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Sentiment Analysis using TextBlob

• Naveen
• January 22, 2023January 22, 2023
• 1

Sentiment analysis or opinion mining can be used to gain insights from large amounts of data. It uses natural language processing, text analysis, and computational linguistics to detect and extract emotional content from text-based sources. It is used to determine the attitudes, opinions, and emotions of a speaker or writer with respect to some topic or the overall contextual polarity of a document.

Sentiment analysis is used in a variety of applications such as:

Social media monitoring:

Companies can use sentiment analysis to monitor brand mentions and customer feedback on social media platforms, to understand how their brand is perceived by the public and respond accordingly.

Marketing and advertising:

Sentiment analysis can be used to evaluate the effectiveness of marketing campaigns, or to gauge consumer sentiment about a product or brand.

Customer service:

Sentiment analysis can be used to automatically classify customer service inquiries and feedback, making it easier for companies to respond to customer needs.

Opinion mining:

Sentiment analysis can be used to extract opinions from reviews, articles, and other sources, to understand how people feel about a particular topic or product.

Overall, Sentiment Analysis is a way to extract meaningful insights from vast amount of unstructured data, it’s used to understand the opinion of people, to know what they think, feel and express about a topic, brand, product, or service. By doing so, it can inform business decisions, create effective marketing strategies, and improve customer service.

In this article we will be using textblob library to analyse text.

Step 1: Install the TextBlob library

The textblob library can be installed on your system by running this command: pip install -U textblob.

Step 2: Import the necessary libraries

Step 3: Define a function to perform sentiment analysis

Step 4: Use the function to analyze a sample text

We can see that output is “positive” for this text.

Step 5: you can use this function to analyze multiple text, for example

We will output “positive” “negative” “neutral” as the sentiment of the texts respectively.

Note: that the textBlob library is based on a pre-trained model, so it might not give accurate results in some cases, especially when the text is highly subjective or has a lot of colloquial language. Also, you may use other libraries such as NLTK, VaderSentiment and more to perform sentiment analysis, each of them has its own pros and cons.

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