Cross-Lingual Sentiment Seeding for Fast Foreign-Language Detection
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Solution Overview
Problem
Current methods for determining sentiment in customer communications, especially in foreign languages, are ineffective or inefficient, particularly in noisy environments and when emoticons are culturally ambiguous, making it difficult for companies to quickly identify negative sentiments that require agent attention.
Innovation Solution
A method that retrieves sentiment terms in a foreign language associated with seed terms from different languages, using a processor to identify and analyze these terms in messages, and calculates a confidence score to determine the sentiment, with the option to re-calculate if the initial score does not reach a predetermined threshold, allowing for efficient identification of positive or negative sentiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sentiment analysis is performed on foreign language messages using traditional methods, then the system can process messages, but the accuracy is low and response time is slow
Solution Approach 1:
The system performs preliminary actions by pre-identifying sentiment terms in foreign languages and storing them in a database before actual sentiment analysis is needed. When a message arrives, the system quickly retrieves pre-identified sentiment terms matching the message content, rather than performing complex analysis from scratch. This preliminary preparation significantly reduces response time while maintaining high accuracy.
Solution Approach 2:
The patent introduces an intermediary component - a database storing pre-identified sentiment terms in multiple foreign languages. This database acts as a mediator between incoming messages and the sentiment analysis engine, enabling fast retrieval of relevant sentiment terms without requiring complex real-time translation or analysis, thus improving both speed and accuracy.
2Reliability
If traditional sentiment analysis methods are used on noisy foreign language data, then the system attempts to analyze all messages, but the reliability of sentiment determination decreases
Solution Approach 1:
The system applies local quality by focusing analysis only on specific portions of messages - namely, segments containing pre-identified sentiment terms. Rather than attempting to analyze entire noisy messages, the system locates and analyzes only the relevant sentiment-bearing portions, improving reliability by concentrating on high-quality signal segments while ignoring noisy surrounding text.
Solution Approach 2:
The patent employs partial action by performing sentiment analysis only on message segments containing matched sentiment terms rather than analyzing entire messages. This selective approach improves reliability by focusing computational resources on the most informative portions of messages, avoiding the noise and complexity of analyzing complete message texts.
3Productivity
If the system uses a simple threshold-based confidence score, then the processing is fast, but the sentiment analysis may be inaccurate in ambiguous cases
Solution Approach 1:
The system implements dynamics by making the confidence score threshold adaptive rather than fixed. The threshold adjusts dynamically based on message characteristics, sentiment term frequency, and contextual factors. This allows the system to maintain high processing speed for clear cases while applying more stringent evaluation for ambiguous cases, thereby improving overall precision without sacrificing productivity.
Solution Approach 2:
The patent applies parameter changes by modifying the confidence score threshold parameter based on different message contexts and sentiment term types. Rather than using a single fixed threshold, the system varies the threshold parameter dynamically, allowing it to maintain high processing speed for straightforward cases while improving precision for ambiguous cases by adjusting the decision criterion.
Data Source
AI summary
A contact center system can receive messages from social media sites or centers. The messages may be in a foreign language. The system can review messages by identifying content in the social media messages with negative/positive sentiment and then identify a seed term in the messages. A seed term can be a word in another language, different from the message body. The seed term is then used to find one or more other words, in the foreign language, that are correlated with the seed term. The identification of the found words in other messages can then be used to determine sentiment in the foreign language.