Python | TextBlob.sentiment() method

Sentiment analysis helps us find the emotional tone of text whether it’s positive, negative or neutral. The TextBlob.sentiment() method simplifies this task by providing two key components:

• polarity: which shows if the text is positive, negative or neutral
• subjectivity: which shows whether the text is more opinion-based or factual

Let's see a basic example:

from textblob import TextBlob

text = "GFG is a good company and always value their employees."
blob = TextBlob(text)
sentiment = blob.sentiment
print(sentiment)

Output:

Sentiment(polarity=0.7, subjectivity=0.6000000000000001)

• Polarity: 0.7 means positive sentiment.
• Subjectivity: 0.6 means it’s more opinion-based.

Syntax:

TextBlob.sentiment

Return:

• Polarity: A score between -1 (negative) and 1 (positive) showing how positive or negative the text is.
• Subjectivity: A score between 0 (factual) and 1 (opinion-based) showing how subjective or objective the text is.

Lets see some more examples:

Example 1: Negative Sentiment

Here, we analyze a sentence that expresses a strong negative sentiment. The polarity score reflects the negative tone while the subjectivity shows opinion-based statement.

text = "I hate bugs in my code."
blob = TextBlob(text)
sentiment = blob.sentiment
print(sentiment)

Output:

Sentiment(polarity=-0.8, subjectivity=0.9)

• Polarity: -0.8 shows a negative sentiment.
• Subjectivity: 0.9 shows the text is highly opinion-based.

Example 2: Neutral Sentiment

In this example, we’ll analyze a neutral sentence that conveys factual information without expressing any strong opinion or emotion. It will show how TextBlob classifies a sentence with no sentiment bias.

text = "The sun rises in the east."
blob = TextBlob(text)
sentiment = blob.sentiment
print(sentiment)

Output:

Sentiment(polarity=0.0, subjectivity=0.0)

• Polarity: 0.0 means neutral.
• Subjectivity: 0.0 shows it's factual.

Example 3: Mixed Sentiment

Here the sentence presents a neutral sentiment but is more opinion-based than factual. The polarity score remains neutral while the subjectivity score reflects an opinion or preference.

text = "I enjoy coding, but debugging can be frustrating."
blob = TextBlob(text)
sentiment = blob.sentiment
print(sentiment)

Output:

Sentiment(polarity=0.0, subjectivity=0.7)

• Polarity = 0.0: This shows a neutral sentiment.
• Subjectivity = 0.7: The text is more opinion-based than factual as it's expressing a preference.

Practical Use Cases

This can be useful in various applications including:

1. Social Media Monitoring: Analyze sentiment in social media posts to identify public opinion, tracking how positive or negative people feel about topics. This helps in understanding overall sentiment toward trends or events.
2. Customer Feedback: Quickly assess customer reviews to identify satisfaction levels, detecting trends in product or service reception. It allows businesses to find customer satisfaction in real-time.
3. Content Moderation: Identify and flag negative or inappropriate comments, helping maintain a positive environment in online communities. This improves user experience and promotes healthy discussions.

Limitations

1. Context Issues: TextBlob struggles with sarcasm or irony, leading to inaccurate sentiment scores and it may miss the true sentiment of a message. This affects its reliability in certain contexts.
2. Simplicity: It relies on a basic lexicon which may miss deeper nuances, making it less accurate for complex or nuanced text. This can limit its application for sophisticated analysis.
3. Ambiguity: Mixed sentiments in one sentence can be difficult for it to handle which may result in incorrect classification of sentiment. This reduces its effectiveness in certain cases.