A hashtag is a keyword or phrase preceded by the hash symbol (#), widely used in social media to categorise our content or post on a sub-topic that is related. Whilst I won’t explain word embedding in its entirety, I will briefly introduce the word embedding concept through an analogous example… Analogy of dimension reduction Rather, I will direct you to these blogs post below which I have carefully curated while I was research this topic: To make this post short, I will skip explaining the details of how it works. After all, what good is word embedding if it doesn’t add any commercial value right? So for my blog post, I will focus on word embedding applications and how it helps improve your predictive model as a powerful feature. Seeing that it works to begin with, makes it worth the effort to try and understand it. Having seen the end results, I understand easier by working backwards. The way I understand a lot of these complex / abstract methodologies is through applications first.
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Despite that however, I noticed there was a lack of coverage around the applications of it, such as how to use it and apply it on various real life challenges in predictive modelling. So I’ve been reading various blogs, doing lots of research, and I have found a few ones that explains the concept really gracefully (short, sweet and easy to understand). It’s a difficult concept to grasp and it’s new to me too. Word Embedding exist for a few years now, and its known by many names such as distributed representation, Word2Vec, word vectors etc. Surprisingly, I know from speaking to various data scientist, not many people in the data science field know what it is, or how it works.
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