Customer feedback sentiment analysis uses algorithms to categorize feedback as positive, negative, or neutral based on included words. This information can be analyzed using average sentiment scores, histograms, or word clouds. While challenging, adopting AI and machine learning can help sentiment analysis tools better detect sarcasm and extract a range of emotions from comments. Techniques like TF/IDF and TensorFlow CNN can help feed analytical data to AI engines for more accurate sentiment analysis.

1. How Does Customer
Feedback Sentiment
Analysis Work in
Search Marketing?
www.countants.com

2. Overview
• What is Customer Feedback?
• Using Sentiment Analysis To Analyze
Customer Feedback
• Sentiment Analysis Using AI and ML
• Conclusion

3. What is Customer Feedback?

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

5. 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.

6. 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.

7. 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.

8. 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.

9. Using Sentiment
Analysis To Analyze
Customer Feedback

10. • 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.

11. 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.

12. 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

13. 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.

14. 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.

15. 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.

16. Sentiment
Analysis Using
AI and ML

17. 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.

18. 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.

19. 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 learning-
based tools can easily extract a range
of emotions from user comments
and feedback, thus enabling better
customer service and improving the
business ROI.

20. Here is a typical process that
AI and ML tools use for
detecting sarcasm in text-
based user comments:
• Importing the dataset of sarcastic
comments
• Feed the analytical data into an AI-
powered engine

21. 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.

22. 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

23. 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.

24. 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.

25. 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.