Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a language interpreter and particularly to a sarcastic expression interpretation system.
Description of Related Art
[002] Sarcasm detection remains a critical challenge in the field of Natural Language Processing (NLP). Traditional sentiment analysis tools struggle to accurately interpret sarcastic expressions, often misclassifying them as literal statements. This misinterpretation arises because sarcasm inherently conveys a meaning opposite to its literal phrasing, making it difficult for conventional text-based algorithms to discern. The widespread use of sarcasm in digital communication, especially on social media platforms, further exacerbates this issue, leading to inaccuracies in sentiment classification and content moderation.
[003] Existing sarcasm detection methodologies primarily rely on supervised learning techniques trained on labeled datasets. These approaches predominantly analyze textual patterns without incorporating additional contextual elements such as images, emojis, and tone, which are often crucial for understanding sarcasm. While advanced Natural Language Processing (NLP) models, including transformer-based architectures, have improved language comprehension, they still struggle with multimodal sarcasm detection. Additionally, variations in linguistic styles, cultural differences, and evolving online communication trends pose further obstacles to achieving high accuracy in sarcasm identification.
[004] Despite ongoing research efforts, commercially available sentiment analysis tools, such as Google Perspective Application Programming Interface (API) and IBM Watson NLU, lack a dedicated sarcasm detection framework. Current implementations tend to exhibit high false-positive and false-negative rates due to their reliance on purely text-based processing. The need for a more sophisticated approach that integrates multiple data modalities such as text, images, and emojis—has become evident in order to enhance the precision of sarcasm detection and improve the overall effectiveness of Natural Language Processing (NLP)-based sentiment analysis systems.
[005] There is thus a need for an improved and advanced sarcastic expression interpretation system that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a sarcastic expression interpretation system. The system comprising a data acquisition unit adapted to receive excerpts from a computing device. The system further comprising a processing unit in communication with the data acquisition unit. The processing unit is configured to extract, using a context aware Natural Language Processing (NLP) engine, textual cues, user intent, conversation context, or a combination thereof from the excerpts fed into the data acquisition unit; analyze the textual cues, the user intent, the conversation context, or a combination thereof to differentiate sarcastic statements from genuine expressions; processes, using an integrated multimodal learning, the excerpts alongside accompanying images or emojis to enhance sarcasm detection accuracy; employ, using a deep learning-based classification model to detect implicit sarcasm in the received excerpts, and identify, using an emotion and sentiment correlation base, inconsistencies between expressed emotions and linguistic patterns to detect sarcasm.
[007] Embodiments in accordance with the present invention further provide a method for content base sarcasm detection with sarcastic expression interpretation. The method comprising steps of extracting, using a context aware Natural Language Processing (NLP) engine, textual cues, user intent, conversation context, or a combination thereof from excerpts fed into a data acquisition unit; analyzing the textual cues, the user intent, the conversation context, or a combination thereof to differentiate sarcastic statements from genuine expressions; processing, using convolutional neural networks (CNNs) or vision transformers, the excerpts alongside accompanying images or emojis to enhance sarcasm detection accuracy; classifying, using a deep learning-based classification model to detect implicit sarcasm in the received excerpts, and identifying, using an emotion and sentiment correlation base, inconsistencies between expressed emotions and linguistic patterns to detect sarcasm.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a sarcastic expression interpretation system.
[009] Next, embodiments of the present application may provide a sarcastic expression interpretation system that integrates text, images, and emojis to improve sarcasm interpretation accuracy.
[0010] Next, embodiments of the present application may provide a sarcastic expression interpretation system that leverages deep learning techniques to analyze conversational context, user intent, and sentiment inconsistencies, reducing misclassification.
[0011] Next, embodiments of the present application may provide a sarcastic expression interpretation system that significantly reduces false positives and negatives in sarcasm detection.
[0012] Next, embodiments of the present application may provide a sarcastic expression interpretation system that is suitable for various applications like social media monitoring, customer service automation, and content moderation.
[0013] Next, embodiments of the present application may provide a sarcastic expression interpretation system that is trained to recognize sarcasm across multiple languages, regional dialects, and slang variations, making it more versatile.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
[0015] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0017] FIG. 1 illustrates a block diagram of a sarcastic expression interpretation system, according to an embodiment of the present invention;
[0018] FIG. 2 illustrates a block diagram of a processing unit of the sarcastic expression interpretation system, according to an embodiment of the present invention; and
[0019] FIG. 3 depicts a flowchart of a method for content base sarcasm detection with sarcastic expression interpretation, according to an embodiment of the present invention.
[0020] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0021] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0022] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0023] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0024] FIG. 1 illustrates a block diagram of a sarcastic expression interpretation system 100 (hereinafter referred to as the system 100), according to an embodiment of the present invention. The system 100 may be adapted to receive excerpts. Further, the system 100 may be adapted to detect a presence of sarcasm in the received excerpts. Moreover, the system 100 may further detect a presence of slang, misnomer, humor, and so forth in the received excerpts. Embodiments of the present invention are intended to include or otherwise cover any figure of the excerpts, including known, related art, and/or later developed technologies.
[0025] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance the processing speed and efficiency such as the system 100 may comprise a computing device 102, a data acquisition unit 104, and a processing unit 106. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0026] In an embodiment of the present invention, the computing device 102 may be adapted to upload the excerpts to the system 100. The computing device 102 may be, but not limited to, a laptop, a mobile, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the computing device 102, including known, related art, and/or later developed technologies.
[0027] In an embodiment of the present invention, the data acquisition unit 104 may be adapted to receive the excerpts from the computing device 102.
[0028] In an embodiment of the present invention, the processing unit 106 may be in communication with the image acquisition unit 104. The processing unit 106 may further be configured to execute computer-executable instructions to generate an output relating to the system 100. In an embodiment of the present invention, the processing unit 106 may be configured to involve a real-time Application Programming Interface (API) deployment engine 116. The real-time Application Programming Interface (API) deployment engine 116 may be adapted to provide scalable integration of the system 100 for social media monitoring, brand analysis, automated moderation systems, and so forth. Further, the real-time Application Programming Interface (API) deployment engine 116 may facilitate integration with third-party applications via Representational State Transfer Application Programming Interface (RESTful APIs), Web Sockets, cloud-based services, and so forth.
[0029] According to embodiments of the present invention, the processing unit 106 may be, but not limited to, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the processing unit 106 including known, related art, and/or later developed technologies. In an embodiment of the present invention, the processing unit 106 may further be explained in conjunction with FIG. 2.
[0030] FIG. 2 illustrates a block diagram of the processing unit 106 of the system 100, according to an embodiment of the present invention. The processing unit 106 may comprise the computer-executable instructions in form of programming modules such as a data extraction module 200, a data execution module 202, and a data classification module 204.
[0031] In an embodiment of the present invention, the data extraction module 200 may be configured to enable a context aware Natural Language Processing (NLP) engine 108. The enablement of the context aware Natural Language Processing (NLP) engine 108 may activate the data extraction module 200 to extract excerpts fed into the data acquisition unit 104. The context aware Natural Language Processing (NLP) engine 108 may employ syntactic, semantic, and pragmatic analysis to extract context-specific meaning.
[0032] Further, the enablement of the context aware Natural Language Processing (NLP) engine 108 may activate the data extraction module 200 to extract textual cues, user intent, conversation context, and so forth from the excerpts fed into the data acquisition unit 104. The data extraction module 200 may be configured to transmit the extracted excerpts, or the extracted textual cues, user intent, conversation context, to the data execution module 202.
[0033] The data execution module 202 may be activated upon receipt of the extracted excerpts, or the extracted textual cues, user intent, conversation context, from the data extraction module 200. In an embodiment of the present invention, the data execution module 202 may be configured to analyze the textual cues, the user intent, the conversation context, and so forth to differentiate sarcastic statements from genuine expressions.
[0034] In an embodiment of the present invention, the data execution module 202 may be configured to engage an integrated multimodal learning 110. The engagement of the integrated multimodal learning 110 may enable the data execution module 202 to process the excerpts alongside accompanying images or emojis to enhance sarcasm detection accuracy. The integrated multimodal learning 110 may comprise convolutional neural networks (CNNs), vision transformers, and so forth. Further, upon analysis and process of the excerpts along with the accompanying images or emojis, the data execution module 202 may be configured to transmit an activation signal to the data classification module 204.
[0035] The data classification module 204 may be activated upon receipt of the activation signal from the data execution module 202. The data classification module 204 may enable a deployment of a deep learning-based classification model 112. The deep learning-based classification model 112 may utilize pre-trained transformer models such as, but not limited to, a Generative Pre-trained Transformer (GPT), a T5-fine-tuned, and so forth, for sarcasm detection. Embodiments of the present invention are intended to include or otherwise cover any pre-trained transformer models, including known, related art, and/or later developed technologies. The deep learning-based classification model 112 may be adapted to adapted to detect inconsistencies between expressed emotions and linguistic patterns to identify sarcastic expressions.
[0036] Further, the data classification module 204 may be configured to activate an emotion and sentiment correlation base 114. The activation of the emotion and sentiment correlation base 114 may enable the identification of inconsistencies between expressed emotions and linguistic patterns to detect sarcasm. The emotion and sentiment correlation base 114 may further apply sentiment analysis algorithms to detect emotional contradictions in text and flag potential sarcasm.
[0037] In an exemplary scenario of the present invention, the data extraction module 200 may be activated upon reception of an excerpt such as “Methinks thou art a general offense, and every man should beat thee.” From All’s Well That Ends Well by William Shakespeare. The enablement of the context aware Natural Language Processing (NLP) engine 108 may activate the data extraction module 200 to extract textual cues, user intent, and conversation context. The extracted textual cues may include lexical choices such as archaic expressions “Methinks” and “thou art”, indicating a classical linguistic structure. Further, the phrase “every man should beat thee” may be identified as an instance of exaggeration, a hallmark of sarcasm or jest, along with negative sentiment conveyed by words like “offense” and “beat thee”. The user intent may be extracted as ridicule, where the statement, rather than being a literal call to action, employs hyperbolic insult to mock or belittle the recipient. The phrase may further indicate sarcasm, given its over-the-top nature, making it an unlikely serious remark. The conversation context may be assessed to determine whether the statement is part of a humorous exchange, a confrontational dialogue, or an assertion of dominance by the speaker. The data extraction module 200 may transmit the extracted cues, user intent, and conversation context to the data execution module 202 for further processing.
[0038] The data execution module 202 may analyze the received data to differentiate between a literal insult and a sarcastic remark. The execution module 202 may engage the integrated multimodal learning 110 to determine whether external markers, such as emojis or accompanying visual elements, reinforce a sarcastic tone. For instance, if the phrase is accompanied by a laughing emoji, the system 100 may classify it as jest rather than aggression. Based on the syntactic and semantic structure of the statement, the data execution module 202 may categorize the phrase under sentiment analysis as a strongly negative statement with humorous undertones, under irony/sarcasm detection due to the exaggerated punishment suggested, under hyperbolic insult classification as an instance of Shakespearean humor, and under linguistic style identification as an archaic expression requiring historical context for accurate interpretation.
[0039] Upon analysis and categorization, the data execution module 202 may transmit the activation signal to the data classification module 204. The data classification module 204 may be activated upon receipt of the activation signal from the data execution module 202. The data classification module 204 may enable the deployment of a deep learning-based classification model 112 to finalize sarcasm detection. The deep learning-based classification model 112 may utilize pre-trained transformers such as a Generative Pre-trained Transformer (GPT) or a T5-fine-tuned model to analyze inconsistencies between the phrase’s linguistic structure and intended meaning. Further, the data classification module 204 may activate the emotion and sentiment correlation base 114 to assess sentiment polarity and verify if the linguistic contradiction aligns with sarcastic expression. Upon final analysis, the system 100 may classify the excerpt as a sarcastic remark and may flag the excerpt accordingly for downstream applications, such as the sentiment analysis, conversational AI, or humor recognition.
[0040] FIG. 3 depicts a flowchart of a method 300 for content base sarcasm detection with sarcastic expression interpretation, according to an embodiment of the present invention.
[0041] At step 302, the system 100 may extract the textual cues, the user intent, the conversation context, and so forth from the excerpts fed into the data acquisition unit 104.
[0042] At step 304, the system 100 may analyze the textual cues, the user intent, the conversation context, and so forth to differentiate the sarcastic statements from the genuine expressions.
[0043] At step 306, the system 100 may processes the excerpts alongside the accompanying images or emojis to enhance the sarcasm detection accuracy.
[0044] At step 308, the system 100 may employ the deep learning-based classification model 112 to detect the implicit sarcasm in the fed excerpts.
[0045] At step 310, the system 100 may identify inconsistencies between the expressed emotions and the linguistic patterns to detect sarcasm.
[0046] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0047] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A sarcastic expression interpretation system (100), the system (100) comprising:
a data acquisition unit (104) adapted to receive excerpts from a computing device (102); and
a processing unit (106) in communication with the data acquisition unit (104), characterized in that the processing unit (106) is configured to:
extract, using a context aware Natural Language Processing (NLP) engine (108), textual cues, user intent, conversation context, or a combination thereof from the excerpts fed into the data acquisition unit (104);
analyze the textual cues, the user intent, the conversation context, or a combination thereof to differentiate sarcastic statements from genuine expressions;
processes, using an integrated multimodal learning (110), the excerpts alongside accompanying images or emojis to enhance sarcasm detection accuracy;
employ, using a deep learning-based classification model (112) to detect implicit sarcasm in the received excerpts; and
identify, using an emotion and sentiment correlation base (114), inconsistencies between expressed emotions and linguistic patterns to detect sarcasm.
2. The system (100) as claimed in claim 1, comprising a real-time Application Programming Interface (API) deployment engine (116) adapted to provide scalable integration for social media monitoring, brand analysis, automated moderation systems, or a combination thereof, wherein the real-time Application Programming Interface (API) deployment engine (116) facilitates integration with third-party applications via Representational State Transfer Application Programming Interface (RESTful APIs), Web Sockets, cloud-based services, or a combination thereof.
3. The system (100) as claimed in claim 1, wherein the context-aware Natural Language Processing (NLP) engine employs syntactic, semantic, and pragmatic analysis to extract context-specific meaning.
4. The system (100) as claimed in claim 1, wherein the deep learning-based classification model (112) is selected from a Generative Pre-trained Transformer (GPT), a T5-fine-tuned, or a combination thereof for sarcasm detection.
5. The system (100) as claimed in claim 1, wherein the deep learning-based classification model (112) is adapted to detect inconsistencies between expressed emotions and linguistic patterns to identify sarcastic expressions.
6. The system (100) as claimed in claim 1, wherein the emotion and sentiment correlation base (114) applies sentiment analysis algorithms to detect emotional contradictions in text and flag potential sarcasm.
7. The system (100) as claimed in claim 1, wherein the integrated multimodal learning (110) comprise convolutional neural networks (CNNs), vision transformers, or a combination thereof.
8. A method (300) for content base sarcasm detection with sarcastic expression interpretation, the method (300) is characterized by steps of:
extracting, using a context aware Natural Language Processing (NLP) engine (108), textual cues, user intent, conversation context, or a combination thereof from excerpts fed into a data acquisition unit (104);
analyzing the textual cues, the user intent, the conversation context, or a combination thereof to differentiate sarcastic statements from genuine expressions;
processing, using convolutional neural networks (CNNs) or vision transformers, the excerpts alongside accompanying images or emojis to enhance sarcasm detection accuracy;
classifying, using a deep learning-based classification model (112) to detect implicit sarcasm in the received excerpts, and
identifying, using an emotion and sentiment correlation base (114), inconsistencies between expressed emotions and linguistic patterns to detect sarcasm.
9. The method (300) as claimed in claim 8, comprising a real-time API deployment engine adapted to provide scalable integration for social media monitoring, brand analysis, automated moderation systems, or a combination thereof, wherein the real-time API deployment engine facilitates integration with third-party applications via Representational State Transfer Application Programming Interface (RESTful APIs), Web Sockets, cloud-based services, or a combination thereof.
10. The method (300) as claimed in claim 8, wherein the context-aware Natural Language Processing (NLP) engine employs syntactic, semantic, and pragmatic analysis to extract context-specific meaning.
Date: March 26, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant