Sentiment Analysis

Sentiment Analysis involves assessing digital text to discern the emotional sentiment, categorized as positive, negative, or neutral. Enterprises handle extensive textual data from various sources like emails, social media comments, and customer support chats. A sentiment analysis tool efficiently processes this data, extracting user attitudes.

How does Sentiment Analysis contribute to your objectives?

The analysis and planning provided by the Discovery and Assessment Service lay the groundwork for the adoption or growth of the enterprise cloud. Customers who intend to incorporate public or hybrid cloud solutions into their computer infrastructure must first use this service.

Reliable and neutral findings in customer sentiment analysis with the aid of confidence scores.

Companies can present enhanced solutions to users by utilizing authentic customer feedback.

Spot and address negative sentiments upon detecting specific keywords.

Efficiently handle substantial unstructured data volumes and scale processes economically.

Sentiment Analysis Use Cases

Customer Feedback Analytics:

  • Understand sentiment and improve the customer experience.
  • Explore how customers perceive your brand, extract sentiments about specific areas of interest, and identify your customers’ top frustrations so you can address them.

Brand Monitoring:

  • Monitor brand perception in real time.
  • Detect positive or negative sentiment around your brand and take proactive measures.

Market Research:

  • Analyze customer opinions about products, services, or industry trends.
  • Gain insights into market sentiment and adapt strategies accordingly.

Employee Feedback Analysis:

  • Process employee survey responses.
  • Understand employee sentiments, identify pain points, and enhance workplace conditions.

Customer Reviews and Emails Analysis:

  • Extract insights from raw survey responses, reviews, and customer emails.
  • Identify patterns, sentiments, and areas for improvement.

Product Surveys:

  • Analyze sentiment from product feedback forms.
  • Refine existing products and develop new ones based on customer sentiment.


OCI provides sentiment analysis at different levels: document, sentence, and aspect. Document-level sentiment analysis provides an overall sentiment score for each document or record in your dataset.

Sentiment analysis, though highly sophisticated, may not achieve complete accuracy, potentially resulting in partial matches.

OCI Sentiment Analysis supports two types of analysis: sentence-level and aspect-level.

At present, OCI Sentiment Analysis exclusively supports English and Spanish language.

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