Sentiment Analysis in Python With TextBlob

Sentiment Analysis in Python With TextBlob

Sentiment Analysis in Python With TextBlob. 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.

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):

Moreover, depending on the task you're working on, it's also possible to collect extra information from the context such as the author or a topic that in further analysis can prevent a more complex issue than a common polarity classification - namely, subjectivity/objectivity identification.

For example, this sentence from Business insider: "In March, Elon Musk described concern over the coronavirus outbreak as a "panic" and "dumb," and he's since tweeted incorrect information, such as his theory that children are "essentially immune" to the virus." expresses subjectivity through a personal opinion of E. Musk, as well as the author of the text.

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