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Advanced Text Sentiment Analysis: Scoring Emotion
by Cypress Vendor
1
Prompt
<100
Summary
Summary
This system analyzes the sentiment in text, providing an emotional score from -10 (extremely negative) to 10 (extremely positive). It enables detailed sentiment tracking across varying types of text,
Overview
Overview
The system utilizes natural language processing technologies to parse and score sentiment expressed in text. The emotion score designates the sentiment from extremely negative to extremely positive. This powerful tool can process any form of written communication such as reviews, social media posts, or comments, providing substantial insight for businesses regarding their audience's emotions and experiences.
System Prompt
System Prompt
As a specialist in textual sentiment analysis, your task is to examine the sentiment in various forms of written communication. Determine an emotional score from -10 (extremely negative) to 10 (extremely positive), considering the context and nuances of the language. Provide reasons behind your scoring.
User Prompt
User Prompt
nalyze the sentiment of the following customer review about a product: "The product is fantastic, but the shipping process is a nightmare! I had to wait for over a week. ⏰😓 #frustratedconsumer"
Sample Response
Sample Response
Sentiment Score: -2
Analysis: The review presents mixed sentiment towards the experience, with positive remarks regarding the product and negative feedback about the shipping process. Key factors include:
Praise: The use of "fantastic" denotes positivity towards the product, suggesting a high-quality experience. Criticism: The reviewer mentions a "nightmare" shipping process and extensive waiting time, implicating negative sentiment. Impatient emoji and clock: The use of the clock emoji (⏰) and worried face emoji (😓) imply frustration over the long wait. Negative hashtag: #frustratedconsumer indicates dissatisfaction. Overall, the mildly negative score of -2 represents a mixed experience, implying improvements in the shipping process could enhance customer satisfaction.
Features
Features
- Variable text analysis: Capable of assessing sentiment from a variety of textual sources.
- Contextual understanding: Takes note of overall context for accurate sentiment scoring.
- Emoji and hashtag interpretation: Considers sentiment indicated by emojis and hashtags.
- Explanation generation: Provides reasoning for each sentiment score.
- Trend analysis: Can track changes in sentiment scores over time for continuous feedback monitoring.
- Sentiment scoring: Provides a detailed sentiment score from -10 (extremely negative) to 10 (extremely positive).
Technical
Updated
September 27, 2024Works with
GenAI Activities
Model Recommended
Claude 3.5 Sonnet
Support
Link: https://docs.uipath.com/7/11
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