This is a project on analysis and Topic modelling / document tagging of BBC Articles with LSA and LDA algorithms.

The project explores a dataset of 2225 BBC News Articles and identifies the major themes and topics present in them. Topic Modeling algorithms such as Latent DIrichlet Allocation and Latent Semantic Analysis have been implemented. Effetiveness of the method of vectorization has also been explored

This is a project on analysis and Topic modelling / document tagging of BBC Articles with LSA and LDA algorithms.

A Project on Topic Modeling using alogoriths like LSA/LSI, LDA, NMF on RACE dataset

Topic Modeling is a way of finding out what topics are in a collection of texts.

This project helps a mobile brand understand customer sentiment and key discussion topics by analyzing Amazon reviews. Using Machine learning, it predicts review ratings and uncovers emerging topics in customer feedback.

In this project, task involves analyzing the content of the articles to extract key concepts and themes that are discussed across the articles to identify major themes/topics across a collection of BBC news articles.

Implementation of machine learning techniques including Movie Sentiment Analysis, Recommender System, Image Recognition and Classification

Unlabeled directed graph mining project from Co-occurrence graph of Document using gSpan algorithm based on Apache Spark

Topic modeling with python and sckit-learn

Topic Modelling of the Twitter Trends using LDA [Latent Dirichlet Allocation] technique.

Supervised and Unsupervised latent space models

Topic modeling with my algorithm ADS-LDA using Gibbs sampling in Scala and Apache Spark

Word Frequency and Topic Cluster Analysis of >600 Reviews of Works and Plays by Henrik Ibsen

Topic modeling and sentiment analysis of the Seinfeld scripts from all 9 seasons

Python script analyzing 130K presidential documents to calculate presidency similarity in terms of most covered topics.

A two-day corpus linguistics and topic modelling workshop for the University of Tartu Digital Methods in Humanities and Social Sciences Summer School on 22-23 August 2018.

Code and Data for analyzing #dvpw18 on Twitter

Using distinct Natural Language Processing (NLP) approaches for the extraction of features relating to diet and physical activity, and understanding their temporal and spatial variation in the US.