Sentiment Analysis in Python: TextBlob vs Vader Sentiment vs Flair vs Building It From Scratch

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Sentiment analysis is one of the most widely known Natural Language Processing (NLP) tasks. This article aims to give the reader a very clear understanding of sentiment analysis and different methods through which it is implemented in NLP. So let’s dive in.

The field of NLP has evolved very much in the last five years, open-source packages like Spacy, TextBlob, etc. provide ready to use functionalities for NLP like sentiment analysis. There are so many of these packages available for free to make you confused about which one to use for your application.

In this article, I will discuss the most popular NLP Sentiment analysis packages:

Rule-based sentiment analysis is one of the very basic approaches to calculate text sentiments. It only requires minimal pre-work and the idea is quite simple, this method does not use any machine learning to figure out the text sentiment. For example, we can figure out the sentiments of a sentence by counting the number of times the user has used the word “sad” in his/her tweet.

Now, let’s check out some python packages that work using this method.

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
sentence = "The food was great!"
vs = analyzer.polarity_scores(sentence)
print("{:-<65} {}".format(sentence, str(vs)))

{'compound': 0.6588, 'neg': 0.0, 'neu': 0.406, 'pos': 0.594} classifier = TextClassifier.load('en-sentiment') sentence = Sentence('The food was great!') classifier.predict(sentence) # print sentence with predicted labels print('Sentence above is: ', sentence.labels)

As we are using a universal sentence encoder to vectorize our input text we don’t need an embedding layer in the model. If you are planning to use any other embedding models like GloVe, feel free to follow one of my previous posts to get a step by step guide. Here I will just build a simple model for our purpose.

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