Author: Philip Godfrey
Oracle Analytics Cloud is a powerful
tool that enables organizations to make data-driven decisions by providing
advanced analytics capabilities.
Previous blogs in this series include
getting started with Oracle Analytics Cloud, loading data, and creating data
flows, and our focus this week is on Analyze Sentiment.
In our previous blog post, we created
a data flow, and added in Analyze Sentiment step, but I wanted to explain what
this is, how it works, and what it can add to your data.
What is Sentiment
Analysis?
Sentiment
Analysis is a subfield of Natural Language
Processing (NLP) and Machine Learning that involves analyzing text data to
determine the emotional tone or attitude conveyed by the writer or speaker.
The goal of sentiment analysis is to
automatically classify text as expressing a positive, negative, or neutral
sentiment.
If we consider a
review from a restaurant, the review might say something like:
"The food was amazing, but the service was terrible! I would
absolutely visit again”.
Sentiment Analysis in this case will help
us figure out that the reviewer is overall happy with the food, and would
return, but unhappy with the service.
It becomes complex because human
language is complex! If one word was changed in this review, it becomes
completely different.
"The food was amazing, but the service was terrible! I would
absolutely visit again, not!”.
At this point, we can see the reviewer is happy with the food, but unhappy with both the service, and would not visit again, although this could be difficult to pick up as it’s a sarcastic comment, and humans often express themselves in ways that are open to interpretation.
Analyze Sentiment
Going back to our example in the previous
blog, once the Analyze Sentiment step has been added into the
Data Flow, we need to indicate the column we want to understand the sentiment
of through parameter selection.
In the Disney dataset, this will be Review_Text.
As the screenshot above suggests, this
step will create a column, named emotion, and will append this to our
Disney Review Dataset.
As we can see here, each review has been reviewed by Oracle Analytics Cloud, and provided an emotion:
- Positive
- Negative
- Neutral
Each review will now be classed as
either Positive, Negative, or Neutral based on the language content, semantic
constructs, and context.
What are the
benefits of understanding sentiment?
Understanding
Sentiment Analysis has numerous benefits across various industries and
applications.
Here are some of
the most significant advantages:
·
Improved Customer Experience:
analyzing customer feedback allows businesses to identify areas for
improvement, resolve issues promptly, and enhance customer satisfaction.
·
Informed Decision-Making:
Sentiment Analysis helps organizations make data-driven decisions by providing
insights into customer opinions, preferences, and emotions.
·
Market Research:
inform market research, helping businesses understand consumer opinions and
sentiment about products, services, or competitors.
· Brand Reputation Management: By monitoring online conversations, businesses can identify and address negative sentiment quickly, protecting their brand reputation and building trust.
Look out
for the next blog in the series which showcases the newly created dataset,
which incorporates our Disney Review Data with Analyze Sentiment and we take
this into OAC to explore using Augmented Analytics through Explain functionality.
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