As the world shrinks, due to rapid growth in technology, every conceivable industry's landscape has changed over a relatively short amount of time to keep pace with technological advancement. On the consumers' products and services side, people can purchase more various goods than ever before on platforms like Amazon to Google's services.

 

With so much access and the ability to tap into a vast consumer base, businesses can see what drives consumer purchasing decisions to leverage customers' experiences before making a purchasing decision or investing in a marketing strategy. Without spending an enormous budget on marketing, online reviews will provide direct feedback on how the business can improve. Moreover, with the online economy, companies can incentivize feedback with better shipping rates or a discount. The establishment was always looking at the demographics.

 

The companies may ask the data scientists to help have an insight with their products and help with their business to keep up with competitors. Rating is fundamental in the online platform. A client may ask to Investigate the ratings. We will concentrate on the ratings and compare ratings one and two with ratings three through five. Base on this group, is the demographic change? When applying polarity analysis to the text reviews, Is the results more positive or negative? What products do you recommend to the customer to focus on or to target products? What model is appropriate for this classification? 

 

One essential benefit a company may exploit from online reviews is that the analytical insights may empower businesses to improve without spending a huge amount of money on traditional marketing research. This also creates opportunities for direct interactions between the business and its customers. For example, companies can incentivize customers to provide direct feedback by offering perks and discounts. Of course, the drawback is that negative sentiment may quickly spread from one unhappy customer to a larger crowd. However, positive feedback from former customers may very likely resonate with new customers, which resonates and realizes a purchase.  

 

 Not surprisingly, some of the most detailed feedback comes from personal experiences, and they are usually unique to individuals. Prospective customers may find this very helpful as there are always minute aspects learned from someone else's perspective. These unique aspects echo among customers, and the prospective customers may leverage reviews to make purchasing decisions and generate more feedback extolling their joy or grief. This mitigates one downside of the online clothing e-commerce -- one cannot try on the clothing before purchasing.

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With so much information in these online reviews, how can companies (and even some customers) churn through a sea of reviews to understand how people feel relatively quickly? Thankfully, machine learning algorithms were implemented to determine whether reviews en masse are related to positive or negative sentiment. Because algorithms are so fast and can run in perpetuity, companies and customers alike can look at the produced aggregated information to divine meaning. The only questions remaining are "which algorithm do you choose?", "How difficult is it to produce?" and "how accurate is it?"

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