Now, more than ever, businesses need to make sense of online reviews and analyze what customers are trying to tell them. And, they can simply do this by using AI-powered text analytics and sentiment analysis.
Companies need to understand that success lies in the hands of their customers. Understanding how your customer feels about your product or service is imperative to financial survival and prosperity. Here, we’ll understand the process of sentiment analysis on reviews and how it helps businesses improve their products and services.
Think of how often you used TripAdvisor, Booking.com, or Expedia to compare hotel ratings before finalizing your trip. So, before you make a purchase commitment, you must know how others feel about their experiences with the same product and services you are about to purchase.
The unprecedented rise in online reviews, rating websites, and mobile review apps has empowered shoppers to raise their hand in pleasure or displeasure every time they feel compelled. A recent study shows that 94% of consumers claim they read online reviews before making a purchase and 92% purchase decisions are influenced by them.
This approach ensures that you are strategically all ears to your primary stakeholder in your success – Your Customer.
Why Do You Need Product Review Sentiment Analysis?
When brands or businesses have voluminous reviews across multiple sites, extracting and analyzing them for sentiment becomes daunting and time-consuming. Businesses for utter efficiency need to look to AI-powered review sentiment analysis to retrieve insights from reviews quickly and precisely.
How is Review Sentiment Analysis Done?
A reliable sentiment analysis API is used to find insights and relationships within the textual data. The three-step process is simple:
Step 1: Data Gathering
Collect and prepare the data that you want to analyze whether it’s internal (customer feedback) or external data (feedback from review sites). To prepare the data from text analysis, all you need to do is put it into a CSV or XLS document format.
Step 2: Integrate Review Sentiment Analysis API
Next, run your input data through your sentiment analytics API. It’ll quickly return sentiment scores for each relevant review topic, aspect, or entity ranging from -1 for negative emotions, 0 for neutral, and +1 for positive emotions.
Step 3: Sentiment Analysis Dashboard
Once you receive the sentiment scoring, you can easily receive different visualization tools like Tableau, Power BI, Repustate to quickly turn your data into visual reports. These reports are made from charts, graphs and tables to identify trends, patterns and actionable insights in your data.
Business Success and Review Sentiment Analysis – A Win-Win Situation
There’s no doubt that online reviews can make or break a business if they fail to heed the criticism of their own patrons. Consumer reviews are crucial because they embody the voice and opinions of customers.
To be precise, consumer reviews are important because they embody the voice and opinions of customers. Successful businesses see reviews as free consultations by people who are emotionally interested in what they have to offer. Reviews not only influence consumer decisions but also strengthen a company’s credibility and gain customer trust.
Lastly, the Benefits of Sentiment Analysis on Reviews
There are myriad benefits of sentiment analysis on reviews, but here, I’ve pocketed the mains.
- Accurately target operational improvements at pain points
- Identify and extract how your customer feels about your business
- Easy to visualize customer insights for fast analysis
- Classify feelings according to different parts of your business
- Develop baseline sentiment metrics to measure the change in progress
Read the original article at: How Is Sentiment Analysis On Reviews Done
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