CODEWITHSUNDEEP
machine-learningdeep-learningnlptext-classification
Arnav Das
Arnav Das

Reputation: 85

Is there any model/classifier that works best for NLP based projects like this?

I've written a program to analyze a given piece of text from a website and make conclusory classifications as to its validity. The code basically vectorizes the description (taken from the HTML of a given webpage in real-time) and takes in a few inputs from that as features to make its decisions. There are some more features like the domain of the website and some keywords I've explicitly counted.

The highest accuracy I've been able to achieve is with a RandomForestClassifier, (>90%). I'm not sure what I can do to make this accuracy better except incorporating a more sophisticated model. I tried using an MLP but for no set of hyperparameters does it seem to exceed the previous accuracy. I have around 2000 data points available for training.

Is there any classifier that works best for such projects? Does anyone have any suggestions as to how I can bring about improvements? (If anything needs to be elaborated, I'll do so.)

Any suggestions on how I can improve on this project in general? Should I include the text on a webpage as well? How should I do so? I tried going through a few sites, but the next doesn't seem to be contained in any specific element whereas the description is easy to obtain from the HTML. Any help?

What else can I take as features? If anyone could suggest any creative ideas, I'd really appreciate it.

Upvotes: 0

Views: 245

Answers (1)

Phil Chae
Phil Chae

Reputation: 1136

You can search with keyword NLP. The task you are facing is a hot topic among those study deep learning, and is called natural language processing.

RandomForest is a machine learning algorithm, and probably works quite well. Using other machine learning algorithms might improve your accuracy, or maybe not. If you want to try out other machine learning algorithms that are light, it's fine.

Deep Learning most likely will outperform your current model, and starting with keyword NLP, you'll find out many models, hopefully Word2Vec, Bert, and so on. You can find out all the codes on github. One tip for you, is to think carefully whether you can train the model or not. Trying to train BERT from scratch is a crazy thing to do for a starter, even for an expert. Try to bring pretrained model and finetune it, or just bring the word vectors.

I hope that this works out.

Upvotes: 1

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