Analysing User Sentiment With Machine Learning

Analysing User Sentiment with Machine Learning

The Challenge

An important aspect of social listening is consumer sentiment analysis. In order to develop and offer products, businesses must pay close attention to the voice of the customer (VoC) to uncover customer satisfaction and decipher their preferences. Offering customers what they genuinely want rather than what companies believe they need, is something product managers must consider in order to build a strong product roadmap. Online review sites are one effective way to obtaining customer feedback on products. However, manually analysing this unstructured data can be challenging and time consuming.

Pain Points/The Opportunity

Product teams sometimes get wrapped up in day-to-day duties and neglect listening to what the consumer is saying. They are unable to collect customer experience, satisfaction, or loyalty data, to improve products and services, or lower churn rates. Moreover, some businesses don’t even get product feedback since they don’t have a feedback loop system in place. As a result, they continue to operate in the dark, failing to recognize customer needs. This is where sentiment analysis comes in to the picture with the help of ML models and deep neural networks.

The Goal

What PredictiVu can do:

Create automated customer satisfaction data collection, custom text analysis and opinion mining solutions from scratch using data of various sorts and sizes.We apply our cross-domain expertise to create unique big data solutions that are in line with retailer business requirements and data that captures the characteristics of their customers.

Build consumer sentiment analysis solutions by integrating sophisticated linguistic algorithms and machine learning to help businesses determine whether a piece of feedback is favourable, negative, or neutral. The solution can help businesses save a significant amount of time and money by determining how the target population feels about a specific product, trend, issue, or brand.

The Results

Using custom sentiment analysis solutions to track how a change in product or service affects customers.

Applying sentiment analysis to incoming reviews to get quick customer data insights and turn them into useful visualisations.

Detecting mood changes and emotional triggers. Reducing customer churn by identifying frustrated customers as quickly as possible.