Sentiment Analysis in Python With TextBlob
Introduction
State-of-the-art technologies in NLP allow us to analyze natural languages on different layers: from simple segmentation of textual information to more sophisticated methods of sentiment categorizations.
However, it does not inevitably mean that you should be highly advanced in programming to implement high-level tasks such as sentiment analysis in Python.
Sentiment Analysis
The algorithms of sentiment analysis mostly focus on defining opinions, attitudes, and even emoticons in a corpus of texts. The range of established sentiments significantly varies from one method to another. While a standard analyzer defines up to three basic polar emotions (positive, negative, neutral), the limit of more advanced models is broader.
Consequently, they can look beyond polarity and determine six “universal” emotions (e.g. anger, disgust, fear, happiness, sadness, and surprise):
Source: Spectrum Mental Health
Moreover, depending on the task you’re working