marktechpost

A Step by Step Guide to Build a Trend Finder Tool with Python: Web Scraping, NLP (Sentiment Analysis & Topic Modeling), and Word Cloud Visualization

Monitoring and extracting trends from web content has become essential for market research, content creation, or staying ahead in your field. In this tutorial, we provide a practical guide to building ...
IEEE

A Machine Learning-Sentiment Analysis on Monkeypox Outbreak: An Extensive Dataset to Show the Polarity of Public Opinion From Twitter Tweets

Abstract: Research on sentiment analysis has proven to be very useful in public health, particularly in analyzing infectious diseases. As the world recovers from the onslaught of the COVID-19 pandemic ...
IEEE

Evaluation of rule-based, CountVectorizer, and Word2Vec machine learning models for tweet analysis to improve disaster relief

Abstract: Social media platforms, such as Twitter, are being increasingly used by people as a means of requesting help during disaster events. Machine learning techniques can be used to identify ...
GitHub

CountVectorizer out of dictionary features

Many tabular data models run CountVectorizer against categorical and text data to featurize the data. At the large scale especially, the min_df or max_features are commonly used to limit the ...
Scientific Research Publishing

Machine Learning Approaches for Classifying the Distribution of Covid-19 Sentiments ()

1 Jomo Kenyatta Unversity of Agriculture and Technology, Nairobi, Kenya. 2 Department of Statistics and Actuarial Sciences, JKUAT, Nairobi, Kenya. 3 Department of Mathematics and Actuarial Sciences, ...
GitHub

CountVectorizer Performance Problem #5306

I was doing text classification task these days, and I found the CountVectorizer / TfidfVectorizer quite slow. Then I look into the source code, I found the the function CountVectorizer._count_vocab: ...