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,
At the end, I will also compare the performance of each of them in a common dataset.
What is sentiment analysis?
Sentiment analysis is the task of determining the emotional value of a given expression in natural language.
It is essentially a multiclass text classification text where the given input text is classified into positive, neutral, or negative sentiment. The number of classes can vary according to the nature of the training dataset.
For example, sometimes it is formulated as a binary classification problem with 1 as positive sentiment and 0 as negative sentiment label.
#natural language processing #data-analysis
