I'd like to perform a textual/sentiment analysis.
I was able to analyse samples with 3 labels:
(positive, neutral, negative) and I used algorithms such as SVM, Random Forest, Logistic Regression and Gradient Boosting.
My script works correctly and with the cross validation I can take the best algorithm among the 4.
I use supervised algorithms with the python function "Countvectorizer"
But my boss typed "NLP" on the internet and looked at some articles.
He told me : "These 3 outputs are not enough, I want a complete semantic analysis that can explain the global meaning of the sentence"
He didn't seem to have a preference between supervised and unsupervised algorithms.
He told me that he wanted an algorithm able to tell that "The company president is behind bars" is equivalent to "the CEO is in jail".
So do you have any idea how one could perform that ? And how to implement it in Python? I guess we need a great database full of words, I know this is not a very specific question but I'd like to present him all the solutions.
What scares me is that he don't seem to know a lot about it, for example he told me "you have to reduce the high dimension of your dataset" , while my dataset is just 2000 text fields.
Thank you very much for your answers :)