Sentiment Analysis using TextBlob

TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.

Installation

pip install textblob

Features

• Noun phrase extraction
• Part-of-speech tagging
• Sentiment analysis
• Classification (Naive Bayes, Decision Tree)
• Language translation and detection powered by Google Translate
• Tokenization (splitting text into words and sentences)
• Word and phrase frequencies
• Parsing
• n-grams
• Word inflection (pluralization and singularization) and lemmatization
• Spelling correction
• Add new models or languages through extensions
• WordNet integration

Create a TextBlob

First, the import.

from textblob import TextBlob

Sentiment Analysis

The sentiment property returns a named tuple of the form Sentiment(polarity, subjectivity).

>>> testimonial = TextBlob("Textblob is amazingly simple to use. What great fun!")
>>> testimonial.sentiment
Sentiment(polarity=0.39166666666666666, subjectivity=0.4357142857142857)

The polarity score is a float within the range [-1.0, 1.0] where 1 means positive statement and -1 means a negative statement.

>>> testimonial.sentiment.polarity
0.39166666666666666

Subjective sentences generally refer to personal opinion, emotion or judgment whereas objective refers to factual information.. The subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.

>>> testimonial.sentiment.subjectivity
0.4357142857142857

Thanks for reading!!