Predict personality scores of a new text

[FatemeFathii]

Hi.
How can I give a review (a new text) to this model and extract the reviewer's personality?

[amirmohammadkz]

Hi Fateme,
I think the best approach is to feed the full essays to the model (skip the psycholinguistics features) and then pass your new test sample to the trained model.

[Arkhemis]

Hi,

I'm currently trying to achieve what Fateme was aiming at. However, I'm not exactly sure of the process to accomplish it? Which of the Python script should I use to pass my sample text files (reviews of several users) to the trained model as to extract their personality? In short, what would be the required steps to infer the personality of multiple users. Thank you for your help! (and really interesting program by the way)

[saminfatehi]

Hi,

We added a new python file predictor.py to predict personality traits for new text as input. (if you want to pass a file, you can modify this code file easily.)

[di-press]

Hi @saminfatehir
Thanks for uploading predictor.py, it will be very helpful!
Please, I still need to follow all the steps in README to run predictor.py, right?
Thanks!

[saminfatehi]

You need to follow steps 1, 2, and 3 in README. Then you can run predictor.py to predict the personality traits for new input.

[Pompiia]

Thank you for the predictor.py file! However, I think there is something wrong with the classifying phase, at least on my side. For instance, asking to classify a random short text from Reddit, such as "We can only fix this in two ways: either we also waive environmental regulations to produce REMs (something I, as an environmentalist, am not wholly against if done responsibly, as we need them to manufacture green technology), or we invest in space mining. We have all the technology we need to mine in space, we just refuse to invest in it because the cost is literally astronomical", is taking forever (around ~1 hour). Is it a normal waiting time?

[saminfatehi]

Hi @Pompiia,
It takes time to create SVM models by essays dataset. If you save the model once you can use them for other times. Now I modified the predictor.py to save the models the first time.

Note that you should run the BERT server in the other terminal like step 2 on README (to get your text embedding).

[Pompiia]

It's amazing, thank you very much!

However, I'm encountering the following issue:

Traceback (most recent call last): File "predictor.py", line 177, in <module> predictions = predict(x) File "predictor.py", line 171, in predict results = predict_with_model(embeddings) File "predictor.py", line 153, in predict_with_model predicts = classifier.predict(embeddings4layers) File "C:\Users\alexa\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\svm\base.py", line 567, in predict y = super(BaseSVC, self).predict(X) File "C:\Users\alexa\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\svm\base.py", line 325, in predict X = self._validate_for_predict(X) File "C:\Users\alexa\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\svm\base.py", line 478, in _validate_for_predict (n_features, self.shape_fit_[1])) ValueError: X.shape[1] = 3072 should be equal to 3156, the number of features at training time.
Could you help me on that matter?

[kkkkangx]

@saminfatehir Hi,

I run the new added predictor.py with my own text (a 72.8Kb txt file) in my lab's computer. I also run the BERT server in the terminal like in step 2 and all workers are ready. But the output is only to print the text again and it's still running, for around 2 hours. Was the model running well?

Thanks for helping in advance!

[kkkkangx]

Here are my screenshots

[saminfatehi]

Hi @kkkkangx,
The text should get user input. When you run the original predictor.py you'll see this:

Then you should enter your custom text like this and press Enter:

[kkkkangx]

@saminfatehir Hi,

Thank you so much, I have tried and it worked.

I have another question: how could I save the trained model? Because I have over 200 texts to be predicted, I wonder if I can save the trained model and just use it the next time, which could save much time.

Thanks in advance!

[saminfatehi]

predictor.py creates and saves SVM models for the first time and uses them other times.

[kkkkangx]

@saminfatehir Hi,

It worked well. Thank you :)

If I understand correctly, the final output of SVM models is a binary variable, indicating whether the corresponding personality trait displayed in the text. Are there any problems with my understanding of the final output?

In my project, I want to get the estimated continous probability of a personality trait displayed in my text. So, I changed the line 150 of predictor.py into predicts = classifier.predict_proba(embeddings4layers)[:,1]. I am not sure if I can get the output I want (i.e. the probability)?

Looking forward to your replying.