Sentiment Analysis of Customer Reviews with IBM’s Granite-3B and Hugging Face

Introduction to Sentiment Analysis

In this tutorial, we will explore how to perform sentiment analysis on text data using IBM’s open-source Granite 3B model integrated with Hugging Face Transformers. Sentiment analysis is a crucial natural language processing (NLP) technique that helps businesses understand customer emotions through feedback, enabling them to improve their products and services.

Installing Necessary Libraries

First, we need to install the essential libraries: transformers, torch, and accelerate. These libraries allow us to load and run powerful NLP models seamlessly.

  !pip install transformers torch accelerate

Importing Required Libraries

Next, we will import the required Python libraries:

  import torch
  from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
  import pandas as pd
  import matplotlib.pyplot as plt

These libraries will assist us in tensor operations, loading pre-trained NLP models, managing data, and visualizing our analysis results.

Loading the IBM Granite Model

We will load IBM’s Granite 3B model using Hugging Face:

  model_id = "ibm-granite/granite-3.0-3b-a800m-instruct"
  tokenizer = AutoTokenizer.from_pretrained(model_id)
  model = AutoModelForCausalLM.from_pretrained(
      model_id,
      device_map='auto',
      torch_dtype=torch.bfloat16,
      trust_remote_code=True
  )
  generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

This model is optimized for tasks such as sentiment classification, even with limited computational resources.

Defining the Sentiment Classification Function

We will create a function that classifies the sentiment of a review:

  def classify_sentiment(review):
      prompt = f"""Classify the sentiment of the following review as Positive, Negative, or Neutral.
      Review: "{review}"
      Sentiment:"""

      response = generator(
          prompt,
          max_new_tokens=5,
          do_sample=False,
          pad_token_id=tokenizer.eos_token_id
      )

      sentiment = response[0]['generated_text'].split("Sentiment:")[-1].split("n")[0].strip()
      return sentiment

Creating a DataFrame of Reviews

We will create a DataFrame to hold example reviews for sentiment classification:

  reviews = [
      "I absolutely loved the service! Definitely coming back.",
      "The item arrived damaged, very disappointed.",
      "Average product. Nothing too exciting.",
      "Superb experience, exceeded all expectations!",
      "Not worth the money, poor quality."
  ]

  reviews_df = pd.DataFrame(reviews, columns=['review'])

Classifying Sentiments

Now, we will apply the sentiment classification function to each review:

  reviews_df['sentiment'] = reviews_df['review'].apply(classify_sentiment)
  print(reviews_df)

Visualizing Sentiment Distribution

Finally, we will visualize the sentiment distribution using a pie chart:

  sentiment_counts = reviews_df['sentiment'].value_counts()

  plt.figure(figsize=(8, 6))
  sentiment_counts.plot.pie(autopct='%1.1f%%', explode=[0.05]*len(sentiment_counts), colors=['#66bb6a', '#ff7043', '#42a5f5'])
  plt.ylabel('')
  plt.title('Sentiment Distribution of Reviews')
  plt.show()

Conclusion

We have successfully implemented a sentiment analysis pipeline using IBM’s Granite 3B model. This method allows businesses to classify customer sentiments quickly and visualize insights effectively. By leveraging these skills, you can apply the same approach to analyze other datasets or explore various NLP tasks.