NLP using NLTK python library

NLP starter kit

Solve your natural language processing problems with smart deep neural networks

A basic machine learning model built in python jupyter notebook to classify whether a set of tweets into two categories: racist/sexist non-racist/sexist.

Instance of CBOW(Continuous Bag Of Words)-bigram model

Text Preprocessing and NLP techniques

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.

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.

Predicting Political Ideology of Twitter Users.

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.

Retrieval Information System

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

SoftUni cource | Software University | Nezhdie Shaip

This is an NLP and Flask-based application which involves predicting the sentiments of the sentences as positive or negative. The classifier is trained on a huge dataset of IMDB movies reviews. The model is then hosted using Flask to be used by end-users.

Twitter-Sentiment-Analysis-Chandigarh University

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

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.

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…

Implemented Text Summarization by using Text Ranking(simple graph based technique) and Sq2Sq Encoder Decoder Model