Harness The Power Of Online Reviews with Sentiment Analysis
In today’s digital world businesses need to make sense of online reviews and analyze what customers are trying to tell them. They can do this using AI-powered text analytics and sentiment analysis. One of the basic lessons that all companies should follow is that success lies in the hands of their customers. Understanding how those customers feel about your product or service is essential to financial survival and prosperity. In this blog, we understand the process of sentiment analysis on reviews and how it can help businesses improve their products and services.
Fortunately, people love to write online reviews to express their feelings about their purchase experiences. People are passionate about what they choose to spend their money on, be it a new house, a used automobile, or a night out for dinner. Think of how often you do research on Google My Business, YouTube, Instagram or Facebook before making a purchase. Or how many times have you used TripAdvisor, Expedia or Booking.com to compare hotel ratings before finalizing your trip? Before you make a purchase commitment, you want to know how others feel about their experiences with the same products and services you are about to buy.
The steady rise of online reviews, ratings websites and mobile review apps have empowered shoppers to raise their hands in pleasure or displeasure every time they feel compelled. In fact, a study says, 94% of consumers claim they read online reviews, 92% claim their purchases are influenced by them. In many cases, shoppers trust online reviews more than recommendations by their friends and family. In such a cluttered digital space it is integral that businesses like you add review sentiment analysis into your marketing strategy. This ensures that you are always strategically listening to the primary stakeholder in your success - your customer.
Why do you need product review sentiment analysis?
When a brand or business has hundreds or even thousands of reviews across various sites, retrieving them and manually examining them for sentiment can be both daunting and time consuming. To be effective, businesses need to begin looking to AI powered review sentiment analysis in order to retrieve insights from reviews quickly and accurately.
How is sentiment analysis on reviews done?
Repustate’s product review sentiment analysis API is used to find insights and relationships within your textual data. Our three-step process is simple:
1. Data Gathering:
Gather and prepare your data that you want to analyze whether it’s internal (customer feedback) or external data (reviews from rating sites). To prepare the data for text analysis, all you need to do is put it into a CSV or XLS document format.
2. Apply Review Sentiment Analysis API:
Run your input data through your sentiment analysis API. It will quickly return sentiment scores for each relevant review topic, aspect, or entity ranging from -1 for negative emotions, 0 for neutral feelings, and 1 for positive sentiment.
3. Sentiment Analysis Dashboard:
Once you receive the sentiment scoring, you can use various different data visualization tools like Power BI, Tableau, or Repustate’s Sentiment analysis dashboard to quickly turn your data into visual reports. These reports are made up of charts, graphs and tables to discover trends, patterns, and actionable insights in your data.
An aspect is a particular part or feature of business, for example, customer service, that topics can be classified under. But customer service can be broken out into various topics such as wait staff, line ups, reservations, etc. A topic or subject is the matter or subject dealt with in the review. For example, pizza is a topic that can be classified under the aspect of food. Topics are more granular or specific than aspects, and therefore the sentiments expressed about pizza are more actionable than if it was only classified as food. Entities are people, places and things such as business people, competitors, locations, etc.
Sentiment analysis for reviews, uses named entity recognition to breakout all your insights and categorize them in a way that is easy and quick. So you can put your focus on improving your business with the help of your customer feedback.
Learn about the top sentiment analysis companies.
See Repustate's sentiment analysis in action
What are the benefits of sentiment analysis on reviews?
Online Review sentiment analysis can help businesses in the following ways:
- Identify and extract how your customers feel about your business
- Classify feelings according to different parts of your business
- Easily visualize customer insights for fast analysis
- Accurately target operational improvements at problem areas
- Create baseline sentiment metrics to measure change progress
Without sentiment analysis many businesses are operating blindly, making them susceptible to competitive challenges and lost sales.
Explore other benefits of social listening.
Take a quick tour of Repustate's Review Sentiment Analysis solution
Watch how Repustate’s sentiment analysis engine tackles reviews
Conducting sentiment analysis on reviews given in surveys can be a complex task because of the ambiguity in questions and absence of context in the brief responses. In the case of restaurants, this complexity is further increased because of the diverse aspects on which a restaurant is judged. This video shows an example of how Repustate trains its sentiment analysis engine to handle the problem of ambiguity in restaurant survey responses. With the addition of a contextual parametre or “frame of reference”, Repustate’s sentiment analysis tool understands the difference between the term “better menu selection”, meaning that the menu is good (positive), and “better menu section”, where the respondent means that the menu needs to be improved (negative). In this way, by having a context linked to different entities like food, drinks, ambiance, etc., the tool can attach a positive and negative sentiment to any given text in a survey response more accurately.
North of Brooklyn: Review Sentiment Analysis in Action
One of my favorite places to grab a pizza and an indy beer is a small local place in my old neighborhood called North of Brooklyn. The pizza is amazing and their tomato and burrata salad is killer. Locals love this place so much that it gets 4.5 stars from 328 reviews on Google My Business. Now if you only paid attention to the star rating you would think that the owners of the place have little to improve and should just keep on doing what they are doing and they’ll be ok. Yet if you dig deeper and read some of the reviews you’ll quickly realize that in fact there is a lot he can do to improve in order to drive up his rating, or more importantly, improve the business.
I pulled a bunch of reviews from Google and after doing a quick analysis I was able to see a real issue coming to the surface regarding an issue that customers were complaining about. It was regarding their core product - pizza, specifically burnt pizza.
The reviews go like this:
I don’t know how you feel like you are going to get clients back with poor quality food like this. Used to be really good pizza but this is the second time we have had a problem post Covid the pizza is burnt, dry and has no flavour at all.
Great pizza but the service is absolutely terrible*. Placed an order for* *delivery* *and it was* *delayed* *by over 40 min.* *Pizza* *arrived completely* *cold**. Very* *disappointing* *and quite frankly* *unacceptable**. Definitely* *not worth* *going back.*
I used to order many times before and their pizzas were really good. Some days ago I ordered and it came overcooked, burnt precisely. Can someone tell the pizzaiolo to set a timer?
Strategically the owners now can move forward with confidence to create a solution to an issue before it brings too much damage to customer loyalty and trust. They will need to investigate whether a poorly trained employee has caused the problem or possibly the pizza oven’s settings have been tampered with. After discovering the source, they can either enhance staff training, review management, or fix or replace their oven.
Applying aspect based sentiment analysis to a broader range of reviews will further help them gather in-depth sentiment about quality, cost, ambiance, customer service, cleanliness, etc. This will help them improve their product and services; for better customer satisfaction resulting in better reviews and thus greater sales.
Explore more with our Google review analyzer.
How Can You Extract Product Review Analysis Insights From YouTube?
To understand product review analysis more thoroughly, let’s take another example. This time we will analyze a product review of the Sony PS5 by the YouTube channel, Unbox Therapy.
Unbox Therapy is a very popular consumer technology channel on YouTube produced by Lewis George Hilsenteger. The channel has over 17 million subscribers and focuses on giving viewers a review of new technology, such as laptops, smart devices, gaming consoles, etc. often before they have hit the market.
In this particular product video, Hilsenteger is comparing two of the top 2020 gaming console releases - Sony’s PS5 and Microsoft’s XBOX Series X.
In order to extract product sentiment analysis insights from this video, we ran it through Repustate IQ, the all-in-one sentiment analysis solution for brand experience. Machine learning tasks such as NLP, semantic clustering, named entity recognition, and deep search algorithms, identified aspects, themes, and adjectives in the video, and calculated sentiment scores based on the degree of intensity of the adjectives used. We got the results insights quickly and accurately, clearly defining the PS5 as superior of the two based on styling and product styling. Let’s see how.
At between the timestamps 5:54 and 6:27, Hilsenteger says:
….that one the thing that gets talked about more frequently actually is the styling which is quite a bit different you have the simplest form possible (XBOX Series X) and you have an incredibly complex form (PS5 here with this tapering and these various shapes and then the lighting that pops up on the inside of the frame over here and the black and white motif i’ll just say i feel like this feels a little bit newer to me i like simple forms i could totally get behind this one to when it comes to styling i don’t think i would make my decision exclusively on that however i think if this thing (XBOX Series X) is sitting in your living room it’s probably going to command a little bit more attention from your pals than this one will if that matters to you.
Here the theme is styling in reference to the two entities PS5 and the XBOX Series X. He says that the PS5’s styling feels “complex” and “newer” and will “command a little bit more attention”. The newness comes from the product’s aspects “tapering”, “shape”, “motif” and “lighting”. On the other hand, the XBOX Series X appears “simple” and logically less new than its competitor.
On the topic of controllers at between 7:07 and 7:14 the Canadian tech influencer says:
….from that standpoint (controller) it was a little bit how can i say a little bit less exciting on the Microsoft (XBOX Series X) side because this thing is so similar to the previous generation controller.
Here we can see that the recurring thematic element is style and product design. The reviewer says that he finds the PS5 “complex” and “newer” with regard to the product’s aspects such as “tapering”, “shape”, “motif” and “lighting”. He also says that it will “command a little bit more attention”. With regard to the XBOX Series X, he says it appears “simple” and thus, by logic, has fewer new stylings than the PS5.
Similarly, with regard to the game controller, Hilsenteger implies that the PS5 has a better design because he finds the XBOX Series X to be “less exciting” and “similar” to its predecessor.
As can be seen from this example, semantic understanding of the language in its native form is a very important feature in a sentiment analysis tool. This is especially true of data from product review videos or even comments on customer forums and websites. Otherwise, you cannot gather accurate insights from informal content. This is because even though much of the content in the reviews can clearly state the positives and negatives of a product, brand, or service, there is a large portion that covers implied opinions. Hence, an intelligent, semantically advanced AI-driven solution for product review analysis becomes imperative to extract high-precision and meaningful insights.
Explore all you can do with YouTube video analysis.
Product sentiment analysis from customer reviews and videos for business and brand insights need not be a complicated process. With the right AI solution that can navigate through numerous languages seamlessly, scale indefinitely, and even allows you to change sentiment rules for your business needs without coding as Repustate IQ does, you can leverage targeted, actionable insights for a more effective growth strategy. Experience Repusatate IQ live for product review analysis.