Word Embedding Model for Public Sentiment Visualization

The patent replaces traditional mechanical text analysis methods with a neural network-based word embedding system. The neural network automatically learns semantic representations of words and n-grams, enabling accurate sentiment analysis without manual feature engineering. This substitution of mechanical analysis with intelligent learning systems resolves the contradiction by achieving high accuracy while managing complexity through automated processes.

The patent introduces word embeddings as an intermediary layer between raw text data and sentiment analysis. The embedding layer transforms discrete text tokens into continuous vector representations that capture semantic meaning, serving as a bridge that enables accurate analysis. This intermediary mechanism allows the system to achieve high measurement precision by translating text into a form that reveals underlying semantic structures.

The patent performs preliminary action by pre-training the word embedding model on a large corpus of text data before deploying it for sentiment analysis. This pre-training phase creates reusable word vector representations that can be quickly applied to new text data without retraining. By performing the computationally intensive learning process in advance, the system achieves high accuracy while reducing processing time for actual sentiment analysis tasks.

The patent uses word embeddings to create vector copies of text data that preserve semantic meaning. Instead of processing raw text repeatedly, the system works with compressed vector representations that capture the essential semantic information. This copying mechanism reduces computational overhead and processing time while maintaining analysis accuracy, as the vector representations can be efficiently manipulated and compared.

The patent extracts only the most relevant n-grams and their word vectors for sentiment analysis rather than calculating vectors for all possible n-grams. The system identifies and extracts key semantic units from the text that are most informative for sentiment determination. This selective extraction approach maintains complete semantic coverage for relevant concepts while reducing computational complexity by focusing resources on the most important elements.

The patent applies local quality by using different levels of n-gram granularity in different contexts. The system dynamically selects between unigrams, bigrams, and longer n-grams based on the specific analysis needs and local text characteristics. This localized approach to n-gram selection ensures semantic coverage where needed while reducing overall computational complexity by avoiding uniform processing of all possible n-gram lengths across the entire text corpus.