Why Sentiment Analysis Is Important and How It Works

With the development of e-commerce, and digital technologies, sentiment analysis is becoming more and more popular. Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

Why it’s important to determine sentiment?

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

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, it’s becoming a more and more popular topic as artificial intelligence, deep learning, machine learning techniques and natural language processing technologies that are booming these days and this will go on.

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

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

How does sentiment analysis work?

There are many ways to do sentiment analysis. Many approaches use the same general idea, however:

Create or find a list of words associated with strongly positive or negative sentiment.

Count the number of positive and negative words in the text.

Analyze the mix of positive to negative words. Many positive words and few negative words indicate positive sentiment, while many negative words and few positive words indicates negative sentiment.

The first step, creating or finding a word list (also called a lexicon), is generally the most time-consuming. While you can often use a lexicon that already exists, if your text is discussing a specific topic you may need to add to or modify it.

"Sick" is an example of a word that can have positive or negative sentiment depending on what it's used to refer to. If you're discussing a pet store that sells a lot of sick animals, the sentiment is probably negative. On the other hand, if you're talking about a skateboarding instructor who taught you how to do a lot of sick flips, the sentiment is probably very positive.