Sentiment Analysis: Customer feedback the life air of business

A satisfied customer is the life air of all successful businesses. Every business has one common goal in mind: how to continuously improve customer satisfaction, retain existing customers, and attract more customers. Therefore, customer feedback plays a pivotal role in helping a company understand its customer sentiment about a product and services. With the help of Natural Language Processing (NLP), a company can quickly perform sentiment analysis to gain insightful information about their customer behaviors, patterns and make recommendations.

 Sentiment Analysis?

Sentiment Analysis is an extensive and influential topic in natural language processing (NLP) and Machine Learning(ML). Sentiment Analysis is the process of examining a piece of text for opinion and feeling. It provides valuable insight to businesses on how people feel about a brand, particular topic, advertisement and helps them stay ahead of the competition.

 Customer Sentiment Analysis?

Customer sentiment analysis detects emotion during customer interactions with brands, services, or products. Many companies use Natural Language Processing(NLP) and Machine Learning(ML) to perform sentiment analysis on customer feedback which helps them to efficiently analyze the sentiment of customer feedback.

 The method used by companies to perform Sentiment Analysis on customer feedback

 With the power of Natural Language Processing(NLP) and Machine Learning(ML) algorithms, an organization can now automatically detect the sentiment of customer conversation.

 Over the years, many different algorithms have been developed to facilitate building models to analyze sentiment based on the quantity of data required for the analysis and accuracy of the model. Let's explore a few of them below:

 Rule-based approach/Manual approach

 A rule-based system a manual approach involves rules designed by humans to determine polarity, subjectivity, opinion of acceptance or rejection.

 Example of how rule-based systems operate:

  • Describe two lists of negative polarized words: rude, worst, bad, ugly and positive words like great, happy, handsome, excellent).
  • Counts the number of positive and negative words that show up in the corpus
  • After analysis, if the count of negative feedback is higher than the positive, then the result is negative sentiment. On the other hand, if the count of polarity is equal, then the result is neutral.

 However, the work is manual and does not follow any pattern to arrange or combine the words for a sequence. Therefore, it cannot handle large amounts of data as you add new rules to support vast vocabulary. As a result, the system will become very complex. And possibly losing past results. Therefore, the method is not compatible with large or complex data.

 Automatic System

 An automatic system is the opposite of a rule-based system. Instead, it relies on machine learning, where a classifier is provided with a piece of text and returns an outcome as either positive, negative, or neutral.

 Implementation of machine learning classifier and how it works

 The model train to learn from input test data, and during the training and prediction process, the feature extractor transforms unseen text inputs into vectors and then feeds it to the model to predict negative, positive, and neural tags. Some of the classification algorithms used for such models are Support Vector Machine, Logistic Regression, Naive Bayes, etc.

  Hybrid System

 It is the combination of the Manual system and the Automatic system. It acts as the most efficient and accurate system because it balances automation and manual processes.Finally, the ultimate purpose of customer feedback is to enable an organization to understand its customer behavior towards a particular brand, product, services, and advertisement. Therefore, the organization can now quickly analyze insightful information using Natural Langauge Processing and Machine Learning to help them improve customer satisfaction, products, and services and make recommendations based on customers' likes.

How can Indika help?

Indika is a global data service company specializing in labeling all types of data across all industries. We have been working with some of the most advanced AI companies worldwide to provide data annotation/labeling services for all kinds of structured and unstructured data (such as text, images, videos, speech, music, etc.) In addition, we can assist in further improving the user experience with better search results and NL-based AI Models. 

 

 

 


 


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