Customers may forget what you said but they’ll never forget how you made them feel.

– inspired by Maya Angelou

In a competitive market wherein corporations compete for clients; patron satisfaction is seen as a key differentiator. Studies say that a ‘totally satisfied customer’ generates 2.6 times more revenue than a ‘somewhat satisfied customer’ and 14 times more revenue than a ‘somewhat dissatisfied customer’. Sentiment analysis in business, also known as opinion mining is a process of identifying and cataloging a piece of text according to the tone conveyed by it.

Relation between Customer Sentiments and Business Success

Customer satisfaction is the most important indicator of how likely a customer will make a purchase in the future. Businesses who succeed in those reduce-throat environments are the ones that make customer satisfaction a key element of their business strategy. Sentiment analysis in business can prove to be a major breakthrough for the complete brand revitalization.

Finding Customers Sentiments about a Product

Most social sites offer popular target market metrics, however, achievement is extra dependent on actionable statistics which might be found out through target audience participation. The reviews sources are mainly sites, example-Twitter, Facebook etc.

Business targeted Sentiment analysis

It is vital to comprehend tremendous, poor, and even impartial opinion traits. The secret to running a successful commercial with the sentiments data is the ability to exploit the unstructured statistics for actionable insights. The experts agree. According to Bruce Temkin, a customer experience visionary, “Emotional” is one of three key experience components. Forrester places emotional engagement at the top of its Customer Experience Pyramid.

Challenges of sentiments analysis

The challenge is to accurately measure sentiment and transform findings into actionable customer-experience strategy. Sentiment analysis via ‘text-analysis’ has been part of market-leading solutions for several years now.

Research work on Sentiment analysis

Sentiment Analysis is an ongoing field of research. The research focuses on the computational treatment of opinions, sentiments, and subjectivity of text. Many researchers are targeting to propose a high accurate classification algorithm to extract the emotions of the texts. Research says that classifying text at the document level or at the sentence level does not provide the necessary detail needed opinions on all aspects of the entity which is needed in many applications.

How the lexicon of a language and cultural barrier creates complexity?

Technical methods most usually begin with a lexicon that assigns phrases — “good,” “fast,” “expensive,” “hot” — to positive and negative categories. The problem arises with the complications of word use.  That coffee is hot is (typically) a good thing, while a hot room is not, and the “hot” in “hot chocolate” is merely descriptive.

Add in words such as “not” that reverse meaning, modifiers such as “very,” and a world of idiom, metaphor, abbreviations, and emoticons — plus language and cultural complications — and you face a complex analytical challenge.

Involvement of Machine Learning in complex lexicon in a language

Machine learning has a solution to address this. To address these details focus is required in aspect-level. Aspect-level aims to classify the sentiment with respect to the specific aspects of entities. The datasets used in Sentiment Analysis are an important issue in this field. The main sources of data are from the product reviews. These reviews are important to the business holders as they can take business decisions according to the analysis results of users’ opinions about their products.

Where Zensar Stands

With our Machine learning capability at Zensar, we have helped our customers build a sentiment analysis system which helped to improve their business. To know more, please write to us at

In the next blog, we will look into how the implementation of Sentiment analysis in a real-world data is done & different technologies used.

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Posted by Bidisha Mukherjee

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