How You Can Organize Reviews Across Platforms to Understand How Your Customers Feel and Optimize Your Offerings Sentiment analysis can help you measure how your customers feel about your products or services.

Having in place a customer feedback program can be of immense benefit for any brand. However, you must gear up for the challenges of conducting sentiment analysis in this era of big data. While the data will be there for harvesting, the problem associated with big data will become more pronounced in the post-COVID-19 world.

Data that flows freely can be a combination of structured, semi-structured and unstructured, which is why analyzing this data can pose challenges. The volume of data we expect in the post-COVID-19 world will not be easy for humans to analyze and utilize effectively, and we'll need to integrate artificial intelligence (AI) to help.

However, in sentiment analysis using product review data, you can deploy Natural Language Processing and computational linguistics to study emotions in subjective information. To find out what customers say and feel about their products or services, brands have always resorted to online reviews.

Fortunately, sites like Capterra, G2Crowd and Trustpilot have made this relatively easy. They collect public reviews about different products. You can also use the avenue created by e-commerce stores such as Amazon and eBay to gather reviews people leave about their experiences with your product.

These reviews are mostly unstructured and without employing AI, you end up expending hours of man labor to make sense of the data. Social media presents another opportunity for you to gather the thoughts of people about your product.

The fact that these platforms are free does not make them very reliable for this purpose. The comments may lack authenticity, so you may find it difficult to analyze these comments into positive, negative or neutral.

Deploying machine learning into sentiment analysis using product review data

Trying to analyze the unstructured data you collect from the review sites can be a herculean task, however, natural language processing and machine learning have become useful tools for this. It would not have been easy without AI to pull and analyze over 213,611 reviews, out of which Revuze used AI to extract 493,422 valuable quotes.

You can train machine learning tools to identify the difference between context, sarcasm and misapplied words. You now have several techniques and complex algorithms such as linear regression, naive Bayes, and support vector machines (SVM) that can be used to detect user sentiments.

The tools enable you to analyze these reviews into positive, negative or neutral within a short time, as well as gain actionable insights.

From the insights you gain from the reviews, it becomes easy for you to:

• Determine what your customers like and dislike about your product.
• Have a comparison level with your competitors
• Obtain real-time product insights.

Product rating

Another source through which you can obtain data for your sentiment analysis of customer product review is product rating. Usually, customers rate your product on a scale of one to five depending on the level of satisfaction they derive from it. While a rating of one means that a customer is very dissatisfied with the product, five means that the customer is highly satisfied. This is another form of a product review.

You can get relevant data from e-commerce stores for product ratings; Google Play and Apple App store, on the other hand, display the ratings of apps along with user comments. Sentiment analysis can then be conducted on the comments in the product rating system to detect the hidden nuances.

Machine learning enables you to analyze the comments with the help of your database that must have sentiment-based words that include both positive and negative keywords. The system will determine if the product is bad, good, better or worse after comparing it with the keywords.

Amazon and APIs product reviews

You can also integrate machine learning into the sentiment analyses of APIs and Amazon product reviews. For instance, Twitter releases three different versions of application programming interfaces (APIs) for researchers and developers: the REST API, the Search API and the Streaming API.

When developers use APIs to develop their applications, sentiment analysis can easily be carried out with the integration of large volumes of social data.

Many Amazon customers trust reviews from the e-commerce shop, and that is an opportunity you can't afford to miss. If you have a high number of reviews, that suggests the product is popular, and when many of the reviews are positive, the product is of high quality and sits well with customers.

Sentiment analysis uses machine learning tools that can interpret more than mere definitions. It detects and tags the emotions in the text.

Conclusion

Sentiment analysis may still be a novel technology, but it has great potential. You can deploy it to understand how consumers feel about your products or brand.

The data you need is readily available. All you need is to visit review sites, social media platforms, app stores and e-commerce stores to gather user sentiment data. The business world is becoming more competitive every day; by using sophisticated machine learning algorithms, you can convert unstructured data into structured data.

Sentiment analysis using product review data is what you need to improve your customer base and stay relevant in the market.