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?

We can use a body of knowledge called natural language processing (NLP) to process verbal data into a format that machines can make use of. In many cases, we can also use a strategy called sentiment analysis to analyze this verbal data and draw conclusions from it. Sentiment analysis is the process of extracting subjective information from the text to gather data and identify the emotion and attitude behind a piece of writing.

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

Alright, now that we know what natural language processing is and why it's so useful, why don’t we take a look at a specific example where it can be used for good.

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

Let’s say we have a database of reviews about a product which consist of a verbal review about the product and a corresponding rating. We can use this data to make a useful algorithm. In our example, we’ll say that each review looks something like this:

1. Tokenization

The first step to processing our text is called tokenization. This is the process of splitting up the paragraphs and sentences in our review into individual words. This allows us to look at each word on its own to analyze how each word connects to the meaning of an entire block of text.

2. Filtering Stopwords

The next step to our natural language processing involves filtering stopwords out of our reviews. Stopwords are words that add little to nothing to the meaning of statements and thus, are irrelevant from an analysis standpoint.

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

3. Stemming & Lemmatization

The process of stemming is optional in many cases, but it often helps to improve the quality of the analysis. Stemming is the process where we reduce every word down to its most simple root or lemma. This helps to associate similar words with each other.

4. Applying Machine Learning

Depending on what kinds of results we are trying to derive from our dataset, there are a variety of different machine learning algorithms that we could use. One common algorithm used in sentiment analysis is the Naive Bayes Algorithm which can help to correlate certain words with good and bad reviews. Like this algorithm, there are numerous others that can be used for these purposes.

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

Now that you know the basics of how natural language processing and sentiment analysis works, why don’t we talk about how you can actually implement these technologies for your own uses. Armed with these tools, you will be able to make the most of the data online to help you in your own endeavors.

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.

Wait… don’t click away yet!

I’m Adam, a 16 year old passionate in technologies like artificial intelligence/machine learning, blockchain, quantum computing, and much more.

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