Project 3: Customer Feedback Analysis Using Sentiment Analysis
4. Step 2: Loading and Preparing the Dataset
For this project, we'll need a substantial dataset to train our sentiment analysis model effectively. There are several excellent options available:
- The IMDB Movie Reviews Dataset: This is a widely-used benchmark dataset containing 50,000 movie reviews labeled as positive or negative. It's particularly useful because it contains longer-form text with nuanced opinions, similar to real customer feedback.
- Kaggle Customer Feedback Datasets: Kaggle offers various customer feedback datasets from different industries, including e-commerce reviews, product feedback, and service evaluations. These datasets often come with sentiment labels and additional metadata that can enrich your analysis.
- Hugging Face Datasets: Through the Hugging Face Datasets library, you can access numerous pre-processed datasets specifically designed for sentiment analysis tasks. These include:
- Amazon Product Reviews
- Yelp Reviews
- Twitter Sentiment Analysis Dataset
- Multi-Domain Sentiment Dataset
The choice of dataset can significantly impact your model's performance, so consider selecting one that closely matches your intended use case in terms of text length, writing style, and domain-specific vocabulary.
Load the Dataset
from datasets import load_dataset
# Load a sentiment analysis dataset (e.g., IMDB reviews)
dataset = load_dataset('imdb')
# Check the dataset structure
print(dataset)Let's break down this code:
- First, we import the necessary module:
from datasets import load_dataset- Then we load the IMDB dataset:
dataset = load_dataset('imdb')This loads the IMDB Movie Reviews Dataset, which is a benchmark dataset containing movie reviews labeled as positive or negative.
- Finally, we print the dataset structure:
print(dataset)When executed, this code will load a dataset that contains both training and test splits, where each entry includes text reviews and their corresponding sentiment labels (0 for negative, 1 for positive).
This code represents the initial step in the sentiment analysis pipeline, where we prepare our data for training a BERT model to classify customer feedback.
The dataset will contain a train and test split, with text reviews and sentiment labels (e.g., 0 for negative, 1 for positive).
Preprocess the Dataset
Before using the data with BERT, it must be tokenized.
from transformers import BertTokenizer
# Load the BERT tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Tokenize the dataset
def tokenize_function(examples):
return tokenizer(examples['text'], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)Let’s break down this code:
1. Importing and Initializing the Tokenizer:
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')This imports the BERT tokenizer and loads the uncased version, which means it converts all text to lowercase.
2. Creating the Tokenization Function:
def tokenize_function(examples):
return tokenizer(examples['text'], padding="max_length", truncation=True)This function processes the text data by:
- Converting text into tokens that BERT can understand
- Using padding to ensure all sequences have the same length
- Truncating longer sequences to fit the model's maximum length
3. Applying the Tokenization:
tokenized_datasets = dataset.map(tokenize_function, batched=True)This applies the tokenization function to the entire dataset, converting the text into numerical token IDs that BERT can process. The batched=True parameter enables efficient processing of multiple examples at once.
4. Step 2: Loading and Preparing the Dataset
For this project, we'll need a substantial dataset to train our sentiment analysis model effectively. There are several excellent options available:
- The IMDB Movie Reviews Dataset: This is a widely-used benchmark dataset containing 50,000 movie reviews labeled as positive or negative. It's particularly useful because it contains longer-form text with nuanced opinions, similar to real customer feedback.
- Kaggle Customer Feedback Datasets: Kaggle offers various customer feedback datasets from different industries, including e-commerce reviews, product feedback, and service evaluations. These datasets often come with sentiment labels and additional metadata that can enrich your analysis.
- Hugging Face Datasets: Through the Hugging Face Datasets library, you can access numerous pre-processed datasets specifically designed for sentiment analysis tasks. These include:
- Amazon Product Reviews
- Yelp Reviews
- Twitter Sentiment Analysis Dataset
- Multi-Domain Sentiment Dataset
The choice of dataset can significantly impact your model's performance, so consider selecting one that closely matches your intended use case in terms of text length, writing style, and domain-specific vocabulary.
Load the Dataset
from datasets import load_dataset
# Load a sentiment analysis dataset (e.g., IMDB reviews)
dataset = load_dataset('imdb')
# Check the dataset structure
print(dataset)Let's break down this code:
- First, we import the necessary module:
from datasets import load_dataset- Then we load the IMDB dataset:
dataset = load_dataset('imdb')This loads the IMDB Movie Reviews Dataset, which is a benchmark dataset containing movie reviews labeled as positive or negative.
- Finally, we print the dataset structure:
print(dataset)When executed, this code will load a dataset that contains both training and test splits, where each entry includes text reviews and their corresponding sentiment labels (0 for negative, 1 for positive).
This code represents the initial step in the sentiment analysis pipeline, where we prepare our data for training a BERT model to classify customer feedback.
The dataset will contain a train and test split, with text reviews and sentiment labels (e.g., 0 for negative, 1 for positive).
Preprocess the Dataset
Before using the data with BERT, it must be tokenized.
from transformers import BertTokenizer
# Load the BERT tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Tokenize the dataset
def tokenize_function(examples):
return tokenizer(examples['text'], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)Let’s break down this code:
1. Importing and Initializing the Tokenizer:
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')This imports the BERT tokenizer and loads the uncased version, which means it converts all text to lowercase.
2. Creating the Tokenization Function:
def tokenize_function(examples):
return tokenizer(examples['text'], padding="max_length", truncation=True)This function processes the text data by:
- Converting text into tokens that BERT can understand
- Using padding to ensure all sequences have the same length
- Truncating longer sequences to fit the model's maximum length
3. Applying the Tokenization:
tokenized_datasets = dataset.map(tokenize_function, batched=True)This applies the tokenization function to the entire dataset, converting the text into numerical token IDs that BERT can process. The batched=True parameter enables efficient processing of multiple examples at once.
4. Step 2: Loading and Preparing the Dataset
For this project, we'll need a substantial dataset to train our sentiment analysis model effectively. There are several excellent options available:
- The IMDB Movie Reviews Dataset: This is a widely-used benchmark dataset containing 50,000 movie reviews labeled as positive or negative. It's particularly useful because it contains longer-form text with nuanced opinions, similar to real customer feedback.
- Kaggle Customer Feedback Datasets: Kaggle offers various customer feedback datasets from different industries, including e-commerce reviews, product feedback, and service evaluations. These datasets often come with sentiment labels and additional metadata that can enrich your analysis.
- Hugging Face Datasets: Through the Hugging Face Datasets library, you can access numerous pre-processed datasets specifically designed for sentiment analysis tasks. These include:
- Amazon Product Reviews
- Yelp Reviews
- Twitter Sentiment Analysis Dataset
- Multi-Domain Sentiment Dataset
The choice of dataset can significantly impact your model's performance, so consider selecting one that closely matches your intended use case in terms of text length, writing style, and domain-specific vocabulary.
Load the Dataset
from datasets import load_dataset
# Load a sentiment analysis dataset (e.g., IMDB reviews)
dataset = load_dataset('imdb')
# Check the dataset structure
print(dataset)Let's break down this code:
- First, we import the necessary module:
from datasets import load_dataset- Then we load the IMDB dataset:
dataset = load_dataset('imdb')This loads the IMDB Movie Reviews Dataset, which is a benchmark dataset containing movie reviews labeled as positive or negative.
- Finally, we print the dataset structure:
print(dataset)When executed, this code will load a dataset that contains both training and test splits, where each entry includes text reviews and their corresponding sentiment labels (0 for negative, 1 for positive).
This code represents the initial step in the sentiment analysis pipeline, where we prepare our data for training a BERT model to classify customer feedback.
The dataset will contain a train and test split, with text reviews and sentiment labels (e.g., 0 for negative, 1 for positive).
Preprocess the Dataset
Before using the data with BERT, it must be tokenized.
from transformers import BertTokenizer
# Load the BERT tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Tokenize the dataset
def tokenize_function(examples):
return tokenizer(examples['text'], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)Let’s break down this code:
1. Importing and Initializing the Tokenizer:
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')This imports the BERT tokenizer and loads the uncased version, which means it converts all text to lowercase.
2. Creating the Tokenization Function:
def tokenize_function(examples):
return tokenizer(examples['text'], padding="max_length", truncation=True)This function processes the text data by:
- Converting text into tokens that BERT can understand
- Using padding to ensure all sequences have the same length
- Truncating longer sequences to fit the model's maximum length
3. Applying the Tokenization:
tokenized_datasets = dataset.map(tokenize_function, batched=True)This applies the tokenization function to the entire dataset, converting the text into numerical token IDs that BERT can process. The batched=True parameter enables efficient processing of multiple examples at once.
4. Step 2: Loading and Preparing the Dataset
For this project, we'll need a substantial dataset to train our sentiment analysis model effectively. There are several excellent options available:
- The IMDB Movie Reviews Dataset: This is a widely-used benchmark dataset containing 50,000 movie reviews labeled as positive or negative. It's particularly useful because it contains longer-form text with nuanced opinions, similar to real customer feedback.
- Kaggle Customer Feedback Datasets: Kaggle offers various customer feedback datasets from different industries, including e-commerce reviews, product feedback, and service evaluations. These datasets often come with sentiment labels and additional metadata that can enrich your analysis.
- Hugging Face Datasets: Through the Hugging Face Datasets library, you can access numerous pre-processed datasets specifically designed for sentiment analysis tasks. These include:
- Amazon Product Reviews
- Yelp Reviews
- Twitter Sentiment Analysis Dataset
- Multi-Domain Sentiment Dataset
The choice of dataset can significantly impact your model's performance, so consider selecting one that closely matches your intended use case in terms of text length, writing style, and domain-specific vocabulary.
Load the Dataset
from datasets import load_dataset
# Load a sentiment analysis dataset (e.g., IMDB reviews)
dataset = load_dataset('imdb')
# Check the dataset structure
print(dataset)Let's break down this code:
- First, we import the necessary module:
from datasets import load_dataset- Then we load the IMDB dataset:
dataset = load_dataset('imdb')This loads the IMDB Movie Reviews Dataset, which is a benchmark dataset containing movie reviews labeled as positive or negative.
- Finally, we print the dataset structure:
print(dataset)When executed, this code will load a dataset that contains both training and test splits, where each entry includes text reviews and their corresponding sentiment labels (0 for negative, 1 for positive).
This code represents the initial step in the sentiment analysis pipeline, where we prepare our data for training a BERT model to classify customer feedback.
The dataset will contain a train and test split, with text reviews and sentiment labels (e.g., 0 for negative, 1 for positive).
Preprocess the Dataset
Before using the data with BERT, it must be tokenized.
from transformers import BertTokenizer
# Load the BERT tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Tokenize the dataset
def tokenize_function(examples):
return tokenizer(examples['text'], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)Let’s break down this code:
1. Importing and Initializing the Tokenizer:
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')This imports the BERT tokenizer and loads the uncased version, which means it converts all text to lowercase.
2. Creating the Tokenization Function:
def tokenize_function(examples):
return tokenizer(examples['text'], padding="max_length", truncation=True)This function processes the text data by:
- Converting text into tokens that BERT can understand
- Using padding to ensure all sequences have the same length
- Truncating longer sequences to fit the model's maximum length
3. Applying the Tokenization:
tokenized_datasets = dataset.map(tokenize_function, batched=True)This applies the tokenization function to the entire dataset, converting the text into numerical token IDs that BERT can process. The batched=True parameter enables efficient processing of multiple examples at once.