Deep Learning-based Sentiment Analysis: Establishing Customer Dimension as the Lifeblood of Business Management

Abstract

‘First mover advantage does not go to the first company that launches, it goes to the first company that scales’ (Reid Hoffman, co-founder of LinkedIn, ScaleIt, 2016). M. Andreesen defines scale-up as a company that has identified its product/market fit and has reached notable proofs of market traction (e.g., profitability, revenues, active users, registered users, market demand). It has been previously established that five interdependent core dimensions can be used to evaluate scale-up: customer, product, team, business model and financials. Most of the business failures result from falling apart of one or more of these dimensions with others.

We in our article discuss the ‘customer dimension’ as the essence of scaling up. For running a profitable business, it is crucial to evaluate customers’ reviews, like their perception and expectation from the product and services in terms of service quality, deliverables, staff and management practices and pricing. This will increase customer satisfaction which will further boost the demand and popularity of the brand and business. The customers today are very vocal in sharing their experiences through social media blogs, channels, review sites and so on. These experiences need to be decrypted by the management to understand the customer’s point of view, their apprehensions and expectations.

Sentiment analysis is one of the best ways to tap customer feedback. The usual way of analysis involves the bag of words model using ngrams. A more refined version is an ontology-based analysis. Research has also been done using the radial basis function kernel for support vector machines (SVMs) (for recognizing polarity of sentiments). Naïve Bayes algorithms have also been used in some sentiment studies along with linear kernel SVM classifiers. But we realized during the literature review that it is not enough to just classify the text into positive and negative or a few more categories. It is even more important to know which topics are being discussed by the customers, their intention behind the message and further the need to classify them as complaints, suggestions, appreciation or query and so on. Thus, we in our article discuss the solution to all these types of analyses using ‘deep sentiment analysis’. We discuss the case of a few current social topics and introduce ‘recurrent neural networks’ (RNNs) and ‘convolutional neural networks’ (CNNs) for sentiment and intent analysis.

These advanced analytical techniques will help in the ‘pivoting’ of businesses by taking desired actions such as improving product quality, improving quality of services and increasing quality checks in areas with maximum negative reviews. This eventually will help start-up ventures in improving customer experience and gradually help in the scaling of ventures.