Artificial Intelligence – Sentiment Analysis Using NLP

Sentiment Analysis Using NLP

Artificial Intelligence is becoming more and more prominent in our everyday life. From Google Assistant to Apple’s Siri, we can interact with computers, smartphones, and other devices as if they were human beings.

However, while a computer can answer and respond to simple questions, recent innovations also let them learn and understand human emotions.

One of the latest uses of Artificial intelligence is sentiment analysis using natural language processing (NLP).

To do a sentiment analysis, you need natural language processing and machine learning programs. These programs can analyze text to find certain emotions or moods that people express through their writing.

The goal of sentiment analysis is to understand what someone feels about something and figure out how they think about it and the actionable steps based on that understanding.

How Machine Learning Influences Sentiment Analysis

There are different machine learning (ML) techniques for sentiment analysis, but in general, they all work in the same way.

You give the algorithm a bunch of texts and then “teach” it to understand what certain words mean based on how people use those words together.

It’s quite similar to how we learn languages as children. Humans learn and understand a language because they are exposed to it at an early age.

Once enough data has been gathered, these programs start getting good at figuring out if someone is feeling positive or negative about something just through analyzing text alone.

One common type of NLP program uses artificial neural networks (computer programs) that are modeled after the neurons in the human brain; this is where the term “Artificial Intelligence” comes from.

These neural networks try to learn how different words relate to each other, like synonyms or antonyms. It will use these connections between words and word order to determine if someone has a positive or negative tone towards something.

How Sentiment Analysis is Used in the Real World

Sentiment analysis can be used for many applications. Some typical applications include:

  • Doing market research on what people think about certain brands or products by analyzing their social media posts, emails and blog comments.
  • Writing customer service emails to respond to specific customer complaints and issues by automatically generating responsesbased on demographics using chatbots.
  • Online reviews to detect fake or fraudulent reviews.
  • Understanding what changes need to be made with existing products based on reviews and comments.

Using NLP for Sentiment Analysis

NLP is used because it can understand phrases, not just individual words, by capturing the semantic meaning behind them.

This allows machines to analyze things like colloquial words that have different meanings depending on the context, as well as non-standard grammar structures that wouldn’t be understood otherwise.

Because social media posts use a wide variety of abbreviations, acronyms, emojis and other symbols, these cannot usually be parsed by computer programs so they must rely on contextual clues from surrounding sentences rather the words themselves.

Types of Sentiment Analysis

Sentiment analysis software looks at how people feel about things (angry, pleased, etc.). Urgency is another element that sentiment analysis models consider (urgent, not urgent), and intentions are also measured (interested v. not interested).

You may define and customize your categories to meet your sentiment analysis needs depending on how you want to read consumer feedback and queries.

Some of the different types of sentiment analysis include:

  • Fine-grained sentiment analysis – here, you can use sentiment analysis with a five-point scale from very positive (5) to very negative (1). Think about Facebook in this scenario. At one point in time, Facebook only allowed users the option of “liking” a post. Now there are many different sentiments available, including negative sentiments like angry and sad.
  • Emotion detection – helpful in analyzing specific words.Emotion detection, can quickly flag customer complaints and problems to leaders.
  • Aspect-based sentiment analysis – can look for specific features in a product review and make a judgment on how consumers view those features.

How Does Sentiment Analysis with NLP Work?

At the core of sentiment analysis is NLP – natural language processing technology uses algorithms to give computers access to unstructured text data so they can make sense out of it.

To do this, the algorithm must be trained with large amounts of annotated data, broken down into sentences containing expressions such as ‘positive’ or ‘negative´.


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Sentiment Analysis

To train the algorithm, annotators label data based on what they believe to be the good and bad sentiment.

For example, a tweet mentioning that you are happy about an update being released would be labeled as positive because of the word “happy.” If it said how disappointed someone is with your product, it could have negative annotations.

Once training has been completed, algorithms can extract critical words from the text that indicate whether the content is likely to have a positive or negative tone. When new pieces of feedback come through, these can easily be analyzed by machines using NLP technology without human intervention.

Why Is Sentiment Analysis Important?

Sentiment analysis is crucial since it helps to understand consumers’ sentiments towards a product or service. Businesses may use automated sentiment sorting to make better and more informed decisions by analyzing social media conversations, reviews, and other sources.

The volume of data being created every day is massive, with 90% of the world’s data being unstructured. The amount of data is increasing on a daily basis.

There is a great need to sort through this unstructured data and extract valuable information. Sentiment analysis can be used for several purposes, including market research, customer service optimization, targeted marketing campaigns, public relations management, crisis monitoring/management, and brand reputation analysis.

Why does sentiment analysis matter? How would it help you understand how people feel about things like the products or services you offer in your work?

The answer is simple; empathy wins!

If you know what consumers are thinking (positively or negatively), then you can use their feedback as fuel for improving your product or service offerings.

You might even discover new opportunities by understanding what’s missing from the marketplace today, which could become tomorrow’s next big thing!