1. A method for diagnosing mental health disorders using sentiment analysis, comprising collecting online content related to an individual, applying sentiment analysis to the online content to extract sentiment information, identifying specific language patterns that are associated with one or more mental health disorders, and using machine learning algorithms to develop a model that predicts the likelihood of the individual having the mental health disorder based on the language patterns identified.

2. A method for diagnosing mental health disorders using sentiment analysis, comprising collecting online content related to an individual, applying sentiment analysis to the online content to extract sentiment information, identifying specific language patterns that are associated with one or more mental health disorders, and using machine learning algorithms to develop a model that predicts the likelihood of the individual having the mental health disorder based on the language patterns identified.

3. A method for diagnosing mental health disorders using sentiment analysis, comprising collecting online content related to an individual, applying sentiment analysis to the online content to extract sentiment information, identifying specific language patterns that are associated with one or more mental health disorders, and using machine learning algorithms to develop a model that predicts the likelihood of the individual having the mental health disorder based on the language patterns identified

Description:Field of the Invention

[0001] The present invention is related to computer science and machine learning field.

Background

[0002] Now a days, sentiment analysis is one of the popular fields, where the research is going on.
[0003] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0004] Sentiment analysis is a computational technique that involves the use of natural language processing (NLP) and machine learning algorithms to identify and extract subjective information from text. This technique has been applied in various domains, including mental health, to analyze social media posts, blogs, and other online content to identify patterns that may indicate the presence of mental health disorders

[0005]. Individuals who were depressed tended to use more negative language, expressed less positivity, and used more first-person singular pronouns compared to individuals who were not depressed. The researchers were able to develop a machine learning algorithm that achieved an accuracy of 70% in detecting depression in social media posts.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0007] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0008] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0009] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.
[0010] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

Objectives of the Invention

[0011] Early detection: One objective of using sentiment analysis for mental health disorder diagnoses is to detect the presence of mental health disorders at an early stage. By analyzing social media posts, blogs, and other online content, sentiment analysis can identify patterns of language use that may indicate the presence of a mental health disorder, allowing for early intervention and treatment.
[0012] Personalized treatment: Second objective of using sentiment analysis for mental health disorder diagnoses is to develop personalized treatment plans based on an individual's language use. Sentiment analysis can identify specific language patterns that are associated with different mental health disorders, allowing clinicians to tailor their treatment approach to the individual's unique needs.
[0013] Improved diagnosis accuracy: Sentiment analysis can help improve the accuracy of mental health disorder diagnoses by providing an objective, data-driven approach to identifying patterns of language use that are associated with different disorders. This can help reduce the risk of misdiagnosis and ensure that individuals receive the appropriate treatment.

Drawings

Figure 1

Brief Description of the Drawing

[0014] The figure 1 represents working model in the present invention with its prototype.

Detailed Description:

[0015] In figure 1, showing the input parameter; the social media, blog posts of user’s feed which is to be processed by the system 100.

[0016] The present invention takes the input and analyze the semantics of the user’s social media and blogs using various algorithms.
[0017] Semantic relevancy module, obtains word to language using the Concept Semantic analysis method based on correlation word and the semantic contribution degree of adopted label.
[0018] In the Concept Semantic analysis method based on correlation word, we are traditional word degree of correlation acquisition methods and word Position relationship has carried out effective fusion between word. Position between word and word is taken into full account and relative contribution between them, the relation of degree.
[0019] The processed data is taken into consideration for the machine learning algorithms to identify the abnormalities or the change in the words used by the user in chat room.
[0020] The deviation in the behavior of the user is figure out and the same is notified.
[0021] In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K- Nearest Neighbours, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering
– Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
[0022] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[0023] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-
exclusive manner, indicating that the referenced elements, components, or
steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
, Claims:We Claim:

1. A method for diagnosing mental health disorders using sentiment analysis, comprising collecting online content related to an individual, applying sentiment analysis to the online content to extract sentiment information, identifying specific language patterns that are associated with one or more mental health disorders, and using machine learning algorithms to develop a model that predicts the likelihood of the individual having the mental health disorder based on the language patterns identified.
2. A method for diagnosing mental health disorders using sentiment analysis, comprising collecting online content related to an individual, applying sentiment analysis to the online content to extract sentiment information, identifying specific language patterns that are associated with one or more mental health disorders, and using machine learning algorithms to develop a model that predicts the likelihood of the individual having the mental health disorder based on the language patterns identified.
3. A method for diagnosing mental health disorders using sentiment analysis, comprising collecting online content related to an individual, applying sentiment analysis to the online content to extract sentiment information, identifying specific language patterns that are associated with one or more mental health disorders, and using machine learning algorithms to develop a model that predicts the likelihood of the individual having the mental health disorder based on the language patterns identified