Nowadays, customers are more vocal about their ideas and experiences than ever before. Sentiment Analysis can extract emotional insights from social media channels, videos, podcasts, blogs, forums, news articles, call center calls, surveys, or any other data sources.

Sentiment Analysis helps businesses monitor and understand a customer’s brand, product, and service sentiment. It allows you to understand what makes a customer happy or frustrated.

There are many applications for Sentiment Analysis, like improving products and services.

Example: customers purchase your product from a major retailer website and leave a review a week after they started using the product. They will share their experience about the state in which the product arrived, the unpacking, getting the product up and running, their perception of the quality and performance, and much more. As many customers leave reviews, many insights can be obtained on how to improve your products and services.

Other applications are, for example, CX strategy development, social media and brand monitoring, campaign development, competitor intelligence, and identifying market and consumer trends.

Thanks to Artificial Intelligence (AI), Sentiment Analysis can process thousands of reviews automatically and identify the most important topics and their relative sentiment. It puts you in the position to understand what needs attention, set priorities, and act accordingly.

AI driven Sentiment Analysis automates the process of identifying and processing customer feelings, opinions, and meanings expressed in text, audio and video. The ability to automatically process customer sentiment has several important benefits:

Fast & cost efficient:
AI driven sentiment analysis makes it possible to process and derive insights from thousands of posts, reviews and survey responses quickly and inexpensively. Manually reading through a large number of reviews is often just too time consuming and costly.

Always on:
AI driven sentiment analysis makes it possible to monitor events 24/7 and stay in the know about critical issues in real-time. It puts you in the position to act immediately, before situations escalate and get out of hand.

Consistent:
AI driven sentiment analysis applies the same criteria to what is positive, negative, and neutral. Two people, or even the same person but on two different days, might interpret the same piece of text differently. AI has a consistent way of interpreting data that helps to improve accuracy and gain better insights.

Consumer reviews often touch on many aspects of a product or service. Complaints or praise for price, quality, ease of use, or performance can all be mentioned in one comment.

Aspect-Based Sentiment Analysis goes one step further than typical Sentiment Analysis as it first determines which aspects are being mentioned and then defines the sentiment for each one.

Example: “the hotel room was great, but the hotel restaurant terrible.”

Overall, this review would be neutral, but it is clear that there are two very distinct opinions: one about the hotel room (great) and one about the hotel restaurant (terrible).

Aspect-Based Sentiment Analysis is able to identify and classify the topics and then define their relative sentiment.

When compiled and aggregated across a large number of reviews, the strengths and weaknesses of a business’ product or services surface quickly and actionable insights become instantly obvious.

Analyzing product, hotel, or restaurant aspects are a few examples of common applications of aspect level sentiment analysis.

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