spacytextblob 4.0.0

A TextBlob sentiment analysis pipeline component for spaCy.

spacytextblob

A TextBlob sentiment analysis pipeline component for spaCy.

• Docs
• GitHub
• PyPi

Table of Contents

• Install
• Quick Start
• Quick Reference
• Reference and Attribution

Install

Install spacytextblob from PyPi.

pip install spacytextblob

TextBlob requires additional data to be downloaded before getting started.

python -m textblob.download_corpora

spaCy also requires that you download a model to get started.

python -m spacy download en_core_web_sm

Quick Start

spacytextblob allows you to access all of the attributes created of the textblob.TextBlob class but within the spaCy framework. The code below will demonstrate how to use spacytextblob on a simple string.

import spacy
from spacytextblob.spacytextblob import SpacyTextBlob

nlp = spacy.load('en_core_web_sm')
text = "I had a really horrible day. It was the worst day ever! But every now and then I have a really good day that makes me happy."
nlp.add_pipe("spacytextblob")
doc = nlp(text)

print(doc._.blob.polarity)
# -0.125

print(doc._.blob.subjectivity)
# 0.9

print(doc._.blob.sentiment_assessments.assessments)
# [(['really', 'horrible'], -1.0, 1.0, None), (['worst', '!'], -1.0, 1.0, None), (['really', 'good'], 0.7, 0.6000000000000001, None), (['happy'], 0.8, 1.0, None)]

In comparison, here is how the same code would look using TextBlob:

from textblob import TextBlob

text = "I had a really horrible day. It was the worst day ever! But every now and then I have a really good day that makes me happy."
blob = TextBlob(text)

print(blob.sentiment_assessments.polarity)
# -0.125

print(blob.sentiment_assessments.subjectivity)
# 0.9

print(blob.sentiment_assessments.assessments)
# [(['really', 'horrible'], -1.0, 1.0, None), (['worst', '!'], -1.0, 1.0, None), (['really', 'good'], 0.7, 0.6000000000000001, None), (['happy'], 0.8, 1.0, None)]

Quick Reference

spacytextblob performs sentiment analysis using the TextBlob library. Adding spacytextblob to a spaCy nlp pipeline creates a new extension attribute for the Doc, Span, and Token classes from spaCy.

• Doc._.blob
• Span._.blob
• Token._.blob

The ._.blob attribute contains all of the methods and attributes that belong to the textblob.TextBlob class Some of the common methods and attributes include:

• ._.blob.polarity: a float within the range [-1.0, 1.0].
• ._.blob.subjectivity: a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.
• ._.blob.sentiment_assessments.assessments: a list of polarity and subjectivity scores for the assessed tokens.

See the textblob docs for the complete listing of all attributes and methods that are available in ._.blob.

Reference and Attribution

• TextBlob
  • https://github.com/sloria/TextBlob
  • https://textblob.readthedocs.io/en/latest/
• negspaCy (for inspiration in writing pipeline and organizing repo)
  • https://github.com/jenojp/negspacy
• spaCy custom components
  • https://spacy.io/usage/processing-pipelines#custom-components