About Sentiment Analysis
Sentimental Analysis is a crucial task in natural language processing (NLP) that involves determining the emotional tone or polarity of a given text, classifying it as positive, negative, or neutral.
However, it faces challenges such as understanding context, detecting sarcasm, handling ambiguous language, and adapting to domain-specific vocabulary.
Setting up the Attack Lab
Requirements:
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apt update && apt install python3-pip -y
mkdir llm-sentiment-analysis
cd llm-sentiment-analysis
cat >requirements.txt <<EOF
transformers==4.30.0
numpy==1.26.2
scipy==1.10.1
EOF
pip install -r requirements.txt
Performing Sentiment Analysis Using an LLM
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cat > simple_sentiment_analyser.py <<EOF
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
import csv
import urllib.request
MODEL = "cardiffnlp/twitter-roberta-base-sentiment"
revision_id = "daefdd1f6ae931839bce4d0f3db0a1a4265cd50f"
model = AutoModelForSequenceClassification.from_pretrained(MODEL, revision=revision_id)
tokenizer = AutoTokenizer.from_pretrained(MODEL, revision=revision_id)
# download label mapping
labels=[]
mapping_link = "https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/sentiment/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
# Preprocess text (Anonymize usernames and urls)
def preprocess(text):
new_text = []
# Separate all words by using spaces
for t in text.split(" "):
# If a word starts with @, and is longer than 1 character, replace it with @user
t = '@user' if t.startswith('@') and len(t) > 1 else t
# If a word starts with http, replace it with http
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
# Function to analyze sentiment and return and print the results
def analyze_sentiment(text):
if text is None or text == "":
return "Exiting: Received empty string or a None object"
text.strip().replace("\n", "").replace("\r", "")
text = preprocess(text) # Preprocess the text
encoded_input = tokenizer(text, return_tensors='pt') # Tokenize the text
output = model(**encoded_input) # Get model output [** is used to unpack the encoded_input]
scores = output[0][0].detach().numpy() # Get the sentiment scores
scores = softmax(scores) # Apply softmax to the scores to normalize them
ranking = np.argsort(scores) # Get the ranking of the scores
ranking = ranking[::-1] # Reverse the order to get highest to lowest
results = [] # List to store the sentiment analysis results
for i in range(scores.shape[0]):
l = labels[ranking[i]] # Get the label for the sentiment
s = scores[ranking[i]] # Get the score for the sentiment
results.append((l, np.round(float(s), 4))) # Add the result to the list
# Print the sentiment results
for label, score in results:
print(f"{label}: {score}")
positive_score = next((score for label, score in results if label == 'positive'), 0)
if positive_score > 0.5:
print("The sentiment is positive.")
#return results # Return the list of sentiment results
return positive_score
if __name__ == "__main__":
while True:
print("+" *50)
text = input("\033[92mEnter your text: Type 'X' or 'x' to exit: \033[0m").strip()
if text in ['X', 'x']:
print("Exiting.")
break
else:
# Analyze the sentiment of the text
sentiment = analyze_sentiment(text)
print("Returned Sentiment:", sentiment)
EOF
Usage:
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python simple_sentiment_analyser.py
Text attack examples:
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