Description:Title:
Sentiment analysis by attention feeder using multivariate functions

Field of the Invention

[0001] The present invention is related to the sentiment analysis in 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] In patent US10298700B2, a method and system for on-line monitoring of electronic communications is discussed which include automatically monitoring text-based communications of one or more chat room or instant messaging participants to determine if a monitoring event has occurred. A monitoring event can be, for example, the combined existence of certain defined factors associated with a particular exchange relating to, for example, the exchange's participants, content, recent history, and/or tone.

[0005] In patent CN103838833B, the invention belongs to information retrieval technique, a kind of text retrieval system based on correlation word semantic analysis is provided, including Query Information receiver module, based on the Concept Semantic analysis module of correlation word, semantic knowledge library module, retrieval module, index database, index module, based on the theme semantic module of correlation word, result set processing module and data server.
[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] Improve the accuracy of sentiment analysis: By using multivariate functions and attention feeder, sentiment analysis models can be trained to identify multiple aspects of sentiment in a text. This improves the accuracy of sentiment analysis, as it takes into account the context and nuances of language.
[0012] Extract deeper insights: By using attention feeder, the sentiment analysis model can identify the most important parts of a text that contribute to the sentiment. This can help extract deeper insights from the text, such as identifying the specific reasons behind a positive or negative sentiment.
[0013] Handle complex texts: Sentiment analysis using attention feeder can handle complex texts, such as those with sarcasm or irony, by analyzing the entire text and not just individual words. This can lead to more accurate sentiment analysis results, even for difficult texts.
[0014] Improve customer experience: By accurately analyzing the sentiment of customer feedback and reviews, businesses can take action to improve their products and services, leading to a better customer experience. Sentiment analysis using attention feeder can help identify areas for improvement and provide valuable insights for businesses.

 
Drawings

Figure 1

Brief Description of the Drawing

[0015] The figure 1 represents working model in the present invention with its prototype.
 
Detailed Description:

[0016] In figure 1, showing the input parameter; input word embedding which is to be processed by the system 100.

[0017] The present invention takes the input and analyze the semantics of the different word sentences.
[0018] 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.
[0019] 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.
[0020] The processed data is taken into consideration for the machine learning algorithms to identify the abnormalities or the change in the words used by processed data.
[0021] The extracted sentiment is mapped with associated mentioned outcome.
[0022] 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.
[0023] 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.
[0024] 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:1.Improved accuracy: Sentiment analysis by attention feeder using multivariate functions can achieve higher accuracy rates than traditional sentiment analysis models. By using attention mechanisms to focus on important parts of the text and analyzing multiple aspects of sentiment, this approach can better capture the complexities of language.

2.Better performance on complex texts: Traditional sentiment analysis models often struggle with complex texts that include sarcasm, irony, or other forms of nuanced language. However, Sentiment analysis by attention feeder using multivariate functions can better handle these texts, resulting in more accurate sentiment analysis results.

3.Deep insights: By analyzing multiple aspects of sentiment, such as polarity, intensity, and subjectivity, Sentiment analysis by attention feeder using multivariate functions can provide deep insights into the sentiment of a text. This can help businesses and organizations make better decisions based on customer feedback and other types of sentiment analysis.

4.Versatile: Sentiment analysis by attention feeder using multivariate functions can be applied to a wide range of texts, including social media posts, reviews, and customer feedback. This approach can provide valuable insights across a variety of industries and contexts, making it a versatile tool for sentiment analysis.