def post(self):
parser = reqparse.RequestParser()
parser.add_argument("text", location="form", required=True)
args = parser.parse_args()
emotion = comment_emotions.emotions(args["text"], g)
sentic_values = emotion.get_all_sentic_values()
compound_emotions = emotion.get_compound_emotion()
sentic_values = [e.name for e in sentic_values if e is not None]
compound_emotions = [(e.name, v.name) for e, v in compound_emotions]
return {"sentic_values": sentic_values, "compound_emotions": compound_emotions}
def calculateVectorsForAllComments(dictFromJSON, g):
compound_emotion_dict = collections.defaultdict(int)
sentic_emotion_dict = collections.defaultdict(int)
processed_comments = list()
overall_rating = 0.0
# the product model from the DB
product = queries.find_media_by_asin(dictFromJSON["asin"])
tokenized_docs = buildListOfTokenizedDocuments(dictFromJSON)
for comment in dictFromJSON["comments"]:
vectorized_comment = calculateVector(tokenizeDocument(comment["text"]), tokenized_docs)
vectorized_desc = calculateVector(tokenizeDocument(dictFromJSON["description"]), tokenized_docs)
comment["vector_space"] = vectorized_comment
relevancy = getCosine(vectorized_comment, vectorized_desc)
if relevancy < 0.15:
continue
comment["relevancy"] = relevancy
# add emotional score
try:
comment_emotion = emotions(comment["text"], g)
except ConceptError:
print("Not enough concepts to do anything useful - skipping this product")
continue
comment["emotion_vector"] = comment_emotion.emotion_vector
compound_emotions = comment_emotion.get_compound_emotion()
sentic_values = comment_emotion.get_all_sentic_values()
sentic_values = [value.name for value in sentic_values if value is not None]
compound_emotions_list = []
for compound_emotion, strength in compound_emotions:
compound_emotions_list.append(
{"compound_emotion": compound_emotion.name, "strength": strength.name}
)
comment["compound_emotions"] = compound_emotions_list
comment["sentic_emotions"] = sentic_values
# add all compound_emotions to the default dictFromJSON
for compound in comment["compound_emotions"]:
compound_emotion_dict[compound["compound_emotion"]] += 1
for sentic in comment["sentic_emotions"]:
sentic_emotion_dict[sentic] += 1
overall_rating += float(comment["rating"])
# add the comment to the database
# insert_comment(item_id, relevancy, pleasantness, attention, sensitivity, aptitude, polarity, date)
queries.insert_comment(product.media_id,
comment["unixtime"],
comment["relevancy"],
comment["emotion_vector"]["pleasantness"],
comment["emotion_vector"]["attention"],
comment["emotion_vector"]["sensitivity"],
comment["emotion_vector"]["aptitude"],
comment["emotion_vector"]["polarity"])
comment["text"] = html.unescape(comment["text"])
processed_comments.append(comment)
popular_compound_emotions = []
if len(compound_emotion_dict) == 0:
raise NoEmotionsFoundError("No compound emotions found")
for i in range(0, 3):
popular_emotion = max(compound_emotion_dict, key=compound_emotion_dict.get)
popular_compound_emotions.append(popular_emotion)
compound_emotion_dict.pop(popular_emotion)
popular_sentic_emotions = []
if len(sentic_emotion_dict) == 0:
raise NoEmotionsFoundError("No sentic emotions found")
for i in range(0, 3):
popular_sentic = max(sentic_emotion_dict, key=sentic_emotion_dict.get)
popular_sentic_emotions.append(popular_sentic)
sentic_emotion_dict.pop(popular_sentic)
# add emotion to the database
queries.insert_media_emotion(product.media_id, popular_sentic)
dictFromJSON["popular_compound_emotions"] = popular_compound_emotions
dictFromJSON["popular_sentic_emotions"] = popular_sentic_emotions
if len(processed_comments) > 0:
rating = overall_rating / len(processed_comments)
rating = float("{0:.2f}".format(rating))
dictFromJSON["overall_rating"] = rating
dictFromJSON["comments"] = sort_list_of_dicts(processed_comments)
# get rid of escaped html characters in description
dictFromJSON["description"] = html.unescape(dictFromJSON["description"])
return dictFromJSON
import json, rdflib, sys, os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from emotion_processing.comment_emotions import emotions, ConceptError
with open('samples.json', 'r') as samples:
data = json.loads(samples.read())
with open('../../senticnet3.rdf.xml', 'r') as f:
print('spinning up db...')
g = rdflib.Graph()
g.parse(f)
for comment in data['comments']:
try:
emotion = emotions(comment, g)
sentics = emotion.get_all_sentic_values()
compounds = emotion.get_compound_emotion()
print(comment[0:60])
print('\temotion vector:')
for emo in emotion.emotion_vector:
print('\t\t{}: {}'.format(emo, emotion.emotion_vector[emo]))
print('\tsentic values:')
for sentic in sentics:
if sentic is None:
print('\t\tnone')
else:
print('\t\t{}'.format(sentic.name))
