Sentiment Analysis

Sentiment Analysis

What is Sentiment Analysis?

By the definition “Sentiment Analysis is a process of extracting opinions that have different polarities” every website and the applications need user feedback to enhance user experience or to decide on go or no-go strategies, this kind of feedback also knows as user sentiment which could be positive, negative or neutral and process of extracting and analyzing this kind of behavioral data is also knows as opinion mining opinion mining and polarity detection.

Sentiment Analysis is a kind of classification where the data is classified into different classes -these classes can be binary in nature (positive or negative) or, they can have multiple classes for example -happy, sad, angry, delighted etc

Algorithm to do sentiment analysis

Natural Language Processing (NLP) and different machine learning algorithms are used to analyze user behavior and its emotions for any kind of user interaction.

3 type of sentiment analysis algorithm

Automatic : kind of a trained model where systems rely on machine learning techniques to learn from data.

Rule-based : predefined rules are setup for the system to automatically perform sentiment analysis.

Hybrid : systems combine both rule-based and automatic approaches.

What are Open source API for Sentiment Analysis?


Python is top in the list of developers and companies performing machine learning and data science related work, it has got good amount of built in libraries to perform sentiment analysis from scratch.

Natural Language Toolkit

Also known as NLTK libraries are very popular to perform sentiment analysis it provide technique and tool to perform tokenizing, speech tagging, stemming and named entity reorganization.


TensorFlow is one of the dominant frameworks for machine learning with comprehensive libraries and advanced machine learning model.


This library is an industrial-strength NLP (Natural Language Processing) library in Python which can be used for building a model for sentiment analysis. It provides interesting additional functionality dependency parsing and word vectors along with deep learning.


It is a neural network library written in Python that is used to build and train deep learning models. It is used for prototyping, advanced research, and production.


This is always been a question that “Can java be used as language to write machine learning code?” off course java is not a popular choice when it comes to writing machine learning code, however there are supporting frameworks written in java like CoreNLP is Stanford’s proprietary NLP toolkit written in Java with APIs for all major programming languages. It is powerful enough to extract the base of words, recognize parts of speech, normalize numeric quantities, mark up the structure of sentences, and indicates noun phrases and sentiment, extract quotes, and many more.


R is another popular choice for writing machine learning and data analysis code, using R we can do sentiment analysis very easily.  Its most common users are statisticians and data miners looking to develop data analysis.

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