Added spell-checker to SVM. Added BERT and EDA files. Multiclass SVM …

…instead of binary. New translations via IBM for multiclass. GPT translation for training data.

EDA_English.py

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+import nltk
+from nltk.corpus import wordnet
+from nltk.corpus import stopwords
+import random
+import pandas as pd
+
+
+
+
+def create_augmented_dataset(file_path):
+    # Read existing data from the CSV file
+    existing_data = pd.read_csv(file_path, encoding = 'ANSI')
+    all_tweets = existing_data["Tweet"].to_list()
+    all_targets = existing_data["Target"].to_list()
+    all_stances = existing_data["Stance"].to_list()
+    alpha = 0.05
+    new_data =[]
+    new_tweets = []
+    new_targets = []
+    new_stances = []
+    tweet_count = len(all_tweets)
+    for i in range(tweet_count):
+        new_tweets.append(eda_SR(all_tweets[i], int(len(all_tweets[i])*alpha)))
+        new_targets.append(all_targets[i])
+        new_stances.append(all_stances[i])
+
+    new_data.append(new_tweets)
+    new_data.append(new_targets)
+    new_data.append(new_stances)
+    # Convert new data to a DataFrame
+    new_data_df = pd.DataFrame(new_data, columns=existing_data.columns)
+
+    # Concatenate existing data with new data
+    combined_data = pd.concat([existing_data, new_data_df], ignore_index=True)
+
+    # Write the combined data back to the CSV file
+    combined_data.to_csv(file_path, index=False)
+
+
+def eda_SR(originalSentence, n):
+
+  stops = set(stopwords.words('english'))
+  splitSentence = list(originalSentence.split(" "))
+  splitSentenceCopy = splitSentence.copy()
+
+  ls_nonStopWordIndexes = []
+  for i in range(len(splitSentence)):
+    if splitSentence[i].lower() not in stops:
+      ls_nonStopWordIndexes.append(i)
+  if (n > len(ls_nonStopWordIndexes)):
+    raise Exception("The number of replacements exceeds the number of non stop word words")
+  for i in range(n):
+    indexChosen = random.choice(ls_nonStopWordIndexes)
+    ls_nonStopWordIndexes.remove(indexChosen)
+    synonyms = []
+    originalWord = splitSentenceCopy[indexChosen]
+    for synset in wordnet.synsets(originalWord):
+      for lemma in synset.lemmas():
+        if lemma.name() != originalWord:
+          synonyms.append(lemma.name())
+    if (synonyms == []):
+      continue
+    splitSentence[indexChosen] = random.choice(synonyms).replace('_', ' ')
+  return " ".join(splitSentence)
+
+
+print(eda_SR("I love to play football", 2))
+
+def eda_RI(originalSentence, n):
+  stops = set(stopwords.words('english'))
+  splitSentence = list(originalSentence.split(" "))
+  splitSentenceCopy = splitSentence.copy() 
+  # Since We Make Changes to The Original Sentence List The Indexes Change and Hence an initial copy proves useful to get values
+  ls_nonStopWordIndexes = []
+  for i in range(len(splitSentence)):
+    if splitSentence[i].lower() not in stops:
+      ls_nonStopWordIndexes.append(i)
+  if (n > len(ls_nonStopWordIndexes)):
+    raise Exception("The number of replacements exceeds the number of non stop word words")
+  WordCount = len(splitSentence)
+  for i in range(n):
+    indexChosen = random.choice(ls_nonStopWordIndexes)
+    ls_nonStopWordIndexes.remove(indexChosen)
+    synonyms = []
+    originalWord = splitSentenceCopy[indexChosen]
+    for synset in wordnet.synsets(originalWord):
+      for lemma in synset.lemmas():
+        if lemma.name() != originalWord:
+          synonyms.append(lemma.name())
+    if (synonyms == []):
+      continue
+    splitSentence.insert(random.randint(0,WordCount-1), random.choice(synonyms).replace('_', ' '))
+  return " ".join(splitSentence)
+
+
+
+
+def eda_RS(originalSentence, n):
+
+  splitSentence = list(originalSentence.split(" "))
+  WordCount = len(splitSentence)
+  for i in range(n):
+    firstIndex = random.randint(0,WordCount-1)
+    secondIndex = random.randint(0,WordCount-1)
+    while (secondIndex == firstIndex and WordCount != 1):
+      secondIndex = random.randint(0,WordCount-1)
+    splitSentence[firstIndex], splitSentence[secondIndex] = splitSentence[secondIndex], splitSentence[firstIndex]
+  return " ".join(splitSentence)
+
+
+
+
+def eda_RD(originalSentence, p):
+  og = originalSentence
+  if (p == 1):
+      raise Exception("Always an Empty String Will Be Returned") 
+  if (p > 1 or p < 0):
+    raise Exception("Improper Probability Value")
+  splitSentence = list(originalSentence.split(" "))
+  lsIndexesRemoved = []
+  WordCount = len(splitSentence)
+  for i in range(WordCount):
+    randomDraw = random.random()
+    if randomDraw <= p:
+      lsIndexesRemoved.append(i)
+  lsRetainingWords = []
+  for i in range(len(splitSentence)):
+    if i not in lsIndexesRemoved:
+      lsRetainingWords.append(splitSentence[i])
+  if (lsRetainingWords == []):
+    return og
+  return " ".join(lsRetainingWords)
+
+
+# Path to your CSV file
+csv_file_path = "aug_train.csv"
+
+create_augmented_dataset(csv_file_path)
+
+