sentimental | simple dictionary-based sentiment analysis system | Predictive Analytics library

Sorting based on polarity

Here's one idea of how to fix it: well, we need a termination condition, right? So let's keep enough structure to know when we should terminate. Specifically, instead of producing a stream of nodes, we'll produce a stream of frontiers, and stop when the current frontier is empty.

For python-vlc, you do need VLC installed. I do not know of a way to package vlc into a python exe. I would recommend looking into independent modules, that are not just python wrappers.

Edit:

You could use the sound functions from the pygame library:

The issue isn't with your code, it is the way the model (behind the scenes) is trained and the way it works. The English model Flair uses is trained on certain datasets (movie and product reviews) based on the release. If you want to look at the model file, it is usually located in the .flair sub-folder in your home directory.

Basically, you are using a pre-trained model provided to give you the score. To get a different score, you could either build your own model, possibly add to the existing model or you could use a different model.

You could try the other models and see what results you get by replacing this line:

I have solved my problem as follows:

According to sklearn.naive_bayes.GaussianNB.fit() manual page, the second parameter is y, where:

y: array-like of shape (n_samples,)
Target values.

The target value in your case is the sentiment of your unique review. Naive Bayes is a supervised classification algorithm. "Supervised" means that you have to guide the algorithm during training (or model fitting) by providing the correct target values (or labels).

The code, as it is now, does not really make much sense. You cannot train/fit meaningfully a model with only one sample. You will need to have a dataset with many reviews to fit the model and then try to predict new samples.

There is no clear answer for this. Most nlp tasks require some form of text-preprocessing for the models to better infer on texts. However, in case of sentiment analysis, punctuation such as ! might be valuable as it indiciates emphasis on text:

I lost my purse!! might have a more negative connotation than Well, I lost my purse.

You have two ways to approach this problem:

1. You could only exclude functional punctuation like ,.; etc. and leave in the ! and the ? kind of punctuation. Then look at the performance of your sentiment analysis model.
2. Evaluate your model both before and after cleaning all punctuation. You can write some kind of grid-search functionality that would control which punctuation to remove and which not and compare the performance.

All in all, as in most machine learning problems (I assume you do sentiment analysis by using a trained model) it comes down to a particular dataset and model whether the interpunction interferes with the model's performance or not. If, however, you use some form of third party API for the analysis, you can safely let the punctuation as it is, as the third-party API will most likely handle the cleaning themselves.

Hope that this gave some intuition!

Use INSERT... SELECT syntax:

Pip defaults to installing Python packages to a system directory which requires root access.

Do you have root permissions? If so, please try to run sudo pip install.... Otherwise, consider installing the dependency to your home directory instead which doesn't require any special privileges: