Improved data review analysis using NLP 

NLP is the latest technology that is essentially used to translate human sentiments or their natural language. To improve business performance and cater to your target customers in a better way, it is crucial to analyze reviews and ratings of your products or services. In fact, companies are using various platforms to showcase their company profile and client reviews, to attract more business projects, and also employ talented professionals.

Natural Language Processing or NLP is used to gauge the predominant review text sentiment and use them in a more structured way. The unstructured data at various portals like customer emails, product reviews, support tickets, and social media, can be used to gain a better insight into customer sentiment and contribute to business profit. Organizations are eager to identify and extract specific useful context, which is otherwise hidden in the unstructured and vast textual swathes of data.

Mapping of Review Text Sentiments

Google’s TensorFlow is a widely used NLP framework that simplifies the process of acquiring useful data, training models, predicting accurate outputs, and delivering refined future results. Google Translate is the most widely used application of NLP in TensorFlow. AWS provides Amazon Comprehend, which is an NLP service used to find insights and relationships in a text. 

Let us take an example of how a review text sentiment is mapped to the given points, using NLP.

Step 1: Find the data

Collect hundreds of reviews and their scores or ratings from different customers, on the products or services, using a simple GET request. Transform the review JSON data into a simple CSV file, and upload it.

Step 2: Setup 

Specify the name and type of analysis using tools like AWS Comprehend or any other NLP framework. For example, we can choose Sentiment, Entities, phrases, or Language type analysis, for the review experiment.

Step 3: Input/ Output Location

Define the Input Source and Output location, to fetch the data and location where output is to be stored, respectively. 

Step 4: Results Analysis

The process takes a short duration and the results are stored at the assigned location, with a predominant sentiment mentioned with each review. We can map these sentiments to, previously established ones, like positive, negative, or neutral sentiments.

The higher star ratings receive “positive” sentiments, whereas the lower star ratings receive “negative” sentiment, considering the text reviews.

Applications where NLP can be used for data review analysis

With industries moving to big data and using AI for improving their business performance, they use NLP for extracting the customer sentiments from the reviews and ratings shared by them. People have gone online for all their research related to products, services, apps, companies, etc. Positive sentiment helps them trust the product whereas negative sentiments can be used for business improvement. The use of NLP is increasing drastically to provide a quick, accurate, and smarter review process for improved business performance. 

Let’s have a look at some of them.

1.Sentiment Analysis 

NLP plays a great role to comprehend and analyze customer responses on the web and social media. It is helpful in analyzing the emotions and attitudes of the customers, who are commenting and sharing their opinions. NLP, along with statistical values assigned to text, like positive, negative, or neutral, can be used to identify the moods like happy, sad, annoyed, etc. Discovering the opinions, feelings, and emotions about specific service attributes to the business growth, build stronger brand perception, enhance the product, and provide a better customer experience.

2. SRS Documentation

The SRS or Software Requirement Specification Document is the first phase of the SDLC and elicits the user requirements, along with the functional, non-functional requirements and the technical aspects of the project. This is a critical document as it is shared with the clients to develop the right software according to the user’s requirements, the objects, methods, and attributes. At times, incompleteness, ambiguity, inconsistency, and redundancy in the SRS, can deteriorate the quality of SRS which can create ambiguity between the developer and the user, and hence this document needs to be very specific. Researchers suggest the use of NLP to simplify this process and develop a convenient methodology for programming systems to find out the names and details of classes directly from the SRS. A vast number of organizations are using them to develop better products and provide customer satisfaction.

3. Employee Review Data

Online employee reviews have great potential and sites like Glassdoor have transformed the way employees identify prospective employers. Around 70% of job seekers refer to Online reviews to find suitable jobs and organizations. Also, employers use online reviews for internal improvement, better management, and to create an improved work culture. There are broad categories that the employees have to rate across, like Senior management, work/life balance, culture and values, compensation and benefits, and career opportunities. Numerical ratings do not cater to the essence of these crucial parameters, and hence NLP plays a critical role in the analysis of reviews.  

So, if you want to improve your business further and analyze the customer reviews in a more structured and useful manner, NLP provides the most accurate, smart, and advanced way for review analysis and relation extraction, using text mining or opinion mining. Sentiment Analysis for hundreds of reviews and for a large pool of descriptors is done effectively to get a quick review of your products or services.