CustoAI

Sentiment Analysis on your bussiness

In a world of ever-growing customer data, businesses are required to have a clear line of sight into what their customers think about the business, its products, people and how it treats them. Insight into these critical areas for a business will aid in the development of a robust customer experience strategy and in turn drive loyalty and recommendations to others by their customers. It is key for business to access and mine their customer data to drive a modern customer experience. Our experts use new artificial intelligence methods to better understand your customer and your brand, in fact we are looking to know what customers think about products, services and interactions with a business. This is commonly known as Voice of the Customer (VOC) data and it is key to unlocking customer sentiment. However, we investigate the potential business insights using various Sentiment Analysis approaches; determine a sentiment score to a piece of verbatim feedback and then categories it as positive, negative, or neutral, as showed in figure 1.

How do you know about Sentiment analysis?

Sentiment analysis is an AI method for identifying the ways in which sentiment is expressed in texts (for instance, a reviewer’s feelings, test opinions) and whether such expressions include positive or negative opinions on a certain product, service, or a company. However, It has been confirmed that Sentiment Analysis can identify the feelings and therefore the opinions of product customers in order to understand how these feelings and opinions affect the customers’ decision making. For example, you may define an average emotional tone of a group of reviews to know what percentage of customers liked your new clothing collection. If you need to know what visitors like or dislike about a specific garment and why, or whether they compare it with similar items by other brands, you’ll need to analyze each review sentence with a focus on specific aspects and use or specific keywords.

How Does Sentiment Analysis work and help?

Basically, we consider a systematic process of understanding customer sentiments for capturing feelings, emotions, and opinions. However, our expert discovers and extracts the key information from your textual data related to your business and customers in5-steps as follows:

Step 1: Collect Your Data

The first step in any Sentimental analysis is collecting the data from your audience. This almost always from user-generated content on social media networks, blogs, and forums. When you grab content from those places online the data is complex as there are different ways to express feelings: emojis, slang, languages, contexts, etc. As a result, you’ll probably need to spend a good chunk of time cleaning and organizing the collected data by eliminating irrelevant content from the mix. Subjective expressions including beliefs, opinions, and views are retained, and objective information including facts are discarded.

Step 2: Data Organization and Assignment

After data is collected with your application, it is time to organize and assign your content data points! Sentiment assignments are made and based on approaches including: • Machine learning: Predict the polarity of sentiments based on trained and test datasets • Lexicon learning: Uses predefined lists of words that are already associated with specific sentiments • Hybrid learning: Uses a combination of both machine learning and lexicon approaches to improve sentiment classification performance By using any of those learnings, data points are typically organized by “feelings” or by the type of content it is like: • Product reviews • Sentences and Comments • Opinion Discussions • Etc…

Step 3: Report and Data Visualization

After the information is sorted, organized and analysed, the data will be displayed visually through a report.