winkjs-org's notebooks

When diacritic marks in text make search & comparison impossible, wink-helpers comes to rescue!

Effective tokenizing is all about handling sentences that contain features beyond words & punctuations such as e-mails, mentions, emojis, emoticons, hashtags, urls and more! Here is an example:

"wink-tokenizer" is an advance yet simple to use tokenizer that supports multiple languages. Here is a simple example to get you started:

"wink-sentiment" handles negation intelligently; for example, phrase "good product" will get a positive score whereas "not a good product" gets a negative score. Here is a little more complex example:

Just give any phrase and checkout the sentiment score. A positive score means a positive sentiment, whereas a negative score indicates a negative sentiment. Neutral sentiment is signalled by a near zero score.

When the person name column in your database table has title, degree, etc. sticking on, here is a simple script to extract the names only!