Emotion detection from text Python

Emotion detection from text Python

Kalebu Jordan's photo
Kalebu Jordan
·

4 min read

Hi guys

In this tutorial, I will guide you on how to detect emotions associated with textual data and how can you apply it in real-world applications.

Understanding emotions associated with text is commonly known as sentiment analysis

Where is used?

You can apply it to perform analysis of customer feedback by directly reading them as either positive or negative feedback instead of manually reading to detect the emotions

Requirements

There variety of libraries in python which can be used for natural language processing tasks including emotions detection from text including

  • Natural Language Toolkit (NLTK)
  • Gensim.
  • polyglot.
  • TextBlob.
  • CoreNLP.
  • spaCy.
  • Pattern.
  • Vocabulary.

Well based on simplicity and ease of getting started I have chosen to go with textblob throughout a tutorial

TextBlob 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.

The good thing I like about it is its simplicity in getting started with natural language processing tasks.

Installation

In Window on a window just you use normal pip command

pip install textblob
python -m textblob.download_corpora

In Linux use pip3 command for python3

pip3 install textblob 
python3 -m textblob.download_corpora

Let’s get started

In performing textual analysis using textblob we first have to create a textblob object as shown below

>>>from textblob import TextBlob
>>>text = 'I had an awesome day'
>>>blob_text = TextBlob(text)

Once you have created a textblob object you can now access tons of textblob methods to manipulate textual data.

For example un tagging part of speech of a text can be as simple as shown below

TextBlob tags () method

>>>from textblob import TextBlob
>>>text = 'I had an awesome day'
>>>blob_text = TextBlob(text)
>>>tags = blob_text.tags
>>>print(tags)
[('I', 'PRP'), ('had', 'VBD'), ('an', 'DT'),
('awesome', 'JJ'), ('day', 'NN')]

TextBlob Sentiment ( )

In order to perform sentiment analysis using textblob we have to use the sentiment () method as shown below;

>>sentiment = blob_text.sentiment 
>>>print(sentiment)
    Sentiment(polarity=1.0, subjectivity=1.0)

As we can see above as we call the sentiment () it returns a Textblob object Sentiment with polarity and subjectivity.

TextBlob Polarity

In building an emotion detector, we are more concerned with the polarity, therefore to get exactly polarity from the Sentiment object we have to get it as it’s an attribute

>>>polarity = sentiment.polarity
>>>print(polarity)
    1.0

Note: The polarity of the textual data ranges from -1 to 1, where negative polarity indicate negative emotions with -1 as mostly negative and vice versa

Use case (Demo Project)

Let’s assume we have our app which allows users to provide feedbacks If they like the user experience or not, and then we are going to use textblob to count negative feedbacks and negative feedbacks

from textblob import TextBlob
​
feedbacks = ['I love the app is amazing ', 
             "The experience was bad as hell", 
             "This app is really helpful",
             "Damn the app tastes like shit ",
            'Please don\'t download the app you will regret it ']
​
positive_feedbacks = []
negative_feedbacks = []
​
for feedback in feedbacks:
  feedback_polarity = TextBlob(feedback).sentiment.polarity
  if feedback_polarity>0:
    positive_feedbacks.append(feedback)
    continue
  negative_feedbacks.append(feedback)

print('Positive_feebacks Count : {}'.format(len(positive_feedbacks)))
print(positive_feedbacks)
print('Negative_feedback Count : {}'.format(len(negative_feedbacks)))
print(negative_feedbacks)

Output :

Once you run the above code the below results with appear, the script with separate between negative and positive feedback given by the customer automatically as shown below

$ -> python app.py
Positive_feebacks Count: 2
['I love the app is amazing ', 'This app is really helpful']
Negative_feedback Count : 3
['The experience was bad as hell', 'Damn the app tastes like shit ', "Please don't download the app you will regret it "]

Congratulations you performed emotion detection from text using Python, now don’t be shy share it will your fellow friends on twitter, social media groups.

Hope you find this Interesting, In case of anything comment, suggestion, or faced any trouble check it out on the comment box and I will get back to you as fast as I can.

The Original Article can be found on kalebujordan.com

PythonMachine LearningArtificial Intelligencenlp