Changed review annotation to arrow key based

ADA/SA/bert_analyzer.py

@@ -28,8 +28,8 @@ class BertAnalyzer:
         self.net.load_state_dict(torch.load(trained_model_path))
         self.net.eval()
 
-    def train(self):
-        train_data = BertDataset(semeval_2014_train_path)
+    def train(self, dataset):
+        train_data = BertDataset(dataset)
         train_loader = DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=4,
                                   collate_fn=generate_batch)
 
@@ -65,8 +65,8 @@ class BertAnalyzer:
 
         torch.save(net.state_dict(), trained_model_path)
 
-    def evaluate(self):
-        test_data = BertDataset(semeval_2014_test_path)
+    def evaluate(self, dataset):
+        test_data = BertDataset(dataset)
         test_loader = DataLoader(test_data, batch_size=BATCH_SIZE, shuffle=False, num_workers=4,
                                  collate_fn=generate_batch)
 
@@ -93,4 +93,4 @@ class BertAnalyzer:
 
 sentiment_analyzer = BertAnalyzer()
 sentiment_analyzer.load_saved()
-sentiment_analyzer.evaluate()
+sentiment_analyzer.evaluate(semeval_2014_test_path)
\ No newline at end of file

ADA/review_annotation.py

@@ -8,6 +8,9 @@ import nltk.data
 from stanfordcorenlp import StanfordCoreNLP
 from nltk.tree import ParentedTree as Tree
 import re
+import readchar
+from sty import fg, bg, ef, rs
+from wcwidth import wcswidth
 
 data_location = 'amazon_reviews_us_Camera_v1_00.tsv'
 selected_reviews_location = 'reviews_to_be_annotated.xml'
@@ -15,6 +18,7 @@ min_characters = 0
 max_characters = 200
 n = 500
 sentiment_mappings = {'+': 'positive', '0': 'neutral', '-': 'negative', 'c': 'conflict'}
+ann_bgs = {'positive': bg.green, 'neutral': bg.li_black, 'negative': bg.red, 'conflict': bg.yellow}
 annotated_reviews_location = 'annotated_camera_reviews.xml'
 included_labels = ['NN', 'NNS', 'NP', 'NNP', 'NNPS', 'DT', 'CD', 'FW', 'PRP$']
 nouns = ['NN', 'NNS', 'NP', 'NNP', 'NNPS']
@@ -23,6 +27,7 @@ prepared_reviews_location = 'prepared_amazon_camera_reviews.xml'
 tokenizer = TweetTokenizer()
 sent_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
 
+
 class bcolors:
     HEADER = '\033[95m'
     OKBLUE = '\033[94m'
@@ -33,6 +38,7 @@ class bcolors:
     BOLD = '\033[1m'
     UNDERLINE = '\033[4m'
 
+
 def get_leaf_indices(tree, phrase_tree):
     phrase_tree_pos = phrase_tree.treeposition()
     start = 0
@@ -43,26 +49,31 @@ def get_leaf_indices(tree, phrase_tree):
         end += 1
     return (start, end)
 
+
 # true if r1 contains r2
 def range_contains(r1, r2):
     return r1[0] <= r2[0] and r1[1] >= r2[1] and Tree.fromstring(r2[2]) in Tree.fromstring(r1[2]).subtrees()
 
+
 def in_range(r, n):
     return r[0] <= n and r[1] >= n
 
+
 # true if rs cover r
 def range_cover(r, rs):
-    for n in range(r[0],r[1]+1):
+    for n in range(r[0], r[1] + 1):
         if not any(in_range(other_r, n) for other_r in rs):
-             return False
+            return False
     return True
 
+
 def is_opinion_target(tree):
     return (tree.label() in included_labels and
             all(sub.label() in included_labels or
                 (sub.label() == 'PRP' and sub[0].lower() == 'it')
                 for sub in tree.subtrees()))
 
+
 def prepare_reviews():
     reviews = pd.read_csv(data_location, sep='\t', error_bad_lines=False)
 
@@ -71,13 +82,13 @@ def prepare_reviews():
 
     # try to filter out reviews for camera accessories
     filter_words = ['accessor', 'battery', 'charger', 'tripod', 'strap', 'case', 'bag',
-    'backpack', 'kit', 'printer', 'adapter', 'album', 'surveillance', 'security']
+                    'backpack', 'kit', 'printer', 'adapter', 'album', 'surveillance', 'security']
     filter_pat = ''
     for word in filter_words:
         word_filter = '[' + word[0].upper() + word[0].lower() + ']' + word[1:]
         filter_pat += word_filter + '|'
     filter_pat = filter_pat[:-1]
-    reviews = reviews[~reviews['product_title'].str.contains(pat = filter_pat, regex = True)]
+    reviews = reviews[~reviews['product_title'].str.contains(pat=filter_pat, regex=True)]
 
     # shuffle reviews
     reviews = reviews.sample(frac=1).reset_index(drop=True)
@@ -121,7 +132,7 @@ def prepare_reviews():
             parse_tree_node.text = parse_tree_str
 
             tokenized_text_node = SubElement(sentence_node, 'tokenized_text')
-            tokenized_text_node.text = ' '.join(parse_tree.leaves()).replace('``','""')
+            tokenized_text_node.text = ' '.join(parse_tree.leaves()).replace('``', '""')
 
     # save tree to file
     xmlstr = minidom.parseString(tostring(root)).toprettyxml(indent='   ')
@@ -131,6 +142,7 @@ def prepare_reviews():
 
     print('Obtained and parsed', len(reviews), 'reviews')
 
+
 def annotate_reviews():
     row_character_count = 100
     reviews = parse(selected_reviews_location)
@@ -143,69 +155,69 @@ def annotate_reviews():
 
     for review in not_annotated:
         for sentence in review.find('sentences'):
-            tokens = sentence.find('tokenized_text').text.split(' ')
-
-            os.system('clear')
-
-            print(bcolors.OKBLUE + '{} reviews annotated'.format(n_annotated) + bcolors.ENDC)
-            print('')
-
-            print(bcolors.OKBLUE + 'annotation: [\'i\'|\'n,m\'] [\'+\'|\'0\'|\'-\'|\'c\']' + bcolors.ENDC)
-            print(bcolors.OKBLUE + 'next:       \'n\'' + bcolors.ENDC)
-            print(bcolors.OKBLUE + 'skip:       \'s\'' + bcolors.ENDC)
-            print(bcolors.OKBLUE + 'quit:       \'q\'' + bcolors.ENDC)
-            print('')
-
-            product_title = review.find('product_title').text
-            print(bcolors.OKGREEN + product_title + bcolors.ENDC)
-            print('')
-
-            index_row = ''
-            text_row = ''
-            for t in range(len(tokens)):
-                space = len(tokens[t]) - len(str(t))
-
-                token_text = tokens[t] + ' '
-                index_text = str(t) + ' '
-                if space > 0:
-                    index_text =  ' ' * math.floor(space / 2) + index_text + ' ' * math.ceil(space / 2)
-                elif space < 0:
-                    space = abs(space)
-                    token_text =  ' ' * math.floor(space / 2) + token_text + ' ' * math.ceil(space / 2)
-
-                index_row += index_text
-                text_row += token_text
-                if t + 1 < len(tokens) and len(index_row) + len(tokens[t + 1]) + 1 > row_character_count:
-                    print(bcolors.WARNING + index_row + bcolors.ENDC)
-                    print(text_row)
-                    index_row = ''
-                    text_row = ''
-            print(bcolors.WARNING + index_row + bcolors.ENDC)
-            print(text_row)
-            print('')
+            text = sentence.find('text').text
+            cursor_pos = 0
+            start = None
+            end = None
 
             annotations = []
 
             while True:
-                task = input(': ')
-
-                if len(task.split(' ')) == 2:
-                    rng = None
-                    sentiment = ''
-
-                    fst = task.split(' ')[0]
-                    if fst.isdigit():
-                        rng = (int(fst), int(fst))
-                    elif (',' in fst and len(fst.split(',')) == 2 and
-                          fst.split(',')[0].isdigit() and fst.split(',')[1].isdigit()):
-                        rng = (int(fst.split(',')[0]), int(fst.split(',')[1]))
-
-                    snd = task.split(' ')[1]
-                    if snd in sentiment_mappings.keys():
-                        sentiment = snd
-
-                    if rng and sentiment:
-                        annotations.append((rng, sentiment))
+                os.system('clear')
+
+                print(bcolors.OKBLUE + '{} reviews annotated'.format(n_annotated) + bcolors.ENDC)
+                print('')
+
+                print(bcolors.OKBLUE + 'next:       \'n\'' + bcolors.ENDC)
+                print(bcolors.OKBLUE + 'skip:       \'s\'' + bcolors.ENDC)
+                print(bcolors.OKBLUE + 'quit:       \'q\'' + bcolors.ENDC)
+                print('')
+
+                product_title = review.find('product_title').text
+                print(bcolors.OKGREEN + product_title + bcolors.ENDC)
+                print('')
+
+                text_row = ''
+                for t in range(len(text)):
+                    char = text[t]
+                    if t == cursor_pos:
+                        char = bg.blue + char + bg.rs
+                    for ann in annotations:
+                        if t in range(ann[0][0], ann[0][1]):
+                            char = ann_bgs[ann[1]] + char + bg.rs
+                    text_row += char
+
+                    if (t + 1) % row_character_count == 0:
+                        print(text_row)
+                        text_row = ''
+                print(text_row)
+                print('')
+
+                task = readchar.readkey()
+
+                if task == readchar.key.RIGHT:
+                    cursor_pos = min(cursor_pos + 1, len(text) - 1)
+                if task == readchar.key.LEFT:
+                    cursor_pos = max(cursor_pos - 1, 0)
+                if task == readchar.key.DOWN:
+                    cursor_pos = min(cursor_pos + row_character_count, len(text) - 1)
+                if task == readchar.key.UP:
+                    cursor_pos = max(cursor_pos - row_character_count, 0)
+
+                if task == readchar.key.SPACE:
+                    if start == None:
+                        start = cursor_pos
+                    elif end == None and cursor_pos >= start:
+                        end = cursor_pos+1
+                        rng = (start, end)
+                        while True:
+                            inp = input('Sentiment for {},{}: '.format(start, end-1))
+                            if inp in sentiment_mappings.keys():
+                                annotations.append((rng, sentiment_mappings[inp]))
+                                start = None
+                                end = None
+                                cursor_pos = min(cursor_pos + 1, len(text) - 1)
+                                break
 
                 if task in ['n', 's', 'q']:
                     if task in ['n']:
@@ -216,7 +228,7 @@ def annotate_reviews():
                             range_node = SubElement(annotation_node, 'range')
                             range_node.text = '{},{}'.format(annotation[0][0], annotation[0][1])
                             sent_node = SubElement(annotation_node, 'sentiment')
-                            sent_node.text = sentiment_mappings[annotation[1]]
+                            sent_node.text = annotation[1]
                     break
             if task == 'q':
                 break
@@ -232,6 +244,7 @@ def annotate_reviews():
         with open(selected_reviews_location, 'w') as f:
             f.write(xmlstr)
 
+
 def longest_common_subsequence(x, y):
     seq = []
     for i in range(min(len(x), len(y))):
@@ -241,6 +254,7 @@ def longest_common_subsequence(x, y):
 
     return tuple(seq)
 
+
 def labelled_tree_str(tree_str, start, end):
     tree = Tree.fromstring(tree_str)
     start_pos = tree.leaf_treeposition(start)
@@ -248,7 +262,7 @@ def labelled_tree_str(tree_str, start, end):
 
     # find highest parent node common to start and end
     if start == end:
-        parent_pos = start_pos[:len(start_pos)-1]
+        parent_pos = start_pos[:len(start_pos) - 1]
     else:
         parent_pos = longest_common_subsequence(start_pos, end_pos)
     parent_node = tree[parent_pos]
@@ -257,7 +271,7 @@ def labelled_tree_str(tree_str, start, end):
         parent_pos = parent_pos[:len(parent_pos) - 1]
 
     # remove branches between start and end inclusive
-    child_index_rng = range(start_pos[len(parent_pos)], end_pos[len(parent_pos)]+1)
+    child_index_rng = range(start_pos[len(parent_pos)], end_pos[len(parent_pos)] + 1)
     child_positions = [list(parent_pos) + [i] for i in child_index_rng]
     children_to_remove = [tree[tuple(child_pos)] for child_pos in child_positions]
     for child in children_to_remove:
@@ -268,27 +282,32 @@ def labelled_tree_str(tree_str, start, end):
 
     return str(tree)
 
+
 def prepare_annotated_reviews():
     reviews = parse(selected_reviews_location)
     root = reviews.getroot()
     annotated = [review for review in root if review.attrib['annotated'] == 'true']
 
-    prepared_root = Element('data')
+    prepared_root = Element('sentences')
 
     for review in annotated:
         for sentence in review.find('sentences'):
             text = sentence.find('text').text
             tree_str = sentence.find('parse_tree').text
-            annotations = sentence.find('annotations') if sentence.find('annotations') else []
-            for annotation in annotations:
-                instance_node = SubElement(prepared_root, 'instance')
-                text_node = SubElement(instance_node, 'text')
-                text_node.text = text
-                op_node = SubElement(instance_node, 'opinion')
-                op_node.text = annotation.find('sentiment').text
-                tree_node = SubElement(instance_node, 'tree')
-                start, end = annotation.find('range').text.split(',')
-                tree_node.text = labelled_tree_str(tree_str, int(start), int(end))
+            sentence_node = SubElement(prepared_root, 'sentence')
+            text_node = SubElement(sentence_node, 'text')
+            text_node.text = text
+
+            if sentence.find('annotations'):
+                aspect_terms_node = SubElement(sentence_node, 'aspectTerms')
+
+                for annotation in sentence.find('annotations'):
+                    start, end = annotation.find('range').text.split(',')
+                    aspect_term_node = SubElement(aspect_terms_node, 'aspectTerm')
+                    aspect_term_node.set('term', text[start:end])
+                    aspect_term_node.set('polarity', annotation.find('sentiment').text)
+                    aspect_term_node.set('from', start)
+                    aspect_term_node.set('to', end)
 
     train_count = 1000
     train_root = Element('data')
@@ -316,6 +335,7 @@ def prepare_annotated_reviews():
     with open('amazon_camera_test.xml', 'w') as f:
         f.write(xmlstr)
 
+
 # prepare_reviews()
-# annotate_reviews()
-prepare_annotated_reviews()
+annotate_reviews()
+# prepare_annotated_reviews()