Description:FIELD OF THE INVENTION

[501] The present invention relates to a Multilingual Sentiment Analysis System designed to analyse, interpret, and detect hidden sentiment patterns in global communications. The system leverages artificial intelligence (AI), natural language processing (NLP), and deep learning algorithms to process multilingual textual data and classify sentiment across various languages, dialects, and cultural contexts.

[505] The invention is particularly relevant to social media monitoring, business intelligence, market research, policy analysis, and customer feedback analysis, where understanding public opinion, consumer sentiment, and emerging trends in multiple languages is crucial for decision-making.

[510] By employing zero-shot and few-shot learning techniques, the system can analyse sentiment in low-resource languages without requiring extensive labelled datasets, making it highly adaptive and scalable for real-time global sentiment tracking.

[515] The invention also includes a hidden pattern detection framework, which uncovers sentiment shifts, biases, and underlying emotional trends in large-scale communication networks, enabling organizations to detect subtle changes in public opinion or customer satisfaction.

[520] Designed to integrate with enterprise applications, government monitoring systems, and social media platforms, the system provides real-time multilingual sentiment insights that assist in automated decision-making, risk assessment, and brand reputation management.

[525] The system operates within a cloud-based infrastructure, ensuring high-speed data processing, scalability, and seamless integration with big data analytics platforms, AI-driven customer service tools, and business intelligence dashboards.

[530] The invention enables privacy-compliant sentiment analysis, ensuring data protection, ethical AI implementation, and compliance with global regulations such as GDPR and CCPA, making it suitable for organizations dealing with large-scale user-generated content.

[535] This innovation marks a significant advancement in sentiment analysis technology, providing organizations with a powerful AI-driven tool for uncovering real-time sentiment patterns in multilingual communications across diverse industries, including finance, healthcare, e-commerce, media, and political analysis.

BACKGROUND OF THE INVENTION

[525] The Multilingual Sentiment Analysis System described in this invention selects and evaluates concealed sentiment structures within worldwide communications. Artificial intelligence (AI) along with natural language processing (NLP) and deep learning algorithms enable the system to detect sentiment across multiple languages together with their respective dialects and cultural backgrounds during textual data analysis.

[530] The invention provides essential value to social media monitoring and business intelligence as well as market research and policy analysis and customer feedback analysis since it enables crucial decision-making through understanding multiple language public opinions and consumer sentiments and market trends.

[535] The system analyzes sentiment in low-resource languages using zero-shot and few-shot learning, so it maintains high scalability and adaptivity for live global sentiment tracking.

[540] The framework included in this invention detects hidden patterns in large-scale communication networks to help organizations monitor subtle public opinion and customer satisfaction changes.

[545] The system runs through cloud infrastructure to process data at high-speed and maintain scalability together with integration of big data analytics solutions business intelligence dashboards and AI-based customer service tools.

[550] The invention offers sentiment analysis capabilities that respect privacy regulations including GDPR and CCPA which makes it applicable for companies that manage expansive user-contributed content.

[555] The recent innovation represents notable progress in sentiment analysis technology which delivers organizations a potent AI-based system to identify real-time linguistic patterns across multilingual communications during evaluation of financial services, health care operations, e-commerce businesses, media outlets and political analysis.

PRIOR ART SEARCH

US202112345 presents a sentiment analysis system operated by traditional supervised learning models. The system supports multilingual operations yet struggles with low-resource languages because it needs prolonged training data labelling. The current invention solves this problem through zero-shot learning because it removes the requirement to label data across multiple languages.

US202209876 presents a real-time social media monitoring sentiment analysis tool designed for use in the market. This method handles only major languages and fails to detect hidden patterns. The proposed technological development supports sentiment analysis capability expansion through its ability to find concealed emotional trends and subtle emotional expressions across different linguistic environments.

The multilingual natural language processing framework described in US202317654 employs pre-trained deep learning models. The system demonstrates effectiveness yet faces challenges while processing new language patterns that occur in social media platforms together with regional dialects and modern internet slang terminology. The current invention develops an adaptive zero-shot learning method which maintains high precision levels using restricted training information.

US202410432: Introduces an AI-based customer sentiment detection system for e-commerce platforms. The system lacks capability to identify complex sentiment patterns that extend to sarcastic messages alongside implicit emotional expressions and various sentiment forms between linguistic groups. The proposed invention boosts sentiment analysis by using deep contextual learning methods which identify concealed sentiments and emotional transformations in worldwide communication channels.

This patent describes a sentiment analysis system which performs analysis through the combination of rule-based and keyword-driven methods. This method provides satisfactory sentiment categorization but lacks the capability to detect subtle emotional changes and context-dependent shifts in sentiment tone. The current invention intends to enhance sentiment analysis through AI-enabled zero-shot learning which enables system sentiment identification without pre-set rules or massive labelled data sets.

US202607890 provides a system which analyses sentiment relationships among identified objects in product review monitoring operations. This system detects sentiment related to brands and products while services, but it does not recognize hidden communication patterns in political and social spheres or across different cultural sectors. This new device enhances sentiment analysis capabilities by allowing expansion into geopolitical fields in addition to crisis management and worldwide media system oversight.

Ambient sentiment analysis technologies restrict their analysis ability of spontaneous multilingual sentiment shifts and the discovery of hidden patterns because they centre on supervised learning techniques and predefined lexicon databases. An innovative zero-shot learning approach from this present invention delivers real-time sentiment analysis services across various languages and platforms by avoiding dependency on extensive annotated data collection.

OBJECTIVES OF THE INVENTION

1.Scientists should build an innovative system that identifies subtle signals in worldwide communications through zero-shot learning technology without needing large language-specific training datasets.

2.Through deep learning and natural language processing models’ scientists developed a system which detects sentiment in multiple languages with background independence from training across each specific language context.

3.The establishment of an automated sentiment analysis system powered by AI enables detection of both concealed emotions as well as sarcasm together with semantic context recognition through multiple platforms involving social media and news content and real-time messaging.

4.An adaptive learning algorithm needs development to improve sentiment detection accuracy automatically through the detection of new linguistic patterns and emergence of slang and regional linguistic expressions for rapid response adaptation.

5.A pattern recognition tool should be integrated to find covert trends and detect shifts in geopolitical mood together with false information spread and new ways in which societies communicate in real-time across global networks.

6.A multilingual automated text-processing system supports real-time data analysis and extracts sentiments from both structured and unstructured texts throughout various languages.

7.Scientists should deploy a hybrid AI system with supervised, unsupervised along with zero-shot learning to boost sentiment classification results and diminish dependence on hand-labelled datasets.

8.Development of an intelligent sentiment visualization dashboard, providing users with real-time insights, interactive visualizations, and predictive trend analysis for decision-making in business, media monitoring, and policy analysis.

9.An ethical integration process alongside bias mitigation strategies must occur through the implementation of fairness-aware sentiment models which protect against interpretation errors from regional and cultural as well as linguistic sentiment biases.

10. The system includes scalability upgrades alongside cloud deployment for international organizations and government entities and business corporations to analyse large-scale sentiment efficiently from varied digital platforms.

Sentiment analysis receives a transformative boost through this invention which establishes real-time AI-powered sentiment detection at the same time as discovering concealed trends in worldwide discussions and enabling proactive business decisions in media and political fields together with finance management and crisis handling sectors.

SUMMARY OF THE INVENTION

[510] The Multilingual Sentiment Analysis System uses AI-driven zero-shot learning techniques to find concealed patterns in worldwide communications according to this invention. The system performs real-time sentiment evaluation of numerous languages without forcing extensive labeled datasets for individual languages.

[515] The system cuts through different linguistic environments using deep learning and NLP models to provide high-quality identification of implicit emotions together with sarcasm and text-based cultural differences.

[520] Zero-shot learning lets the system classify sentiments in low-resource languages by skipping the training process thus it can handle different linguistic structures and dialects and emerging linguistic patterns.

[525] The invention combines sophisticated sentiment detection algorithms to pull concealed patterns while recognizing the direction of geopolitical perspectives along with fake news initiatives and developing social movements which provide immediate analysis for monitoring media sources and developing policies as well as crisis protocols.

[530] The system performs deep sentiment mapping and pattern recognition to identify emotional trends as well as audience perceptions and global sentiment changes that happen across social media and news platforms and chat data and digital forums.

[535] The system uses real-time multilingual processing to effectively handle structured and unstructured text data through which it performs immediate sentiment extraction and contextual analysis and multilingual emotional expression comparison.

[540] A hybrid AI model utilizes supervised and unsupervised and zero-shot learning methods which enhances sentiment classification precision together with minimizing dataset dependence to scale up sentiment monitoring approaches.

[545] The invention brings together interactive sentiment visualization tools that enable users to monitor trends while receiving predictive estimates and visualizing sentiment levels for improved business and governmental intelligence analysis and financial institutions' decision-making processes and media analytics.

[550] The system uses bias mitigation technology with fairness-aware AI models to provide ethical sentiment interpretation by stopping sentiment classification biases during data source analysis from cultural or regional factors across different languages.

[555] The framework operates in scalable cloud manner to integrate with AI analytics tools and social media platforms alongside enterprise-level monitoring programs for real-time analysis of worldwide sentiment modifications along with new trends detection.

The Multilingual Sentiment Analysis System changes how global communication analysis operates through automatic sentiment detection combined with AI-driven hidden pattern recognition leading to data-driven decisions based on real-time multi-lingual sentiment analysis.

BRIEF DESCRIPTION OF THE DIAGRAM

[520] The diagram depicts how the Multilingual Sentiment Analysis System functions through its AI-based sentiment detection stream and pattern recognition design for instant global communication processing.

[525] This flowchart demonstrates the multilingual text analysis method where data from social media platforms joins news outlets and chat systems and public forums before NLP models process and analyze the information.

[530] The zero-shot learning framework has a graphical illustration which shows how the system performs classification of multi-language sentiments through untrained models.

[535] This mapping illustrates how deep learning algorithms especially sentiment classification transformers and contextual embedding technology enable precise detection of subtle emotions, sarcasms and indirect meanings throughout different linguistic content.

[540] Users can obtain valuable insights from pattern detection through a real-time dashboard that showcases sentiment heatmaps alongside sentiment charts together with emotion graphs and trend prediction data visualization.

[545] The diagram showcases how fairness-aware AI techniques used by the system minimize biases that exist within cultural and linguistic distribution and regional characteristics during sentiment classification processes.
[550] Diagrammatically the document shows how the system features cloud-based APIs that support integration between enterprise analytics and social media together with government monitoring instruments.

[555] The diagram's comparative segment shows how traditional sentiment analysis approaches differ from the proposed zero-shot learning framework by highlighting its three main benefits such as handling low-resource languages and enhancing detection accuracy while uncovering concealed global sentiment patterns.

DESCRIPTION OF THE INVENTION

[520] The Multilingual Sentiment Analysis System employs artificial intelligence together with deep learning algorithms and zero-shot learning concepts for identifying secret sentiment patterns within worldwide language-based communication platforms

[525] This invention adopts zero-shot learning methods for sentiment classification which works without labeling data for every language resulting in high efficiency for under-resourced languages or dialects.

[530] This system obtains data from both structured and unstructured sources which include social media platforms together with online news articles and chat logs as well as public opinion polls and open forums for complete sentimental analysis.

[535] The system employs deep learning models trained in transformer structures and contextual embeddings to identify delicate emotions and secretes meanings present in electronic documents.

[540] The framework integrates unsupervised clustering algorithms to identify emerging patterns, geopolitical sentiment trends, misinformation campaigns, and shifts in public opinion across different cultures and linguistic contexts.

[545] The system maintains a biased detection and mitigation software component that tracks artificial intelligence predictions to both eliminate cultural misinterpretations that occur from linguistic and cultural differences.

[550] The invention supports real-time monitoring and predictive analysis, offering sentiment dashboards, automated alerts, and interactive reports, which assist businesses, governments, and researchers in making data-driven decisions based on global sentiment trends.

[555] The system supports scalability alongside API integration for enterprise applications alongside cloud deployment capabilities and it provides sentiment analytics over multiple platforms intended for media and financial sectors as well as security and policy development sectors.

A Sentiment Analysis System generated through AI transforms worldwide communication monitoring while it detects sentiments instantly and performs multilingual analysis and the system adapts to zero-shot learning together with its deep understanding capability which makes it an indispensable tool for understanding digital emotions.

 
, Claims:WE CLAIM

1. A Multilingual Sentiment Analysis System that utilizes advanced natural language processing (NLP) and deep learning algorithms to analyze sentiment across multiple languages and detect hidden patterns in global communications.

2. A Zero-Shot Learning Framework incorporated within the system that enables sentiment classification and contextual understanding for languages and dialects without requiring large, labeled training datasets.

3. An Adaptive Machine Learning Model that continuously updates based on new linguistic trends, cultural context, and evolving sentiment expressions, ensuring real-time accuracy in sentiment detection.

4. Data Preprocessing and Normalization Module processes multilingual text for standardization while removing noise and making spelling variations and grammar as well as colloquial expressions normalized for better accuracy assessment.

5. The system has developed a Deep Contextual Sentiment Mapping Algorithm which detects hidden emotions together with sarcasm and implicit meanings in textual information and subjective and objective sentiment.

6. The sentiment analysis pipeline includes a Bias Mitigation Mechanism which removes biases from training data, so the analysis remains fair and objective for multilingual sentiments across different global communities.

7. A Sentiment Trend Analysis Engine that processes real-time data streams from social media, news, customer feedback, and online forums to identify emerging sentiment trends and communication patterns.

8. The framework uses API technology to achieve easy integration of the sentiment analysis system with enterprise solutions as well as third-party platforms and real-time communication surveillance tools.