{"id":8995,"date":"2020-11-08T10:22:24","date_gmt":"2020-11-08T10:22:24","guid":{"rendered":"https:\/\/www.revuze.it\/?p=8995"},"modified":"2020-11-08T10:22:24","modified_gmt":"2020-11-08T10:22:24","slug":"sentiment-analysis-using-product-review-data","status":"publish","type":"post","link":"https:\/\/www.revuze.it\/blog\/sentiment-analysis-using-product-review-data\/","title":{"rendered":"Sentiment Analysis Using Product Review Data"},"content":{"rendered":"

Sentiment analysis refers to the use of Natural Language Processing and computational linguistics to study emotions in subjective information.<\/span><\/p>\n

Previously, we discussed the importance of <\/span>sentiment analysis<\/span><\/a>. In this article, we will learn how to use sentiment analysis using product review data.\u00a0<\/span><\/p>\n

Let us find out the ways.<\/span><\/p>\n

Sentiment Analysis of Online Product Reviews<\/b><\/h2>\n

Every brand is concerned about what customers say about their products or services. For this, they try to find out the online reviews of their products. They do it in two ways:<\/span><\/p>\n

 <\/p>\n

1- Review Sites<\/b><\/h3>\n

There are specific sites like Capterra, G2Crowd, Trustpilot, and similar ones that collect public reviews about different products.\u00a0<\/span><\/p>\n

There are eCommerce stores like Amazon and eBay where people leave reviews about their experience with the product. Companies often refer to these sites to evaluate customer feedback.<\/span><\/p>\n

However, the reviews present on these sites are unstructured and not easy to understand. Companies have to devote hours of manual labor to bring the data into a structured format and analyze the data.\u00a0\u00a0<\/span><\/p>\n

 <\/p>\n

2- Social Media<\/b><\/h3>\n

There are social media platforms like Facebook, Twitter, LinkedIn, Instagram, Pinterest, and Reddit<\/span> where<\/span> people share their thoughts freely.\u00a0<\/span><\/p>\n

Then, there are other social platforms like forums and Q&A sites where people engage in conversation on specific topics.\u00a0<\/span><\/p>\n

However, collecting data about a particular product from the discussions happening around social streams is not easy.\u00a0<\/span><\/p>\n

Firstly, the authenticity of the opinion cannot be guaranteed as people can freely post their content. For instance, the posts can be spam posted using fake accounts.\u00a0<\/span><\/p>\n

Secondly, the expression of the opinion is not always clear. Sometimes, it is difficult to say if an opinion is positive, negative, or neutral.<\/span><\/p>\n

 <\/p>\n

Sentiment Analysis of Customer Product Reviews Using Machine Learning<\/b><\/h2>\n

The data that you collect via review sites and social channels are all in an unstructured format, which is difficult to analyze. This is where Natural Language Processing and machine learning is so useful.\u00a0<\/span><\/p>\n

Machine learning tools are trained to learn the difference between context, sarcasm, and misapplied words.\u00a0<\/span><\/p>\n

Several techniques and complex algorithms such as Linear Regression, Naive Bayes, and Support Vector Machines (SVM) are used to detect user sentiments.<\/span><\/p>\n

Using these techniques, the tool can separate the reviews into tags – positive, negative, or neutral. This way, you can obtain the insights within minutes.<\/span><\/p>\n

For example, review insights platforms like <\/span>Revuze<\/span><\/a> automate your product review analysis by understanding the voice of the customer using qualitative eCommerce opinion insights.\u00a0<\/span><\/p>\n

When you have insights that describe consumer needs, you can use them to:<\/span><\/p>\n