Creating Self Learning Sentiment Dictionary

[esanjaysaini]

Hi Python lovers,

I am planning to build a self-learning dictionary of sentiment word with their sentiment label.

I am able to identify the sentiment words by using POS tags but not able to label those words as positive, negative or neutral.

For example: "The food was not good" is the sentence, and I have extracted "not good" from the sentence as sentiment word by using the POS tag. Now I want to label this as negative and add it to my new dictionary for future use.

my preference to do this project/task is by not using any pre-defined dictionary/word bank/any pre-defined sentiment analysis package.

I am seeking your views to know the way to label it without using any pre-defined dictionary or with pre-defined dictionary.

Currently, I have explored Word embedding, Skip through n-gram model for this. I have also used a pre-defined dictionary to train the model by using some supervised learning model like Xgboost, KNN, Naive Bayes classifier. I have used some unsupervised model like k-mean to predict the label by using the words.
Still not able to get the results.

If You know any other way or some input to apply with any of above-used models to label word as positive, negative or neutral then please suggest.

Thanks in advance for your time and inputs...

[ThomasL]

Have a look at Yann LeCuns Webpage
and study his links about NLP.