18120721. DETERMINING INPUT SENTIMENT BY AUTOMATICALLY PROCESSING TEXT DATA AND NON-TEXT DATA USING ARTIFICIAL INTELLIGENCE TECHNIQUES simplified abstract (Dell Products L.P.)
Contents
- 1 DETERMINING INPUT SENTIMENT BY AUTOMATICALLY PROCESSING TEXT DATA AND NON-TEXT DATA USING ARTIFICIAL INTELLIGENCE TECHNIQUES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DETERMINING INPUT SENTIMENT BY AUTOMATICALLY PROCESSING TEXT DATA AND NON-TEXT DATA USING ARTIFICIAL INTELLIGENCE TECHNIQUES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Sentiment Analysis
- 1.11 Original Abstract Submitted
DETERMINING INPUT SENTIMENT BY AUTOMATICALLY PROCESSING TEXT DATA AND NON-TEXT DATA USING ARTIFICIAL INTELLIGENCE TECHNIQUES
Organization Name
Inventor(s)
Parminder Singh Sethi of Punjab (IN)
Praveen Kumar of Uttar Pradesh (IN)
DETERMINING INPUT SENTIMENT BY AUTOMATICALLY PROCESSING TEXT DATA AND NON-TEXT DATA USING ARTIFICIAL INTELLIGENCE TECHNIQUES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18120721 titled 'DETERMINING INPUT SENTIMENT BY AUTOMATICALLY PROCESSING TEXT DATA AND NON-TEXT DATA USING ARTIFICIAL INTELLIGENCE TECHNIQUES
Simplified Explanation
This patent application describes methods, apparatus, and storage media for determining input sentiment by processing text and non-text data using artificial intelligence techniques.
- Dividing user input data into text and non-text data.
- Converting non-text data into text data.
- Classifying text and non-text data based on sentiment-related categories using artificial intelligence techniques.
- Performing automated actions based on the classification of the data.
Key Features and Innovation
- Utilizes artificial intelligence techniques to analyze both text and non-text data for sentiment.
- Converts non-text data into text data for sentiment analysis.
- Automates actions based on the sentiment classification of the data.
Potential Applications
This technology can be applied in various fields such as customer feedback analysis, social media sentiment analysis, and market research.
Problems Solved
This technology addresses the need for automated sentiment analysis of both text and non-text data.
Benefits
- Improved accuracy in sentiment analysis.
- Automation of actions based on sentiment classification.
- Enhanced understanding of user sentiment.
Commercial Applications
- Customer feedback analysis tools.
- Social media monitoring platforms.
- Market research software.
Questions about Sentiment Analysis
How does this technology improve sentiment analysis accuracy?
This technology utilizes artificial intelligence techniques to analyze both text and non-text data, improving the accuracy of sentiment analysis.
What are the potential applications of automated sentiment analysis?
Automated sentiment analysis can be applied in various fields such as customer feedback analysis, social media sentiment analysis, and market research.
Original Abstract Submitted
Methods, apparatus, and processor-readable storage media for determining input sentiment by processing text data and non-text data using artificial intelligence techniques are provided herein. An example computer-implemented method includes dividing user input data into at least a first set of text data and a set of non-text data; converting at least a first portion of the non-text data into at least a second set of text data; classifying at least a portion of the at least a first set of text data and at least a portion of the at least a second set of text data in accordance with sentiment-related categories using a first set of artificial intelligence techniques; classifying at least a second portion of the non-text data in accordance with the sentiment-related categories using a second set of artificial intelligence techniques; and performing automated actions based on the classifying of the text data and/or the non-text data.
