SpamGuard is an intelligent SMS filtering system designed to detect and filter spam messages using machine learning techniques.

The system is implemented to scrape data from a booking website, perform Emotion Analysis on the reviews of the selected hotel and visualized the result over a time axis. R is used to implement the system and Shiny library is used to develop the Front-end.

This notebook contains entire text preprocessing pipeline for NLP problems. The ready-to-use functions require NLTK and SKlearn package installations. It also contains some prominent text classification models.

Proyek ini bertujuan untuk mengembangkan model yang dapat menentukan apakah produk dalam review direkomendasikan atau tidak berdasarkan teks ulasan yang diberikan oleh para pengulas.

NLP LSTM model to predict python codes (Text prediction) (Tokenized special characters)

Performed PySpark based text pre-processing including lemmatization, POS tagging and UDF functions on customer feedback. Computed and visualized sentiment score to identify areas of improvements.

The comparison between different embeddings (TF-IDF, USE, and TF-IDF + USE) and various classifiers provides valuable insights into the performance of different techniques for sentiment classification.

An ATS app automates the recruitment process by managing job applications, parsing resumes, and screening candidates. It centralizes candidate data, facilitates collaboration among hiring teams, and ensures compliance with hiring regulations.

Code in R to classify the news articles depending on whether their content is about financial fraud or complementary subjects

Data Science - Text Mining Work

Text Preprocessing and NLP techniques

Retrieval Information System

Here we will apply natural language process using variety packages like keras, textblob, genism etc

Preprocess the 500K amazon reviews from raw texts into squences and fit a LSTM model with embedding layer, to determine a new review, tweet, or any product related message positive, negative.

The purpose of this project is to connect an ontology(from Protégé) to RStudio and retrieve the details of each class of the ontology on which we have analysed and retrived 5 keywords for each class using tf–idf and also calculate the page rank based on a query search using cosine distance.

Rule-based chatbots 🤖 are pretty straight forward as compared to learning-based chatbots. There are a specific set of rules. If the user query matches any rule, the answer to the query is generated, otherwise the user is notified that the answer to user query doesn't exist. One of the advantages of rule-based chatbots is that they always give ac…

Topic modelling of ML papers using LDA model in order to improve other methods such us Keyword extraction with TF-IDF method.

This repository is for all the method involve in building advance and industrial application of the Natural language processing. This repository is only for learning purposes. This repository is based on the books "Natural Language Processing Recipes" by Akshay Kulkarni, Adarsha Shivananda

Fizz buzz is not yet another implementation of Fizz buzz. Fizz buzz is a demo of an homogeneous and consistent development and documentation environment.

This is project is based on the text classification using NLP .