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Analyzing The Sentiments Expressed In Reviews

Analyzing The Sentiments Expressed In Reviews

Brands and businesses are increasingly leveraging the concept of generating reviews and ratings. Digital capabilities have made it significantly easier for brands to build engagement with consumers and a key component of this is Reviews. As brands and local businesses expand and transform their strategies in the modern era of digital technologies, understanding consumers through the Reviews capability is gaining significant traction. On avenues such as formal digital business profiles, reviews go a long way in delivering qualitative and quantitative impact. One of the granular ways through which this impact can be measured is known as Sentiment Analysis

What Sentiment Analysis Is All About

In simple terms, customer sentiment is all about how they are feeling. Are they feeling happy or are they feeling disappointed about the purchase and the experience that a brand or business offered? Whether it is a positive or a negative feeling, to what extent are they feeling it? Taking a close look at such emotions expressed in the text of the Reviews, interpreting, and categorizing them our better outcomes, is what sentiment analysis entails.

This type of analysis enables brands to understand what customers are saying about them on various online review platforms and posts. Beyond providing only quantitative data, sentiment analysis also offers qualitative insights that help marketers understand the reasons behind customer opinions.

For instance, if a furniture business has an average rating of three stars, sentiment analysis can uncover positive emotions around the quality of the furniture and negative or neutral emotions about the quality of customer service. However, to be truly effective, sentiment analysis must accurately comprehend the context and tone of a consumer’s language. And given the extent to which technology contributes to most activities today, marketers can use capabilities such AI and ML to study patterns of generated reviews,understand nuances such as jargon, slang, and sarcasm in the customer’s language. 

Review sentiment analysis done using advanced technological capabilities is an efficient way for brands and businesses to retrieve key insights from thousands of reviews across digital business profiles and various websites. Rather than manually examining every review for gathering sentiments, businesses can rely on AI-powered algorithms to accurately and quickly classify reviews based on sentiment. This saves time, improves efficiency, and eliminates the formidable task of manually retrieving and analyzing reviews.

How Sentiments Are Analyzed

As user-generated content gains more trust, brands require an effective and systematic way to differentiate positive, neutral, and negative reviews. However, this is a challenging task as comments, reviews, and other digital medium data are typically lengthy, unstructured text pieces that are difficult to analyze. With data becoming increasingly crucial to businesses, brands must keep track of this information and extract quantifiable and actionable insights.

At the most fundamental level, customer sentiment analysis involves breaking down a review into individual words, assigning scores to relevant words to indicate their positivity, neutrality, or negativity, and aggregating these scores to obtain an average sentiment score for the review. By using technological features such as Machine Learning, large volumes of reviews can be rapidly evaluated and scored, allowing businesses to quantify and analyze review quantitative information that was previously considered unquantifiable. Consequently, it becomes possible to visualize and gain insights into business performance, in several areas such as the fluctuation of sentiment scores from week to week.

With customized dashboards and reporting capabilities that are available to brands, the task of classifying, analyzing, and understanding data has become simpler. Masses of Reviews data, when presented on such advanced dashboards, can refine unstructured and effectively offer accurate actionable insights, helping brands and businesses reach action-oriented outcomes. Particularly for multi-location businesses, analyzing sentiments gathered through lacs of Reviews at multiple levels, finds more seamlessness and direction when performed with technology-powered strategic and logical approaches.

The Difference Sentiment Analysis Can Make

By staying on top of customer sentiments, brands and businesses can not only deliver delightful customer experiences, but can also unlock lucrative business opportunities. Whether positive or negative, efficiently studying and managing reviews and sentiments allow brands to always stay ahead. Consumers today have multiple options today. If brands do not pay attention to their sentiments and take appropriate action to improve their journeys, in no time they can lose their consumer base to competition. Assessing sentiments through reviews can help brands quickly identify problem areas, improve processes and operations, and create the right parameters to strengthen consumer associations for measuring progress. Holistically, by grasping the consumer’s pulse through detailed analysis of sentiments derived from large chunks of reviews brands can transform their businesses with tangible positive outcomes.

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