The real time feature is very cool. But when I went through the code. Something catch my attention.
The model used in Stocktalk is nltk.sentiment.sentiment_analyzer module. In the documentation, we can see that the tool is not really state-of-the-art.
(html comment removed: StartFragment)A SentimentAnalyzer is a tool to implement and facilitate Sentiment Analysis tasks using NLTK features and classifiers, especially for teaching and demonstrative purposes.
The real-time request and pyplot is very sophisticated and efficient. I would like to see if the developer can create a Stocktalk 2.0 featuring a more mature technique like wod2vec
Why would wod2vec be superior to NLTK? NLTK is not just used for teaching purposes, it's widely regarded as one of the best open source libraries for NLP. The model uses NLTK, yes, but NLTK is a massive library. It specifically uses the Vader Sentiment trained model which is an amazing source of data with thousands and thousands of data points.
The real time feature is very cool. But when I went through the code. Something catch my attention.
The model used in Stocktalk is nltk.sentiment.sentiment_analyzer module. In the documentation, we can see that the tool is not really state-of-the-art.
The real-time request and pyplot is very sophisticated and efficient. I would like to see if the developer can create a Stocktalk 2.0 featuring a more mature technique like wod2vec
Why would wod2vec be superior to NLTK? NLTK is not just used for teaching purposes, it's widely regarded as one of the best open source libraries for NLP. The model uses NLTK, yes, but NLTK is a massive library. It specifically uses the Vader Sentiment trained model which is an amazing source of data with thousands and thousands of data points.