Sentiment Analysis using NLP

Sentiment Analysis using NLP

February 8, 2019 DATAcated Challenge 0

Sentiment analysis using Natural Language Processing

“This is almost correct.” Can you tell whether it is in a positive or negative sense? We all might have been in such a conundrum in our lives. This is called as sentiment analysis in technical terms. Sentiment analysis has been a crucial part of understanding the view, perspective of an audience or a person about a topic. As it can be confusing sometimes, we can use NLP (Natural Language Processing) to do sentiment analysis.

How does it work?

NLP learns the pattern of a statement and trains itself to provide a sentiment score to each word in it’s vocabulary according to the content given. We can then use this model to know the overall sentiment of the statement. Here the vocabulary has some words which don’t provide any emotion to the text and hence are removed while training. These words are called stopwords.


Sentiment analysis is aggressively used in companies where the understand the sentiment of the market for their product and try to chart a relationship with their stock prices and profits. This is a crucial part of algorithmic trading. Analysis over the news commentaries and social media text gives an overview about the state of public, which can be advantageous for marketing a new product or understanding the general consensus about an issue in politics.

As a youtuber or blogger, you can can assess your comments or feedback to know the public view about your work and can improve upon it. So, it helps in personal branding too.

In conclusion, sentiment analysis is an easy to implement and very effective NLP tools in data science.

By : Jerome Francis


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