in k and len(classification_list) < 2:
classification_list.append(k.rsplit("_", 1)[1])
if created_at_str:
created_at = parser.isoparse(created_at_str)
created_at_str = (
created_at.replace(tzinfo=timezone.utc)
.astimezone(tz=IST_TZ)
.strftime("%Y-%m-%d %H:%M:%S")
)
tweet_url = user_url + "/status/" + tweet_id
# Sentiment rules
if negative > 8.0:
sentiment = "Strong Negative"
elif 0.3 < negative <= 8.0:
sentiment = "Negative"
elif positive >= 0.8:
sentiment = "Strong Positive"
elif 0.4 < positive < 0.8:
sentiment = "Positive"
else:
sentiment = "Neutral"
enquiry = {
"Source": source_information,
"FeedbackBy": user_url,
"Sentiment": sentiment,
"TweetUrl": tweet_url,
"FormattedText": text,
"PredictedCategories": ",".join(classification_list),
}
if created_at_str:
enquiry["ReportedAt"] = created_at_str
kv_str_list = [k + ": " + str(v) for k, v in enquiry.items()]
request_payload["enquiryMessage"] = "\n".join(kv_str_list)
return request_payload
def convert(
self,
analyzer_response: TextPayload,
base_payload: Optional[Dict[str, Any]] = None,
**kwargs,
) -> Dict[str, Any]:
request_payload = base_payload or {}
use_enquiry_api = kwargs.get("use_enquiry_api", False)
if analyzer_response.source_name != "Twitter":
return {**request_payload, **analyzer_response.to_dict()}
source_information = kwargs["source_information"]
partner_id = kwargs["partner_id"]
user_url = ""
positive = 0.0
negative = 0.0
text = ""
tweet_id = None
created_at_str = None
classification_list: List[str] = []
flat_dict = flatten_dict(analyzer_response.to_dict())
for k, v in flat_dict.items():
if "username" in k:
user_url = TWITTER_URL_PREFIX + v
elif "text" in k:
text = str(v).replace("\n", " ")
elif "positive" in k:
positive = float(v)
elif "negative" in k:
negative = float(v)
elif "meta_id" in k:
tweet_id = v
elif "created_at" in k:
created_at_str = v
elif "segmented_data" in k and len(classification_list) < 2:
classification_list.append(k.rsplit("_", 1)[1])
created_at_str_parsed: Optional[str] = None
if created_at_str:
created_at = parser.isoparse(created_at_str)
created_at_str_parsed = (created_at.replace(
tzinfo=timezone.utc).astimezone(
tz=IST_TZ).strftime("%Y-%m-%d %H:%M:%S"))
tweet_url = f"{user_url}/status/{tweet_id}"
# Sentiment rules
if negative > 8.0:
sentiment = "Strong Negative"
elif 0.3 < negative <= 8.0:
sentiment = "Negative"
elif positive >= 0.8:
sentiment = "Strong Positive"
elif 0.4 < positive < 0.8:
sentiment = "Positive"
else:
sentiment = "Neutral"
if use_enquiry_api:
enquiry = {
"Source": source_information,
"FeedbackBy": user_url,
"Sentiment": sentiment,
"TweetUrl": tweet_url,
"FormattedText": text,
"PredictedCategories": ",".join(classification_list),
}
if created_at_str_parsed is not None:
enquiry["ReportedAt"] = created_at_str_parsed
kv_str_list = [k + ": " + str(v) for k, v in enquiry.items()]
request_payload["enquiryMessage"] = "\n".join(kv_str_list)
else:
message = {
"message":
text,
"partnerId":
partner_id,
"query":
source_information,
"source":
analyzer_response.source_name,
"url":
tweet_url,
"userProfile":
user_url,
"sentiment":
sentiment,
"predictedCategories":
",".join(classification_list),
"metadata":
str(
json.dumps(analyzer_response.segmented_data,
ensure_ascii=False)),
"originatedAt":
created_at_str,
}
request_payload["messageDetail"] = str(
json.dumps(message, ensure_ascii=False))
return request_payload