{"id":23893,"date":"2022-09-09T17:00:11","date_gmt":"2022-09-09T17:00:11","guid":{"rendered":"https:\/\/www.revuze.it\/blog\/\/"},"modified":"2022-09-09T17:00:11","modified_gmt":"2022-09-09T17:00:11","slug":"sentiment-analysis","status":"publish","type":"post","link":"https:\/\/www.revuze.it\/blog\/sentiment-analysis\/","title":{"rendered":"Sentiment Analysis For Brand Building"},"content":{"rendered":"

They love me\u2026 they love me not.\u00a0<\/span><\/p>\n

It\u2019s a question most people ask themselves about loved ones. But what about asking this question from the position of a CEO or a data analyst?\u00a0<\/span><\/p>\n

When you do that, you\u2019re conducting sentiment analysis, albeit without stripping a flower of its petals.<\/span><\/p>\n

When building your brand, one of the most important things you can do is read your audience.\u00a0<\/span><\/p>\n

How people feel about your product is imperative to its success. And understanding the nuances of these feelings will help you get a leg up over your competitors.<\/span><\/p>\n

It\u2019s not just about a general \u201cthey love my product; they love it don\u2019t.\u201d It extends to minor details that make up your products or services and how you present them.\u00a0<\/span><\/p>\n

If there are things that rub customers the wrong way, keeping on top of them is key to success.\u00a0<\/span><\/p>\n

What customers want isn\u2019t always obvious and consistent. If something works in one place and time, there\u2019s no guarantee it\u2019ll work in another. This is certainly true in trendy industries like fashion, where there\u2019s an emphasis on culture and everything changes quickly.<\/span><\/p>\n

So how do you keep on top of consumer perception and your response to it? Especially in the internet age, where social media posts and website reviews are published every few minutes. There\u2019s simply too much data to analyze manually.<\/span><\/p>\n

That\u2019s where sentiment analysis comes in.<\/span><\/p>\n

What is sentiment analysis?<\/strong><\/h2>\n

Sentiment analysis, also known as \u201copinion mining,\u201d is the automated process of analyzing a text and interpreting the sentiments behind it.\u00a0<\/span><\/p>\n

Through machine learning and text analytics, algorithms can classify statements as positive, negative, and neutral.<\/span><\/p>\n

Companies and brands often use this process as a strategy to manage large amounts of data coming from Yelp, Twitter, Amazon, you name it.\u00a0<\/span><\/p>\n

This data allows businesses to learn more about customers’ feelings for their products and competitors\u2019 offerings.<\/span><\/p>\n

How sentiment analysis works<\/strong><\/h2>\n

Sentiment analysis relies on an AI engine powered by machine learning (ML) and natural language processing (NLP) to extract information.<\/span><\/p>\n

Machine learning allows the software to learn independently and become more accurate at predicting the outcome of analysis without being programmed for that explicit scenario. Essentially, it allows the software to \u201clearn\u201d from past examples to improve itself over time.<\/span><\/p>\n

NLP analyzes human language and the meaning behind it. This covers text segmentation, grammatical analysis, and terminology extraction.<\/span><\/p>\n

Which algorithms are used for sentiment analysis?<\/span><\/h3>\n

ML and NLP are tools to help the sentiment analysis algorithm produce the final results. There are three types of algorithms that are usually deployed:<\/span><\/p>\n