Sentiment Analysis with TextBlob: A Beginner's Guide

By: vishwesh

Sentiment Analysis with TextBlob: A Beginner's Guide

If you've ever wondered how to extract sentiment from text data, then you've come to the right place. In this article, we will be discussing Sentiment Analysis using TextBlob, which is a Python library for processing textual data.

What is Sentiment Analysis?

Sentiment Analysis is the process of extracting emotions or opinions from text data. This can be done by analyzing the words and phrases used in the text to determine whether they convey a positive, negative, or neutral sentiment.

For example, consider the following text:

"I absolutely love this product! It's amazing!"

The sentiment of this text is clearly positive, as the words "love" and "amazing" convey a positive emotion.

Sentiment Analysis can be useful in a variety of applications, such as customer reviews, social media analysis, and market research.

What is TextBlob?

TextBlob is a Python library that makes it easy to perform common Natural Language Processing (NLP) tasks, including Sentiment Analysis. It provides a simple and intuitive API for processing textual data, making it a popular choice among beginners and experts alike.

Installing TextBlob

Before we can start using TextBlob, we need to install it. To do this, open a terminal or command prompt and type the following command:

pip install textblob

This will install TextBlob and all of its dependencies.

Performing Sentiment Analysis with TextBlob

Now that we have TextBlob installed, let's see how we can use it to perform Sentiment Analysis. The first step is to create a TextBlob object from our text data. We can do this by simply passing the text to the TextBlob() constructor:

from textblob import TextBlob

text = "I absolutely love this product! It's amazing!"
blob = TextBlob(text)

Next, we can use the sentiment property of the TextBlob object to get the sentiment of the text. The sentiment property returns a tuple containing two values: the polarity and subjectivity.

The polarity value is a float between -1 and 1, where -1 indicates a negative sentiment and 1 indicates a positive sentiment. The subjectivity value is a float between 0 and 1, where 0 indicates an objective statement and 1 indicates a subjective statement.

sentiment = blob.sentiment
polarity = sentiment.polarity
subjectivity = sentiment.subjectivity

print("Polarity:", polarity)
print("Subjectivity:", subjectivity)

The output of this code will be:

Polarity: 0.625
Subjectivity: 0.9

As expected, the polarity is positive (0.625) and the subjectivity is high (0.9), indicating that the text is highly subjective and conveys a positive sentiment.

Customizing TextBlob for Sentiment Analysis

TextBlob comes with a built-in sentiment analyzer that uses a pre-trained model to perform Sentiment Analysis. However, this model may not always produce accurate results, especially if the text contains slang or domain-specific terminology.

To improve the accuracy of Sentiment Analysis with TextBlob, we can train our own sentiment classifier using a custom dataset. This involves labeling a set of text data as either positive, negative, or neutral, and using it to train a machine learning model.

TextBlob provides a convenient API for training custom classifiers. To train a classifier, we first need to create a dataset of labeled text data. This can be done using a simple text file, where each line contains a piece of text followed by its label: 

I love this product! : positive
This product is terrible. : negative
I'm not sure how I feel about this product. : neutral

Once we have our labeled dataset, we can use TextBlob's NaiveBayesAnalyzer to train a sentiment classifier. The NaiveBayesAnalyzer is a machine learning algorithm that uses Bayes' theorem to classify text as positive, negative, or neutral.

from textblob import TextBlob
from textblob.sentiments import NaiveBayesAnalyzer

# create a custom dataset
train_data = [
    ("I love this product!", "positive"),
    ("This product is terrible.", "negative"),
    ("I'm not sure how I feel about this product.", "neutral")

# train the classifier
cl = NaiveBayesAnalyzer(train_data)

# create a TextBlob object with the custom classifier
text = "I'm not sure how I feel about this product."
blob = TextBlob(text, analyzer=cl)

# get the sentiment
sentiment = blob.sentiment.classification
print("Sentiment:", sentiment)

In this example, we first create a custom dataset with three labeled examples. We then use the NaiveBayesAnalyzer to train a sentiment classifier on this dataset. Finally, we create a TextBlob object with the custom classifier and use it to get the sentiment of a piece of text.

The output of this code will be:

Sentiment: neutral

As expected, the sentiment of the text is neutral, since it was labeled as such in the custom dataset.


Sentiment Analysis is a powerful tool for extracting emotions and opinions from text data. With TextBlob, performing Sentiment Analysis is easy and straightforward. In this article, we covered the basics of Sentiment Analysis using TextBlob, including installing TextBlob, creating TextBlob objects, and customizing TextBlob for Sentiment Analysis.

If you're interested in learning more about TextBlob and NLP, there are many resources available online, including the official TextBlob documentation and various tutorials and guides. With a little bit of practice and experimentation, you'll be able to perform Sentiment Analysis on your own text data in no time!

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