Pinned Repositories
Amazon-product-details-scrapper
Script to scrape the product details of any search input.
Computational-Linguistics
Projects related to Computation Linguistics
Covid-statistics
Plot of the latest statistics of Covid-19 of any country.
Cricket-Stats-scraper
This project aims at scrapping dataset for any cricket playing country.
Genre-Specific-News-Text-Generation-using-Markov-Chains
A simple Markov chain to generate text.
lexical_diversity
This is a simple Python package for calculating a variety of lexical diversity indices
Linguistic-features-of-text
This repository contains codes for extracting various linguistic features of text.
MachineLearning_projects
Applying the fundamental concepts of Machine Learning in python
News-Headlines-Generation-using-LSTM
LSTM model to generate headlines built on character-based modeling.
News-Headlines-Sentiment-Analysis
Sentiment analysis on current News Headlines using nltk and text-blob
armankazmi's Repositories
armankazmi/Cricket-Stats-scraper
This project aims at scrapping dataset for any cricket playing country.
armankazmi/Amazon-product-details-scrapper
Script to scrape the product details of any search input.
armankazmi/Covid-statistics
Plot of the latest statistics of Covid-19 of any country.
armankazmi/Linguistic-features-of-text
This repository contains codes for extracting various linguistic features of text.
armankazmi/MachineLearning_projects
Applying the fundamental concepts of Machine Learning in python
armankazmi/News-Headlines-Generation-using-LSTM
LSTM model to generate headlines built on character-based modeling.
armankazmi/Computational-Linguistics
Projects related to Computation Linguistics
armankazmi/Genre-Specific-News-Text-Generation-using-Markov-Chains
A simple Markov chain to generate text.
armankazmi/lexical_diversity
This is a simple Python package for calculating a variety of lexical diversity indices
armankazmi/News-Headlines-Sentiment-Analysis
Sentiment analysis on current News Headlines using nltk and text-blob
armankazmi/Sentiment-Analysis-On-Hindi-Reviews
We have used 250 sentences of movie reviews available for research from IIT bombay and also crawled and manually annotated 750 reviews from jagran.com, In total 1000 reviews. After preprocessing the dataset, We generate the featureset as a vector-based approach using Term frequency, tfidf for unigrams and bigrams. Then we used three approaches to predict the sentiment of a review. Approaches used are Resource based, In-language semantic analysis and Machine Translation based semantic analysis.