How AI Has Enhanced Sentiment Analysis Using Product Review Data

Customer feedback is great. But have you been able to turn that feedback into meaningful customer insights? A few years back, brands depended on surveys to gauge customers’ feelings about how their products were performing.  

From the product reviews, they were able to somehow get a grip on the general feeling of good, bad, or neutral response to their marketing campaign or product. There is, however, so much more information in the form of unstructured data that brands need to lay their hands on to better analyze the sentiments of their customers. 

When customers share their feelings and emotions through social media, blogs, forums, reviews, or online news commenting, they express their opinions. As there are different brands, so also are there a variety of products manufactured by these brands, and being able to provide relevant reviews to the consumers is very important. 

The number of reviews generated by customers on a particular product or a brand is increasing at mammoth rates; this is more like handling big data. Once you can analyze the reviews based on the sentiment of customers into positive, negative, or neutral sentiment, you have a  better sentiment orientation of the review, and you are in a position to make a better product judgment. 

Apart from the brand, the segregation of reviews based on their sentiment will also equip future buyers to evaluate positive and negative feedback constructively and arrive at better decisions based on their needs. In the advanced sentiment analysis using product review data,  comments are analyzed to detect the hidden sentiments.  

Big data

The evolution of data analytics has enabled us to unravel the hidden patterns in data. Apart from volume, velocity, and variety that define big data, veracity and value are two more Vs that play an essential role in our understanding of big data. 

The volume and the relentless rapidity at which data are being generated every day have greatly surpassed the computing capacity of many IT departments. 

E-commerce websites such as Amazon and BestBuy are examples of sites where you can scrape a large set of products and their reviews. Armed with these reviews, you should be able to determine consumer behavior and make informed decisions. 

Product reviews are based on semi-structured, structured, or unstructured data. However, you may have to devote hours of manual labor to bring the data into a structured format and analyze the data. 

Otherwise, you will have to use sophisticated machine learning algorithms to convert unstructured data into structured data. You end up with valuable business insights after the filtration of the irrelevant data.

Big data has transformed the way businesses are conducted, they now flourish and can easily improvise based on evidence as against when they depended more on ordinary intuition. 

Brands are enabled to gain useful insights on better targeted social influencer marketing, segmentation of customer base, recognition of sales and marketing opportunities, detection of fraud, quantification of risks, better planning and forecasting, as well as understanding consumer behavior.

Sentiment analysis of customer product reviews using machine learning

In a very simple term, sentiment analysis means the identification of product reviews based on positive, negative, and neutral nuances. You can perform sentiment analysis at the following three levels: document level, sentence level, and phrase level.

When you collect data through review sites and social channels, they are usually in an unstructured format, and difficult to analyze. Natural Language Processing and machine learning come in to play vital roles here. 

Machine learning tools have been adapted to differentiate between context, sarcasm, and misapplied words. You have the opportunity to use several techniques and complex algorithms such as Linear Regression, Naive Bayes, and Support Vector Machines (SVM) to detect customers’ sentiments.

By using these techniques, the tools will enable you to separate product reviews into data tags – positive, negative, or neutral and you can then obtain the necessary insights within minutes quite unlike when you have to depend on human labor.

AI can gather mammoth insights from both unstructured data and affective computing in sentiment analysis using product review data at scale. You can then carry out predictive analytics, predict buyer response in the stock market, and manage employee engagement, which will be onerous tasks if you have to depend on intuition. 

While surveys had afforded brands the opportunity of accessing verbatim comments some years back, sentiment analysis is capable of 90% accuracy. This is not a technology you can say is just evolving; it’s rather a technology in a state of maturity that you can deploy to empower your brand, employees, and customers all at once.

AI and machine learning have enabled technologies that can review customer service calls in real-time, detecting human signals, and offering behavioral guidance to improve the quality of the interaction. They can also bring in empathy and EQ in customer engagement, a lot of people had always believed these were only possible with the human-touch. 

Where humans can be stressed up to the point of losing empathy, AI has no emotions, it can never lose its empathy—it can only grow it. And you need to believe this; you can even deploy it.in any industry or field, from manufacturing to HR.

If we combine sentiment analysis with cognitive recognition and affective computing, it has the potential to save lives. Affective computing can take sentiment analysis from text to audio and video. By correctly deploying AI, vehicles, and guns manufacturers can use affective computing to determine the state of their potential users, and if it is safe to allow somebody to operate a car or a gun.

Conclusion

We have seen sentiment analysis at work already; by using product review data and deploying advanced AI techniques, we can still harness its full potential. AI’s power is by no means trying to replace our need to understand our customers; rather it’s allowing us to utilize tools to understand them and then act on those insights for a better output.