Ability to detect and classify bad sentiment communication from partner

[bstiawan]

problem: we should deal with partner differently depending on their current sentiment. In order to help the prioritization process for the engagement

user: partnership team especially community manager

solution:

• we should implement sentiment analysis on the echo summarization

[Vidiskiu]

Overall Point: 5

Functional Complexity: 0.9

Sentiment analysis requires understanding of the context and user interactions, with intermediate functionalities linking to partner communication.

Technical Complexity: 1.2

Sentiment analysis is a technical task involving natural language processing which is intricate and may require the use of AI services or libraries.

UI/UX Complexity: 0.5

The UI/UX complexity is moderate as it involves presenting the analysis results, but is not the main focus of the issue.

Data Manipulation: 0.6

Manipulating textual data for analysis is expected and while important, it's not extensively complex.

Testing: 0.3

Testing sentiment analysis models requires careful evaluation but can be handled with well-defined test cases and data sets.

Dependencies: 0.4

Will likely rely on NLP libraries or external APIs, which introduces some dependency, but it should be a manageable integration.

Risk and Uncertainty: 0.3

The primary risk lies in the accuracy of the sentiment classification, which can be somewhat controlled with a robust solution and training.

User Impact: 0.8

Having an effective sentiment analysis tool is highly beneficial for the partnership team's responsiveness and strategy, making it significant for user impact.