I’m going to talk about Sentiment Analysis in OAC. Sentiment analysis in OAC is an automated process of determining whether a text expresses a positive, negative, or neutral opinion about a product. Developer don’t have to spend endless hours on doing this analysis. Sentiment analysis helps companies to monitor their brand reputation on high level in social media, gain insights from customer feedback, and much more.
In this process, sentiment is extracted from the entire review, and a whole opinion is classified based on the overall sentiment of the opinion holder. The goal is to classify a review as positive, negative, or neutral.
In this post, we’ll go into more detail about How do we create sentiment analysis in OAC? and how it can help your business?
Here are the steps to do Sentiment Analysis in OAC (Oracle Analytics Cloud).
Step#1: Get feed from Kaggle
· Login into Kaggle link with your credentials https://www.kaggle.com/.
· Search with a key words Twitter US Airline Sentiment Reviews /Covid19/ Product Reviews/ Amazon reviews etc.
· Download the file and view the data and comments.
Step#2: Sentiment Analysis using OAC
As part of OAC, DVCS has inbuilt capabilities to perform sentiment Analysis on textual data. To invoke sentimental functionality, add the Twitter_US_Airline_Sentiment data set and create a data flow using the data set. In Oracle DV, sentiment analysis is implemented using Python. To invoke it add Analyze Sentiment node to the dataflow.
· Click on create Data set and select the Twitter_US_Airline_Sentiment csv file and click on add
· Click on create data flow
· Select the data set which you added (Twitter_US_Airline_Sentiment excel) and click on Add
· For e.g. I took the Twitter_US_Airline_Sentiment feed of product XXXXX and performed sentimental analysis on column “Review”
- Click on + sign and select “Analyze Sentiment”.
- In the Analyze Sentiment pane, Output section, give a column name to capture the emotion value. Rename the default column ‘emotion’ as needed.
- In the Parameters section, choose the column with text content to analyze
- Click on select column and add the column from available data for e.g. Review (column name)
- After adding the analyze sentiment column we can see column with name as Sentiment and it indicates the “Positive”,” Negative” or “Neutral”
- To add the column, click on Add step (+) and select the Add Columns
- Rename the column as required
- Give the Expression and click on Validate and select apply
- Now we can see the Count column added to the available data, click on save.
• Go to Home page and click on create and select the Project as shown
• Select the Dataflow which you created above and click on Add to project
- Select the attribute emotion and select the count measure and click on pick visualization (or you can drag drop the columns on right side pane accordingly)
- Below is the line chart which is showing the positive, negative or neutral Reviews as per the data available in the data set.
