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I aim in this project to analyze the sentiment of tweets provided from the Sentiment140 dataset by developing a machine learning sentiment analysis model involving the use of classifiers. The performance of these classifiers is then evaluated using accuracy and F1 scores.
Webapp para classificar comentários (positivos, negativos e neutros) advindos do Facebook usando Natural Language Toolkit (NLTK) + Django e Bootstrap na interface Web.
NLP Project for SDAIA T5 Data Science Bootcamp. This project consists of sentiment analysis for hotel reviews and classification algorithms based on that. Also, the project has word clustering models and a hotel recpmmendation system based on the nationalities and the reviewers' scores.
There are three classes InfoTheory, CompVis and Math. These can occur in any combination, so an article could be all three at once, two, one or none. The job is to build text classifiers that predict each of these three classes individually using the Abstract field.
Bernoulli and Multinomial Naive Bayes classifiers are trained and tested on different datasets. We present and compare the accuracy scores for both Bernoulli NB and Multinomial NB models.
Fake News Detection** is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake.
The Toxic Comment Classifier using Natural Language Processing (NLP) is a project designed to detect and categorize toxic, severe toxic, identity hate, obscene, threat, and insulting comments within digital communication platforms. It may help to promote a healthier online discourse by identifying and flagging potentially harmful content.
We picked up a data from Kaggle regarding musical instrument review. We applied Machine Learning algorithms to extract meaningful data that concludes sentiment of consumer.