Sentiment Engine Modules for Social Media Opinion Analysis
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Solution Overview
Problem
The abundance of social media content makes it difficult for customers and service providers to extract and analyze opinions about products, hotels, and services effectively, as most reviews are lengthy and contain limited opinion-bearing sentences, hindering informed decision-making.
Innovation Solution
A sentiment engine is developed to aggregate content from social media websites, comprising modules for crawling, keyword extraction, filtering, classification, polarity prediction, and social media net promoter score calculation, to determine sentiment and loyalty metrics, facilitating opinion summarization across various categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If social media content aggregation is performed to gather more opinions, then the quantity of available sentiment data increases, but the complexity of processing and analyzing the content increases
Solution Approach 1:
The patent segments the complex sentiment analysis task into multiple specialized modules: crawling module for data collection, keyword extraction module for identifying sentiment-bearing terms, classification module for categorizing opinions, and polarity prediction module for determining sentiment orientation. Each module handles a specific aspect of the analysis, making the overall system more manageable and efficient despite processing large quantities of social media content.
Solution Approach 2:
The patent introduces intermediate processing layers between raw social media content and final sentiment analysis results. These include keyword extraction as an intermediary step to identify sentiment-bearing terms, and classification as another intermediary layer to organize opinions before polarity prediction. These intermediary steps simplify the overall analysis process by breaking down the complex task into manageable stages.
2Measurement precision
If comprehensive keyword extraction is performed to identify all sentiment-bearing keywords, then the accuracy of sentiment analysis improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary keyword extraction and classification before polarity prediction. By pre-identifying sentiment-bearing keywords and categorizing opinions in advance, the system prepares the data structure needed for accurate sentiment analysis, reducing the time required for the final polarity prediction step while maintaining high accuracy.
Solution Approach 2:
The patent focuses keyword extraction on sentiment-bearing keywords specifically, rather than extracting all possible keywords from the text. This partial action approach concentrates processing resources on the most relevant terms for sentiment analysis, achieving high accuracy without the time cost of comprehensive keyword extraction across all text elements.
3Loss of information
If detailed classification of opinions by category is performed, then the usefulness of extracted insights improves, but the complexity of the analysis process increases
Solution Approach 1:
The patent segments opinions into distinct categories (e.g., product features, service quality, customer experience) as a separate classification step before final sentiment prediction. This segmentation organizes the data into meaningful groups, making the extracted insights more useful and actionable while keeping the analysis process structured and manageable through clear separation of classification and prediction functions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The sentiment engine effectively extracts and classifies opinions, providing actionable insights for customers and service providers by analyzing sentiment-bearing keywords and predicting sentiment polarity, thus enhancing decision-making with accurate sentiment analysis.
Implementation Method 1
extracting, by a processor, the metadata from the social media content by identifying a polarity of the sentiment-bearing keywords in a sentence from social media content using a phase transition formula
Data Source
AI summary
The sentiment engine includes a sentiment module configured to gather opinions or determine sentiment expressed in documents, a crawling module configured to crawl servers to obtain at least a subset of the documents or opinions from social media websites, a keyword module configured to extract keywords from documents, a filtering module configured to filter keywords and documents, and a classification module configured to classify documents, sentences, and/or keywords, a polarity prediction module configured to predict the polarity of a sentiment sentence, and a social media net promoter score (SNPS) configured to calculate a loyalty metric of users from social media websites. The functionality of these modules may be combined with one another or in addition to other modules.