diff --git a/ADA/SA/bert_analyzer.py b/ADA/SA/bert_analyzer.py
index d472fcb39bc87fa626106ea9ca4298caac3e44e5..afd6b997bdb0351add4ef357562dab6c76332d8e 100644
--- a/ADA/SA/bert_analyzer.py
+++ b/ADA/SA/bert_analyzer.py
@@ -12,7 +12,7 @@ import shap
semeval_2014_train_path = 'data/SemEval-2014/Laptop_Train_v2.xml'
semeval_2014_test_path = 'data/SemEval-2014/Laptops_Test_Gold.xml'
-amazon_test_path = 'data/Amazon/amazon_camera_test.xml'
+amazon_test_path = 'data/Amazon/annotated_amazon_laptop_reviews.xml'
trained_model_path = 'semeval_2014_2.pt'
BATCH_SIZE = 32
@@ -26,6 +26,12 @@ def loss(outputs, labels):
class BertAnalyzer:
+ @staticmethod
+ def default():
+ sa = BertAnalyzer()
+ sa.load_saved('semeval_2014.pt')
+ return sa
+
def load_saved(self, path):
self.net = TDBertNet(len(polarity_indices))
self.net.load_state_dict(torch.load(path))
@@ -93,7 +99,7 @@ class BertAnalyzer:
f1 = metrics.f1_score(truths, predicted, labels=range(len(polarity_indices)), average='macro')
print('macro F1:', f1)
- def analyze_sentence(self, text, char_from, char_to):
+ def get_sentiment_polarity(self, text, char_from, char_to):
instance = Instance(text, char_from, char_to)
tokens, tg_from, tg_to = instance.get()
text, target_indices = instance.to_tensor()
@@ -116,10 +122,18 @@ class BertAnalyzer:
# ax.set_xticklabels(tokens, rotation=45, rotation_mode='anchor', ha='right')
# plt.show()
- _, pred = torch.max(outputs.data, 1)
- return pred
-
-
-sentiment_analyzer = BertAnalyzer()
-sentiment_analyzer.load_saved('semeval_2014.pt')
-print(sentiment_analyzer.analyze_sentence("Well built laptop with win7.", 11, 17))
\ No newline at end of file
+ val, pred = torch.max(outputs.data, 1)
+ if pred == 0:
+ # positive
+ return val
+ elif pred == 1:
+ # negative
+ return -val
+ else:
+ # neutral or conflicted
+ return 0
+
+
+sentiment_analyzer = BertAnalyzer.default()
+sentiment_analyzer.evaluate(semeval_2014_test_path)
+sentiment_analyzer.evaluate(amazon_test_path)
diff --git a/ADA/SA/sentiment_analyzer.py b/ADA/SA/sentiment_analyzer.py
index 54ae37ceb3f2d7eec65fc23f2346e75f930dd3d4..e4311d33f3c4fca366772a2c84d1e9580d86705f 100644
--- a/ADA/SA/sentiment_analyzer.py
+++ b/ADA/SA/sentiment_analyzer.py
@@ -11,6 +11,7 @@ from sklearn.feature_extraction.text import CountVectorizer
import os
import math
+
def resample_data(instances, labels):
label_instances = {label: [instance for instance in instances if instance.opinion == label] for label in labels}
max_n_instances = max([len(v) for v in label_instances.values()])
@@ -22,6 +23,7 @@ def resample_data(instances, labels):
print(len(resampled_data))
return resampled_data
+
class SentimentAnalyzer:
expr_clf = svm.SVC() # determines if sentence expresses sentiment towards ARG
@@ -39,10 +41,11 @@ class SentimentAnalyzer:
def expresses_sentiment(self, instances):
return self.expr_clf.predict([instance.vector for instance in instances])
+
semeval_2014_train_path = 'data/SemEval-2014/SemEval_2014_Laptop_Train_with_labelled_parse_trees.xml'
semeval_2014_test_path = 'data/SemEval-2014/SemEval_2014_Laptop_Test_with_labelled_parse_trees.xml'
amazon_train_path = 'data/Amazon/amazon_camera_train.xml'
-amazon_test_path = 'data/Amazon/amazon_camera_test2.xml' # 'data/Amazon/prepared_amazon_camera_reviews.xml'
+amazon_test_path = 'data/Amazon/amazon_camera_test2.xml' # 'data/Amazon/prepared_amazon_camera_reviews.xml'
semeval_train_path = 'data/SemEval-2016/ABSA16_Laptops_Train_SB1_v2_with_labelled_parse_trees.xml'
semeval_test_path = 'data/SemEval-2016/ABSA16_Laptops_Test_with_labelled_parse_trees.xml' #
tweet_train_path = 'data/acl-14-short-data/tweet_train_with_labelled_parse_trees.xml'
@@ -56,7 +59,7 @@ sa = SentimentAnalyzer()
train_tree = ET.parse(train_path)
train_instances = [Instance(instance) for instance in train_tree.getroot()]
-train_instances = resample_data(train_instances, labels)
+# train_instances = resample_data(train_instances, labels)
# create and train vectorizer model
vec = Vectorizer(train_instances)
diff --git a/ADA/SA/vectorizer.py b/ADA/SA/vectorizer.py
index 7d24e18c0db57e12f5e93ee847ebdbe57f1f4ae5..45835316f60bc7ad45a6da19f64249c4643df340 100644
--- a/ADA/SA/vectorizer.py
+++ b/ADA/SA/vectorizer.py
@@ -17,7 +17,7 @@ class Vectorizer:
self.transformer = TfidfTransformer()
# indep features:
- self.bow_vectorizer = CountVectorizer(stop_words='english', ngram_range=(1,2))
+ self.bow_vectorizer = CountVectorizer(stop_words='english', ngram_range=(1,5))
texts = [instance.text for instance in train_instances]
train_bow_vectors = self.bow_vectorizer.fit_transform(texts).toarray()
train_sent_vectors = [self.sentiment_scores(instance) for instance in train_instances]
diff --git a/ADA/agent.py b/ADA/agent.py
index e908879510e50c82f0972d00897c179c72e91d33..10410f670b61a47f6792488f21ca832043cd21ea 100644
--- a/ADA/agent.py
+++ b/ADA/agent.py
@@ -5,11 +5,12 @@ from anytree import PostOrderIter
import pickle
from argument import *
from functools import reduce
-
+from SA.bert_analyzer import BertAnalyzer
class Agent:
sentiment_threshold = 0.95
review_tokenizer = ReviewTokenizer()
+ bert_analyzer = BertAnalyzer.default()
def __init__(self):
# load classifier
@@ -27,7 +28,7 @@ class Agent:
return phrases
# analyze sentiment
- def get_sentiment(self, phrase):
+ def get_bayes_sentiment(self, phrase):
# get classification
tokens = self.review_tokenizer.tokenize_review(phrase)
prob_classification = self.classifier.prob_classify(dict([token, True] for token in tokens))
@@ -35,6 +36,9 @@ class Agent:
strength = (prob_classification.prob(classification) - 0.5) * 2
return strength if classification == '+' else -strength
+ def get_bert_sentiment(self, text, char_from, char_to):
+ return self.bert_analyzer.get_sentiment_polarity(text, char_from, char_to)
+
# remove all ancestors of node in list l
def remove_ancestors(self, node, l):
if node.parent != None:
@@ -51,8 +55,9 @@ class Agent:
while len(arguments) > 0:
f = arguments.pop(0)
for word in glossary[f]:
- if word in phrase:
- argument_matches.append(f)
+ matches = [(f, m.start(), m.end()) for m in re.finditer(word, phrase)]
+ if matches:
+ argument_matches += matches
self.remove_ancestors(f, arguments)
break
return argument_matches
@@ -61,17 +66,16 @@ class Agent:
votes = {}
vote_phrases = {}
for phrase in phrases:
- arguments = self.get_arguments(phrase)
- sentiment = self.get_sentiment(phrase)
- if abs(sentiment) > self.sentiment_threshold:
- for argument in arguments:
+ for argument, start, end in self.get_arguments(phrase):
+ sentiment = self.get_bayes_sentiment(phrase) # self.get_bert_sentiment(phrase, start, end)
+ if abs(sentiment) > self.sentiment_threshold:
if (argument not in votes) or (abs(votes[argument]) < abs(sentiment)):
votes[argument] = sentiment # what if there's two phrases with same argument?
vote_phrases[argument] = {'phrase': phrase, 'sentiment': sentiment}
# normalize votes to 1 (+) or -1 (-)
for argument in votes:
votes[argument] = 1 if votes[argument] > 0 else -1
- return (votes, vote_phrases)
+ return votes, vote_phrases
# augment votes (Definition 4.3) obtained for a single critic
def augment_votes(self, votes):
diff --git a/ADA/DataLoader.py b/ADA/dataloader.py
similarity index 100%
rename from ADA/DataLoader.py
rename to ADA/dataloader.py
index 1b0dc663fe8947ed2937dbff612cffd246a75280..33560bf95b0035541b24f1a8cc82611498785998 100644
--- a/ADA/DataLoader.py
+++ b/ADA/dataloader.py
@@ -1,7 +1,7 @@
import pandas as pd
-class DataLoader:
+class DataLoader:
data_location = 'camera_prepared_data.tsv'
reviews = pd.read_csv(data_location, sep='\t', error_bad_lines=False)
diff --git a/ADA/server/ios_server/views.py b/ADA/server/ios_server/views.py
index b58d1508d26735dd3d9bc63e044c55420701cc43..691e6332e8b2dbcd026a1ccb9083b777a26ea4c5 100644
--- a/ADA/server/ios_server/views.py
+++ b/ADA/server/ios_server/views.py
@@ -4,7 +4,7 @@ import jsonpickle
from django.views.decorators.csrf import csrf_exempt
import sys
-sys.path.append('/Users/joeloksanen/individual_project/ADA')
+sys.path.append('/home/joel/individual_project/ADA')
from dataloader import DataLoader
from communicator import Communicator
diff --git a/ADA/server/server/settings.py b/ADA/server/server/settings.py
index a5931b845f9a8010f9e24472864cdc9db7699d16..f6446479f3aedf54b36354dc442c801d0a3cdf48 100644
--- a/ADA/server/server/settings.py
+++ b/ADA/server/server/settings.py
@@ -25,7 +25,7 @@ SECRET_KEY = 'z)tj_b=**v@b5-l6s!$*+_0=nzmor8dc#y$-%4%45kt8e8q@-f'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
-ALLOWED_HOSTS = ['192.168.0.13', '146.169.222.109', '146.169.218.37']
+ALLOWED_HOSTS = ['192.168.1.104']
# Application definition