How to organize reviews on all platforms to understand how your customers are feeling and optimize your offers
Have in place a customer feedback program can be of immense benefit to any brand. However, you need to prepare for the challenges of sentiment analysis in the age of big data. While the data will be there for harvest, the problem associated with big data will become more pronounced in the post-COVID-19 world.
Free flowing data can be a combination of structured, semi-structured and unstructured data, which is why analyzing this data can be challenging. The volume of data we expect in the post-COVID-19 world will not be easy for humans to analyze and use effectively, and we will need to integrate artificial intelligence (AI) to help.
However, in sentiment analysis using product rating data, you can deploy natural language processing and computational linguistics to study emotions in subjective information. To find out what customers say and think about their products or services, brands have always relied on online reviews.
Fortunately, sites like Capterra, G2Crowd, and Trustpilot have made this relatively easy. They collect public opinions on different products. You can also use the avenue created by e-commerce stores such as Amazon and eBay to collect the reviews people leave about their experiences with your product.
These reviews are mostly unstructured and without using AI you end up spending hours of manpower making sense of the data. Social media gives you another opportunity to collect people’s opinions on your product.
The fact that these platforms are free does not make them very reliable for this purpose. Reviews can lack authenticity, so it can be difficult to analyze those reviews in positive, negative, or neutral terms.
Deploy machine learning in sentiment analysis using product review data
Trying to analyze the unstructured data you collect from review sites can be a Herculean task, however, natural language processing and machine learning have become useful tools for this. It wouldn’t have been easy without AI to extract and analyze over 213,611 reviews, including 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 complex techniques and algorithms such as Linear Regression, Naive Bayes, and Support Vector Machines (SVMs) that can be used to detect user sentiment.
The tools allow you to analyze these reviews in positive, negative or neutral terms in a short time, as well as gain actionable insights.
From the information you get from reviews, it becomes easy for you to:
- Find out what your customers like and dislike about your product.
- Have a level of comparison with your competitors
- Get real-time product information.
Product rating
Product rating is another source through which you can get data for your sentiment analysis of customer rating of products. Usually, customers rate your product on a scale of one to five depending on how satisfied they are with it. While a rating of one means that a customer is very dissatisfied with the product, five means the customer is very satisfied. This is another form of product review.
You can get relevant data from e-commerce stores for product reviews; Google Play and Apple App Store, on the other hand, show app ratings as well as user reviews. Sentiment analysis can then be conducted on the product rating system reviews to detect hidden nuances.
Machine learning allows you to analyze comments using your database which must contain 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 to the keywords.
Amazon Product Reviews and APIs
You can also incorporate machine learning into sentiment analytics from APIs and Amazon product reviews. For example, 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 done with the integration of large amounts of social data.
Many Amazon customers trust the online store reviews, and this is an opportunity you can’t afford to miss. If you have a large number of reviews, it suggests that the product is popular, and when a lot of reviews are positive, the product is of high quality and is suitable for customers.
Sentiment analysis uses machine learning tools that can interpret more than just definitions. It detects and tags the emotions in the text.
Conclusion
Sentiment analysis may still be a new technology, but it has great potential. You can deploy it on understand what consumers think of 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 collect data on user sentiment. The business world is becoming more competitive every day; 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.
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