What are the cons and potenzial problems of using TextBlob to perform sentiment analysis? How could they be solved?
Most of the Challenges in NLP sentiment analysis tasks are semantic ones like Irony and sarcasm ambiguity in th text,Multipolarity...
Thay why TextBlob may not yield the best resulat depending on your text and if it contains multiples languges , you can add new models or languages through extensions .
I know that TextBlob ignore the words that it doesn’t know, and it will consider words and phrases that it can assign polarity to and averages to get the final score.
Are there any other problems and defects that I don't know about?
Also, I would like to know how it is possible to fix them.
Considering that we can use TextBlob both with a dictionary and through machine learning, I think a solution could be to use a larger dictionary and improve the train set.
Are my intuitions right?