Machines’ key to understanding humans: how I used natural language processing to analyze human sentiment and draw conclusions.

How can machines make sense of text and why should we want them to?

Using natural language processing and machine learning to analyze customer sentiment.

An example of how natural language processing can be used to help analyze important information in reviews and improve customer experience.

Building the algorithm with NLP and ML

1. Tokenization

2. Filtering Stopwords

Here is a list of some of the most common stopwords in English

3. Stemming & Lemmatization

4. Applying Machine Learning

How can YOU use NLP and sentiment analysis to your advantage?

Check out my project!

Key Takeaways

  • There is so much data online and so much of that data is in the form of text. As a result, we need an effective way to process and make use of this text data.
  • Natural language processing can be used in parallel with machine learning to draw conclusions from verbal data.
  • We can use NLP processes like tokenization, stopword filtering, stemming, and lemmatization to prepare verbal data for analysis by traditional machine learning algorithms.
  • There are a number of different tools that you can use to create your own sentiment analysis algorithm or make use of previously existing ones.
  • The combination of both NLP and ML has immense potential to help us today and even greater potential to make an impact on the future.

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Adam Maj

Adam Maj

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