How Does Customer Feedback Sentiment Analysis Work in Search Marketing?

How Does Customer Feedback Sentiment Analysis Work in Search Marketing?

www.countants.com

Overview •What is Customer Feedback?

•Using Sentiment Analysis To Analyze Customer Feedback •Sentiment Analysis Using AI and ML • Conclusion

What is Customer Feedback?

In the form of customer reviews, customer feedback can play a vital role in search marketing. For example, usergenerated content can be a widely trusted source of authentic content for other users.

Here are some of the ways in which customer feedback works in search marketing: •Builds the SEO value of your business.

•Drives higher business transparency •Differentiates your brand standing.

Builds the SEO value of your business Popular search engines like Google love user-generated content (or UGC) and give higher ranking to websites with plenty of UGCs. Websites with UGCs are rated more authentic and credible by search engines.

Drives higher business transparency •

During online research, customers are constantly looking for proof before interacting with any brand.

•

Customer reviews boost the business transparency that can drive higher online trust in your business.

•

Customer feedback can act as a third-party validation tool that can build user trust in your brand and online promotions.

Differentiates your brand standing •

Effective digital marketing is all building website content that can engage your audience and drive them to a deeper online engagement with your business.

•

Customer reviews or testimonials can differentiate your B2B or B2C business from your competitors and help your brand stand apart from the others.

Using Sentiment Analysis To Analyze Customer Feedback

• In simple terms, sentiment analysis is an algorithm-driven process that can categorize user feedback as positive, negative, or neutral. • Sentiment analysis algorithms have access to a large dictionary of words each of which has either a positive or negative sentiment attached to them.

Based on the included words and the associated sentiment in the user feedback, the sentiment analysis method assigns a sentiment score to them. As a result, positive feedback gets a higher sentiment score while negative feedback gathers a lower score.

Based on the sentiment score, data analysts can analyze customer feedback through any of the following methods:

•Calculating the average sentiment score •Measuring a sentiment histogram •Developing a word cloud

Calculating the average sentiment score The average sentiment score is a good indicator of overall customer feedback. A high average score indicates a positive response meaning that positive sentiments represent a major share in the responses. On the other hand, a low (or negative) score indicates largely negative feedback.

Measuring a sentiment histogram

A sentiment histogram provides a visual representation of how your sentiment scores is distributed across. A histogram shows the point where most of the sentiment scores are clustered.

Developing a word cloud • While a sentiment score can indicate either a positive or negative feedback, a word cloud can help analyze the actual words used to convey user sentiment. • Developing a word cloud can help in the understanding of feedback themes or topics being discussed in the response.

Sentiment Analysis Using AI and ML

Implementing sentiment analysis for better customer service is a great idea, but very challenging in execution. Even with the adoption of natural language processing (or NLP), sentiment analysis tools are unable to detect user comments replete with sarcasm.

For example, consider the following user review: “This is a good-looking shopping bag. I found it so useful that within a month, it was worthy of carrying all my local groceries.”

With the use of words like “good-looking,” “useful,” and “worthy,” it’s likely to be categorized as “positive” feedback. With an overload of such obviously “sarcastic” comments, your sentiment report is bound to be inaccurate.

However, the solution lies in the adoption of technologies like AI and ML that can accurately perform sentiment analysis on a wide range of data sources. Machine learningbased tools can easily extract a range of emotions from user comments and feedback, thus enabling better customer service and improving the business ROI.

Here is a typical process that AI and ML tools use for detecting sarcasm in textbased user comments:

• Importing the dataset of sarcastic comments •

Feed the analytical data into an AIpowered engine

Importing the dataset of sarcastic comments The first step is to import the dataset containing millions of sarcastic comments. With millions of rows, each dataset record typically contains the following attributes: •Label •Comment •Author •Subreddit •Score •Ups & Downs •Date •Created_utc •Parent_comment For sentiment analysis, only the “label” and the “comment” attributes matter. The “label” is marked 0 (for any sarcastic comment) or 1 (for a non-sarcastic comment). The “comment” attribute contains the text of the user’s comment.

Feed the analytical data into an AI-powered engine This can be performed using any of the following techniques: •Using TF/IDF •Using TensorFlow CNN

Using TF/IDF

• Short for Term Frequency/ Inverse Document Frequency, this technique measures the overall number of records in the dataset divided by the number of times a specific term appears in the dataset. Example, N-gram level TF/IDF score that measures the combined total of N terms.

• With this technique, you can split the dataset in the 70:30 ratio with “label” as the targeted column. Additionally, remove all the other dataset columns and just retain the “comment” column in the final dataset.

Using TensorFlow CNN

•Short for Convolutional Neural Networks, the CNN technique is dependent on TensorFlow data models that is based in machine learning technology.

•This ML-powered model is generated using the Topic Modelling technique that identifies a word group (also referred to as a topic) from the collected dataset.

•Using TensorFlow, AI tools can process larger volumes of text and build efficient models with the available data.

 Conclusion It is complex to execute, sentiment analysis in search marketing is the best thing to handle your generated customer feedback data. Thanks to technologies like artificial intelligence and machine learning, accuracy in sentiment analysis is a definite possibility today.