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[Music] hello and welcome to this video this is another video on spacey and some of the cool things you can do with it over the past couple months spacey has been putting out on twitter and on the repo all these different really cool projects and were going to explore one of them right now which comes from pandora intelligence and its called classy classification real quickly the entire idea behind this project is that you can use spacey and with just a few sample sentences you can do fuchsia text classification so that what this means is that with just a little bit of data you can actually go through and relatively quickly get a classifier up and working so what im going to do in the first part of this video is walk through the installation how you get everything set up its very simple if youve installed spacey the hard parts already done and then im going to walk through this kind of demo example that they give us and the big question were going to be asking is how well does

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Where is text classification used?

Academia, law practitioners, social researchers, government, and non-profit organisation can also make use of text classification technology. As these organisations deal with a lot of unstructured text, handling the data would be much easier if it is standardized by categories/tags.

Which deep learning algorithm is best for text classification?

TCN is an excellent alternative to recurrent architecture and has been proven effective in classifying text data. The ensemble learning-based model can help make better predictions than a single model trained independently.

How to do text classification in Python?

Following are the steps required to create a text classification model in Python: Importing Libraries. Importing The dataset. Text Preprocessing. Converting Text to Numbers. Training and Test Sets. Training Text Classification Model and Predicting Sentiment. Evaluating The Model. Saving and Loading the Model.

Is BERT good for text classification?

BERT is a very good pre-trained language model which helps machines learn excellent representations of text wrt context in many natural language tasks and thus outperforms the state-of-the-art. In this article, we will use a pre-trained BERT model for a binary text classification task.

Why is Naive Bayes best for text classification?

Since a Naive Bayes text classifier is based on the Bayess Theorem, which helps us compute the conditional probabilities of occurrence of two events based on the probabilities of occurrence of each individual event, encoding those probabilities is extremely useful.

Is Naive Bayes good for text classification?

Naive Bayes classifiers are simple and robust probabilistic classifiers that are particularly useful for text classification tasks. The Naive Bayes algorithm relies on an assumption of conditional independence of features given a class, which is often a good first approximation to real-world phenomena.

What is NLTK text classification?

A fairly popular text classification task is to identify a body of text as either spam or not spam, for things like email filters. In our case, were going to try to create a sentiment analysis algorithm. To do this, were going to start by trying to use the movie reviews database that is part of the NLTK corpus.

What is the best classifier for text classification?

Linear Support Vector Machine is widely regarded as one of the best text classification algorithms.

Which classifier to use for text classification?

Linear Support Vector Machine is widely regarded as one of the best text classification algorithms.

How to do classification in Python?

Implementing Classification in Python Step 1: Import the libraries. Step 2: Fetch data. Step 3: Determine the target variable. Step 4: Creation of predictors variables. Step 5: Test and train dataset split. Step 6: Create the machine learning classification model using the train dataset.

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How to Classify Contact Text For Free