Identifying Consumer Intent: Sentiment Analysis and NLP in Social Media

by September 6, 2020 0 comments

Sentiment Analytics

Interpretating emotions within text data allows businesses to identify customer sentiment toward brands and services.

Social Media has let customers to communicate with their favourite brands and express their thoughts more openly than ever before. It is estimated that 80% of the world’s data is unstructured, or unorganized. Huge volumes of data through emails, support tickets, chats, social media conversations are created every day which forms the supporting pillars of sentiment analysis.

Being said, sentiment analysis classifiers may not be as accurate as other types of classifiers. But is it worth the effort?  Sentiment analysis finds its presence into social media monitoring, brand monitoring, customer service and market research giving marketers an opportunity to get their insights right about 70-80% of the times.

Brands of all shapes and sizes look forward to have meaningful interactions with customers, leads, and even competition on social networks like Facebook, Twitter, and Instagram. Marketing departments measure social media chatter as brand awareness. However, to get into the skin of the quality of conversation that’s happening around a brand, marketers need to analyse Twitter tweets, Facebook posts and monitor the social media presence of their brand.


Understanding Customer Intent

Not only do brands collate a wealth of information available on social media, but they are on a look out of a customer’s digital footprint. Instead of focusing on specific social media platforms such as Facebook and Twitter, they also segregate the news, blogs, and forums looking at not just the volume of mentions, but also the quality of those mentions to analyse the audience sentiments.

This lets marketers to automatically categorize the urgency of their online brand mentions and alert designated team members for responses. Sentiment analysis, a term that integrates natural language processing (NLP) and machine learning techniques offers a sneak peek to competitor analysis letting marketers research their competition and understand how their reputation evolves over time. Besides helping them to identify potential PR crises which issues need to be prioritized and put out immediately and what mentions can wait.

Sentiment Analytics can be an essential part of an enterprise’s market research and customer service approach portfolio. This potent tool enables marketers and data analysts to distinguish user sentiments in a far more granular way. Looking forward, automated insights from sentiment analysis would empower data backed decisions rather than plain intuitions -that isn’t always right.

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