What is Sentiment Analysis and How to Do It Yourself

There’s a couple of definitions, be it by Wikipedia, by Brandwatch, by Lexalytics, or any other sentiment analysis provider. All in all, sentiment analysis boils down to one thing: It’s the process of analysing online pieces of writing to determine the emotional tone they carry. In simple words, sentiment analysis is used to find the author’s attitude towards something. Sentiment analysis tools categorize pieces of writing as positive, neutral, or negative. Some tools offer sentiment score which helps with the gradation of particular emotions.

Here’s an example of a negative piece of writing because it contains hate.

What is sentiment score?

Sentiment score is a scaling system that reflects the emotional depth of emotions in a piece of text. It detects emotions and assigns them a particular value, for example, from 0 up to 10 – from the most negative to most positive.

Why sentiment analysis is important?

First of all, sentiment analysis saves time and effort because the process of sentiment extraction is fully automated – it’s the algorithm that analyses sentiment data and so human participation is sparse.

Can you imagine browsing the Web, finding relevant texts, reading them, and assessing the tone they carry MANUALLY? Madness.

Secondly, sentiment analysis is important because emotions and attitudes towards a topic can become actionable pieces of information useful in numerous areas of business and research.

Thirdly, sentiment analysis is becoming a more and more popular topic as artificial intelligence, machine learning and natural language processing technologies that are booming these days.

Fourthly, as the technology develops, sentiment analysis will be more accessible and affordable for the public and smaller companies as well.

And lastly, sentiment analysis tools are becoming smarter with every day. The more they’re fed with data, the smarter and more accurate they become in sentiment extraction.

What is sentiment analysis used for?

Sentiment analysis and opinion mining find numerous applications in e-commerce, marketing, advertising, politics, and research. Let’s have a closer look at how text analysis benefits these areas.

Brand reputation management

The Internet is where consumers talk about brands, products, services, share their experiences and recommendations. Social media, review sites, blogs and discussion forums are boiling with opinions which, if collected and analyzed, are a rich source of business information.

When it comes to brand reputation management, sentiment analysis can be used for brand reputation management to analyze web and social media opinions about a product, a service, a marketing campaign or a brand.

Online sentiment analysis helps to gauge brand reputation and its perception by consumers.