AU2021106074A4 - Social networking mood recognition algorithm for conflict detection and management of educational institutions - Google Patents
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Abstract
A system and a method to recognize mood of a user for conflict
management, comprises of: a data collection module (102) for
collecting a primary data and a secondary data; a mood recognition
module (104) for identifying mood of the user based on the primary
data and the secondary data, wherein sentences from the primary
data and the secondary data are divided into a plurality of words or
tokens and is classified using a classifier; a conflict detection module
(106) for continuous real time monitoring of the mood of the user to
detect situation of a conflict by analyzing the plurality of words
syntactically; and an API module (108) for performing various stage
such as data collection, parsing, filtering or aggregation, wherein
storing output obtained from the various stage in a data store and
handling and processing query of the data store using the HTTP.
108
102
CONFLICT DETECTION
DATA COLLECTION MODULE MODULE
104 110
APPLICATION
MOOD RECOGNITION
MODUL PROGRAMMING INTERFACE MMODUL
FIGURE 1
collecting a primary data and a secondary data using a data collection module (102), wherein
conducting interviews to ensure expert surveys for collecting data using a Delphi method
202
identifying mood of the user based on the primary data and the secondary data using a mood
recognition module (104) associated with the data collection module (102), wherein sentences from 204
the primary data and the secondary data are divided into a plurality of words or tokens and is
classified using a classifier to identify the mood of the user;
continuous real time monitoring of the mood of the user using a conflict detection module (106)
associated to the mood recognition module (104) to detect situation of a conflict by analyzing the
plurality of words syntactically, wherein maps of different mood or emotions are created 1206
performing various stage such as parsing, filtering or aggregation using an application programming
interface (API) module (108) associated with the conflict detection module (106), wherein storing
output obtained from the various stage in a data store and handling and processing query of the data
store using a Hyper Text Transfer Protocol (HTTP).
FIGURE 2
Description
102 CONFLICT DETECTION DATA COLLECTION MODULE MODULE
104 110
FIGURE 1
collecting a primary data and a secondary data using a data collection module (102), wherein conducting interviews to ensure expert surveys for collecting data using a Delphi method 202 identifying mood of the user based on the primary data and the secondary data using a mood recognition module (104) associated with the data collection module (102), wherein sentences from 204 the primary data and the secondary data are divided into a plurality of words or tokens and is classified using a classifier to identify the mood of the user;
continuous real time monitoring of the mood of the user using a conflict detection module (106) associated to the mood recognition module (104) to detect situation of a conflict by analyzing the plurality of words syntactically, wherein maps of different mood or emotions are created 1206
performing various stage such as parsing, filtering or aggregation using an application programming interface (API) module (108) associated with the conflict detection module (106), wherein storing output obtained from the various stage in a data store and handling and processing query of the data store using a Hyper Text Transfer Protocol (HTTP).
FIGURE 2
The present invention generally relates to the field of information and communication technology. More particularly, the present invention relates to a field of mood recognition algorithm for conflict detection using information and communication technology.
In today's world of Internet, social media is playing an essential role in the quick passage of information and planning among the teachers as well as the student fraternity. Social networking platforms like Facebook, Twitter, WhatsApp, Instagram, etc. are identified to be playing a major role in instilling such mood shifts. The problem is getting aggravated because of the increased misuse of social media. than its positive use during such situations that are creating an abnormal atmosphere in an educational system.
It has been seen that the events surrounding the social, economic, cultural and political aspects do have a significant impact on the stakeholders of the institution. It creates an immediate effect on the different dimensions of the sentiments and moods of the people.
Mood detection and text recognition are a recent research area that is closely related to sentiment analysis. Mood detection mainly aims to detect and recognize various types of feelings from the texts' expressions like angry, love, worry, neutral, happiness, sadness and surprise while sentiment analysis aims at detecting positive, negative or neutral feelings from the given text.
Nowadays, social networking is found to be the handiest source and easiest means to monitor the emotions of the users, i.e., the population desirable for the research study. The problem of the alarmingly high rate of conflicts, clashes, strikes and agitations has to be prevented and managed primarily to maintain peace and harmony in the educational environment.
Therefore, there exists a need to develop a system and a method for creating conflict management strategies and dispute resolution plans accordingly to maintain peace and harmony in the educational environment and ensuring control in the educational environment with the help of early-stage conflict resolution and effective learning development should be the primary focus, and therefore, the educational institutions in India should formulate a framework to continuously moderate and monitor the usage of social networking platforms among the stakeholders.
The technical advancements disclosed by the present invention overcomes the limitations and disadvantages of existing and convention systems and methods.
The present invention generally relates toa system and a method for mood detection and conflict management.
An object of the present invention is to provide effectiveness of social media networks (especially Twitter) on the lines of conflict resolution and management.
Another object of the present invention is to provide unique situations
of clashes, strikes, agitations, disputes and conflicts with the help of information technology.
Another object of the present invention is to find emotions such as anger, happiness, sadness, surprise, love, worry and neutral for performing the mood recognition process.
According to an aspect of the present invention, a system to recognize mood of a user for conflict management, wherein the system comprises of: a data collection module for collecting a primary data and a secondary data, wherein conducting interviews to ensure expert surveys for collecting data using a Delphi method; a mood recognition module associated with the data collection module for identifying mood of the user based on the primary data and the secondary data, wherein sentences from the primary data and the secondary data are divided into a plurality of words or tokens and is classified using a classifier to identify the mood of the user; a conflict detection module associated to the mood recognition module for continuous real time monitoring of the mood of the user to detect situation of a conflict by analyzing the plurality of words syntactically, wherein maps of different mood or emotions are created; and an application programming interface (API) module associated with the conflict detection module for performing various stage such as data collection, parsing, filtering or aggregation, wherein storing output obtained from the various stage in a data store and handling and processing query of the data store using a Hyper Text Transfer Protocol (HTTP).
According to an aspect of the present invention, a method to recognize mood of a user for conflict management, wherein the method comprises of: collecting a primary data and a secondary data using a data collection module, wherein conducting interviews to ensure expert surveys for collecting data using a Delphi method; identifying mood of the user based on the primary data and the secondary data using a mood recognition module associated with the data collection module, wherein sentences from the primary data and the secondary data are divided into a plurality of words or tokens and is classified using a classifier to identify the mood of the user; continuous real time monitoring of the mood of the user using a conflict detection module associated to the mood recognition module to detect situation of a conflict by analyzing the plurality of words syntactically, wherein maps of different mood or emotions are created; and performing various stage such as data collection, parsing, filtering or aggregation using an application programming interface (API) module associated with the conflict detection module, wherein storing output obtained from the various stage in a data store and handling and processing query of the data store using a Hyper Text Transfer Protocol (HTTP).
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates a block diagram for a system recognize mood of a user for conflict management,
Figure 2illustrates a flow diagram of a method to recognize mood of a user for conflict management,
Figure 3illustrates a flow chart of a post tagger, and
Figure 4 illustrates a graphical representation of matched and unmatched Tweets for emotions in mood recognition module at the preliminary testing phase.
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have been necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
Embodiments of the present invention will be described below in detail with reference to the accompanying drawings.
Figure 1 illustrates a block diagram for a system recognize mood of a user for conflict management, wherein the system comprises of: a data collection module (102), a mood recognition module (104), a conflict detection module (106), and an application programming interface (API) module (108).
The data collection module (102) for collecting a primary data and a secondary data, wherein conducting interviews to ensure expert surveys for collecting data using a Delphi method. The primary data is collected from a social media platform such as twitter, and the secondary data is collected from other social media platforms, research and news article.
The mood recognition module (104)is associated with the data collection module (102) for identifying mood of the user based on the primary data and the secondary data, wherein sentences from the primary data and the secondary data are divided into a plurality of words or tokens and is classified using a classifier to identify the mood of the user. The mood of the user is a set of emotion such as anger, happiness, sadness, surprise, love, worry and neutral. A Stanford parser divides the sentences into the plurality of words, wherein a lexical analyzer creates token for each of the plurality of words. The classifier is a modified NaYve Bayes Classification algorithm for classifying the plurality of words or tokens to detect the mood of the user.
The conflict detection module (106)is associated to the mood recognition module (104) for continuous real time monitoring of the mood of the user to detect situation of a conflict by analyzing the plurality of words syntactically, wherein maps of different mood or emotions are created. The situation of conflict includes unique situations of clashes, strikes, agitations, disputes, etc.
The application programming interface (API) module (108)is associated with the conflict detection module (106) for performing various stage such as data collection, parsing, filtering or aggregation, wherein storing output obtained from the various stage in a data store and handling and processing query of the data store using a Hyper Text Transfer Protocol (HTTP).
Figure 2 illustrates a flow diagram of a method to recognize mood of a user for conflict management, wherein the method comprises of:
Step (202) discloses about collecting a primary data and a secondary data using a data collection module (102), wherein conducting interviews to ensure expert surveys for collecting data using a Delphi method.
Step (204) discloses about identifying mood of the user based on the primary data and the secondary data using a mood recognition module (104) associated with the data collection module (102), wherein sentences from the primary data and the secondary data are divided into a plurality of words or tokens and is classified using a classifier to identify the mood of the user.
Step (206) discloses about continuous real time monitoring of the mood of the user using a conflict detection module (106) associated to the mood recognition module (104) to detect situation of a conflict by analyzing the plurality of words syntactically, wherein maps of different mood or emotions are created.
Step (208) discloses about performing various stage such as parsing, filtering or aggregation using an application programming interface (API) module (108) associated with the conflict detection module (106), wherein storing output obtained from the various stage in a data store and handling and processing query of the data store using a Hyper Text Transfer Protocol (HTTP).
Figure 3 illustrates a flow chart of a post tagger.
The primary data are collected from Twitter API (108) and the data comprised of real-time Tweets. The secondary data are collected from social media, research and news articles. Some interviews are also conducted to ensure expert surveys with the help of the Delphi method. The analysis is done to perform mood recognition.
The Stanford parser for dividing the sentences extracted from the tweets. After that, the data are trained to make it ready for the Naive Bayes classification algorithm. The Nave Bayes classifier has been used to attain better results despite trained data being limited. Naive Bayes classifier is found to be an excellent classifier for text classification.
The data are extracted by the Twitter Streaming process with the help of API (108) and libraries. Negation handling is also taken into account. The mood recognition of the users who tweeted regarding the recommended keywords is done. The keywords are related to the Indian educational institutions based on the frequency of clashes, agitations or conflicts.
Firstly, the work is done by the parser. This segment is basically used to divide each sentence which is extracted from input from the user, dataset or the real-time streaming of tweets from twitter into multiple words and differentiating noun, verbs, adjectives and adverbs, i.e., keyword extraction. The parser is preceded by a separate lexical analyzer, which creates tokens for the words. After tokenization, the extracted words are analyzed syntactically for creating a parse tree. The parse tree is basically a rooted tree that normally represents the string's syntactic structure according to some given context-free grammar. Thus, parsing the sentence would convert the sentence into a tree whose leaves hold POS (part of speech) tags, but the rest of the tree tells us how exactly words are joining together to make the overall sentence. Thus, each word is provided with the part of speech tag and then noun, verb, adverb and adjective with the help of Stanford POS tagger and is thus filtered from the output.
Extracted tags-NN, NNS, NNP, NNPS, JJ,JJS, JJR, RB, RBS, VB, VBD, VBG, VBN, VBP, VBZ,CC.
After extracting keywords from the dataset, maps of different moods or emotions have been created along with the finding of vocabulary size where dataset comprises of about 40,000 tweets marked along with the emotions. However, the number of tweets vary according to other embodiments. The tweets are trained and increased accordingly for better results gradually.
The training set is created in such a way that it handles the negation, and hence, the machine is ready for Nave Bayes classification. The Twitter Streaming process is done with the help of Application Programe Interfaces. The Streaming APIs (108) allow developers to have low latency access to Twitter's global stream of Tweet data. The basic task of the API (108) is to act as a mediator between Twitter and analyzer. The API (108) gives the stream data inJSON (JavaScript Object Notation) format. JSON is used because it is easier, faster to use and similar to XML.
Firstly, the streaming process extracts input Tweets. Then, it performs various stages which are parsing, filtering or aggregation. These steps are needed before storing the result in data storage. The HTTP (Hyper Text Transfer Protocol) handling then processes queries to the data store for outcomes in response to users' requests. When the user clicks on the "Live Streaming" button on the mood detection analyzer, instantly live streaming is performed, and simultaneously, its mood is detected. The mood recognition is done on the basis of the tweets extracted within the given range or threshold value along with timestamps. Naive Bayes is a probabilistic algorithm that acts as the foundation for application and implementation of Bayes' theorem with Naive independence assumptions between the features.
Figure 4 illustrates a graphical representation of matched and unmatched Tweets for emotions in mood recognition module (104) at the preliminary testing phase.
The emotions are represented as X-axis (horizontal) with frequency represented as Y-axis (vertical). This graph shows the preliminary accuracy level of the research study. Gradually, the accuracy is observed to upsurge with more trained data. The comparison analysis of different algorithms justified that the Nave Bayes Classifier with logarithm and negation handling exhibited better accuracy than other algorithms along with continuous improvement in the results.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.
Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
Claims (7)
1. A system to recognize mood of a user for conflict management, wherein the system comprises of:
a data collection module (102) for collecting a primary data and a secondary data, wherein collecting Twitter data along with conducting interviews to ensure expert surveys for collecting data using a Delphi method;
a mood recognition module (104) associated with the data collection module (102) for identifying mood of the user based on the primary data and the secondary data, wherein sentences from the primary data and the secondary data are divided into a plurality of words or tokens and is classified using a classifier to identify the mood of the user;
a conflict detection module (106) associated to the mood recognition module (104) for continuous real time monitoring of the mood of the user to detect situation of a conflict by analyzing the plurality of words syntactically, wherein maps of different mood or emotions are created; and
an application programming interface (API) module (108)associated with the conflict detection module (106) for collecting data, performing various stage such as parsing, filtering or aggregation, wherein storing output obtained from the various stage in a data store and handling and processing query of the data store using a Hyper Text Transfer Protocol (HTTP).
2. The system as claimed in claim 1, wherein the primary data is collected from a social media platform such as twitter, and the secondary data is collected from other social media platforms, research and news article.
3. The system as claimed in claim 1, wherein the mood of the user is a set of emotion such as anger, happiness, sadness, surprise, love, worry and neutral.
4. The system as claimed in claim 1, wherein a Stanford parser divides the sentences into the plurality of words, wherein a lexical analyzer creates token for each of the plurality of words.
5. The system as claimed in claim 1, wherein the classifier is a Nave Bayes Classification algorithm for classifying the plurality of words or tokens to detect the mood of the user.
6. The system as claimed in claim 1, wherein the situation of conflict includes unique situations of clashes, strikes, agitations, disputes, etc.
7. A method to recognize mood of a user for conflict management, wherein the method comprises of:
collecting a primary data and a secondary data using a data collection module (102), wherein collecting Twitter data along with conducting interviews to ensure expert surveys for collecting data using a Delphi method;
identifying mood of the user based on the primary data and the secondary data using a mood recognition module (104) associated with the data collection module (102), wherein sentences from the primary data and the secondary data are divided into a plurality of words or tokens and is classified using a classifier to identify the mood of the user;
continuous real time monitoring of the mood of the user using a conflict detection module (106) associated to the mood recognition module (104) to detect situation of a conflict by analyzing the plurality of words syntactically, wherein maps of different mood or emotions are created; and performing various stage such as data collection, parsing, filtering or aggregation using an application programming interface (API) module (108) associated with the conflict detection module (106), wherein storing output obtained from the various stage in a data store and handling and processing query of the data store using a Hyper Text Transfer Protocol (HTTP).
FIGURE 1
FIGURE 2
FIG GURE 3
GURE 4 FIG
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