Archive for the 'chatbot' Category

The rapid diffusion of social media like Facebook and Twitter, and the massive use of different types of forums like Reddit, Quora, etc., is producing an impressive amount of text data every day. 

There is one specific activity that many business owners have been contemplating over the last five years, that is identifying the social sentiment of their brand, by analysing the conversations of their users.

In this episode I explain how one can get the best shot at classifying sentences with deep learning and word embedding.

 

 

Additional material

Schematic representation of how to learn a word embedding matrix E by training a neural network that, given the previous M words, predicts the next word in a sentence. 

 

word2vec_training.png?w=702&ssl=1

 

 

Word2Vec example source code

https://gist.github.com/rlangone/ded90673f65e932fd14ae53a26e89eee#file-word2vec_example-py

 

 

References

[1] Mikolov, T. et al., "Distributed Representations of Words and Phrases and their Compositionality", Advances in Neural Information Processing Systems 26, pages 3111-3119, 2013.

[2] The Best Embedding Method for Sentiment Classification, https://medium.com/@bramblexu/blog-md-34c5d082a8c5

[3] The state of sentiment analysis: word, sub-word and character embedding 
https://amethix.com/state-of-sentiment-analysis-embedding/

 

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In this episode I have a wonderful conversation with Chris Skinner.

Chris and I recently got in touch at The banking scene 2019, fintech conference recently held in Brussels. During that conference he talked as a real trouble maker - that’s how he defines himself - saying that “People are not educated with loans, credit, money” and that “Banks are failing at digital”.

After I got my hands on his last book Digital Human, I invited him to the show to ask him a few questions about innovation, regulation and technology in finance.

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Today’s episode is about text analysis with python.
Python is the de facto standard in machine learning. A large community, a generous choice in the set of libraries, at the price of less performant tasks, sometimes. But overall a decent language for typical data science tasks.

I am with Rebecca Bilbro, co-author of Applied Text Analysis with Python, with Benjamin Bengfort and Tony Ojeda.

We speak about the evolution of applied text analysis, tools and pipelines, chatbots.

 

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