Chaitanyasuma Jain - OpenGenus IQ: Learn Algorithms, DL, System Design

Different Word Representations

We have discussed the different word representations such as distributional representation, clustering based representation and distributed representation with several sub-types for each representation.

Machine Learning (ML)

Machine Learning Approach for Sentiment Analysis

The Lexical methods of Sentiment Analysis, even though easy to understand and implement, are not proven to be very accurate. Thus, we discuss the Machine Learning approach for Sentiment Analysis, focusing on using Convolutional Neural Networks for Sentiment Analysis.

Machine Learning (ML)

Lexicon based Sentiment Analysis

Lexicon-based Sentiment Analysis techniques, as opposed to the Machine Learning techniques, are based on calculation of polarity scores given to positive and negative words in a document.

Machine Learning (ML)

Sentiment Analysis Techniques

Sentiment Analysis is the application of analyzing a text data and predict the emotion associated with it. This is a challenging Natural Language Processing problem and there are several established approaches which we will go through.

Machine Learning (ML)

Implement Document Clustering using K Means in Python

In this article, we discuss the implementation of concepts like TF IDF, document similarity and K Means and created a demo of document clustering in Python

Machine Learning (ML)

TextRank for Text Summarization

TextRank is a text summarization technique which is used in Natural Language Processing to generate Document Summaries. It uses an extractive approach and is an unsupervised graph-based text summarization technique based on PageRank.

Machine Learning (ML)

Text Summarization Techniques

Text Summarization is the process of creating a compact yet accurate summary of text documents. In this article, we will cover the different text summarization techniques.

Machine Learning (ML)

Document Clustering using K Means

In this article, we use ideas from TF IDF and similarity metrics to use K Means clustering algorithm to cluster documents.

Natural Language Processing (NLP)

Find similarity between documents using TF IDF

We will follow NLP techniques like TF IDF to achieve document similarity in this article. Did you know that even today 4 out of 5 systems use NLP techniques to deal with document similarity?

Software Engineering

Learn to use Queue in Java Collections Framework

In this article, we will take a look at how to use the Queue class in Java Collections library. This is useful as one can directly use queue without natively implementing it

Software Engineering

Understanding TF IDF (term frequency - inverse document frequency)

tf-idf stands for Term Frequency - Inverse Document Frequency. It is a 2 dimensional data matrix where each term denotes the relative frequency of a particular word in a particular document as compared to other documents. This is a widely used metric in Text Mining and Information retrieval