What is Sentiment Analysis?

Definition, Benefits, and tools

What is Sentiment Analysis?

Definition of Sentiment Analysis

Sentiment Analysis is a Natural Language Processing (NLP) technique to determine whether data is negative, positive, or neutral. This technique is used for business branding specifically to understand customer needs, Feedback, and Monitor the Brand.

For instance, Imagine that you are on Twitter and you want to share your experience buying on a marketplace even if you just type an emoji, NLP can determine some level of satisfaction you were given by the Marketplace. Incredible, isn't it?

Benefits of Sentiment Analysis for business

  1. Learn what makes customers happy or frustrated.

  2. Discovering insights from Data Analytics based on sentiments.

  3. Create products customized to customers' needs.

  4. Monitor Brand

Tools for Sentiment Analysis Development

  1. Sikit Learn is an open-source Machine Learning library that supports supervised and unsupervised learning.

  2. NLTK is another open-source computation linguistics using python.

  3. Spacy is a free and open-source Natural Language Processing developed in Python.

  4. Tensorflow is an end-to-end Machine Learning platform by Google.

  5. Keras is a deep-learning API written in python.

  6. OpenNLP is a Machine Learning for the processing of natural text.

Sentiment Analysis Platforms

  1. NetBase Quid

  2. Hootsuite

  3. IBM Watson

  4. MonkeyLearn

  5. Google Cloud Natural Language API


Sentiment Analysis is an interesting field of Natural Language Processing (NLP) that can determine people's sentiments from social media about brands, organizations, products, and so on. This technique can be developed by those tools mentioned but also you can try the platforms that lead sentiment analysis.

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