Extracting Events from Social Media Using NLP

2017 ◽  
pp. 43-48
Author(s):  
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Natural Language Processing ◽  
Language Processing ◽  
Scattered Data ◽  
Huge Data ◽  
Time Period ◽  
Event Category ◽  
The Social ◽  
Extract Information

Now a day’s Social media is major channel of communication between individuals and organizations. Huge data is available over the social networks, so it is important and essential to analyze this data to extract information. The data on social media is very much scattered, to extract an information it needs to be organized. Natural Language Processing (NLP) techniques are used to analyze the scattered data to fetch information for targeted entities (Event, Category, Date, Place, and Time period). The extracted information it is listed on a database and can be used in several ways. In this paper, a model is proposed which categorize event by their types, Date Place and Time. The results show this model can categorize the 90% events.

scholarly journals Automatic morphological analysis on the material of Russian social media texts

10.29007/dlff ◽  
2019 ◽  
Author(s):  
Alena Fenogenova ◽  
Viktor Kazorin ◽  
Ilia Karpov ◽  
Tatyana Krylova
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Morphological Analysis ◽  
State Of The Art ◽  
Test Set ◽  
The Social ◽  
Media Texts

Automatic morphological analysis is one of the fundamental and significant tasks of NLP (Natural Language Processing). Due to special features of Internet texts, as they can be both normative texts (news, fiction, nonfiction) and less formal texts (such as blogs and texts from social networks), the morphological tagging has become non-trivial and an actual task. In this paper we describe our experiments in tagging of Internet texts presenting our approach based on deep learning. The new social media test set was created, that allows to compare our system with state-of-the-art open source analyzers on the social media texts material.

scholarly journals Twitter Sentiment Analysis Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 296-302
Author(s):  
Pushkar Dubey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Approach ◽  
Analysis Model ◽  
Market Place ◽  
The Social

Social networks are the main resources to gather information about people’s opinion towards different topics as they spend hours daily on social media and share their opinion. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. .We use natural language processing and machine learning concepts to create a model for analysis . In this paper we are discussing how we can create a model for analysis of twittes which is trained by various nlp , machine learning and Deep learning Approach.

scholarly journals Uncovering Cybercrimes in Social Media through Natural Language Processing

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Julián Ramírez Sánchez ◽  
Alejandra Campo-Archbold ◽  
Andrés Zapata Rozo ◽  
Daniel Díaz-López ◽  
Javier Pastor-Galindo ◽  
...  
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Media ◽  
Natural Language Processing ◽  
Law Enforcement ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Law Enforcement Agencies ◽  
Illegal Activities

Among the myriad of applications of natural language processing (NLP), assisting law enforcement agencies (LEA) in detecting and preventing cybercrimes is one of the most recent and promising ones. The promotion of violence or hate by digital means is considered a cybercrime as it leverages the cyberspace to support illegal activities in the real world. The paper at hand proposes a solution that uses neural network (NN) based NLP to monitor suspicious activities in social networks allowing us to identify and prevent related cybercrimes. An LEA can find similar posts grouped in clusters, then determine their level of polarity, and identify a subset of user accounts that promote violent activities to be reviewed extensively as part of an effort to prevent crimes and specifically hostile social manipulation (HSM). Different experiments were also conducted to prove the feasibility of the proposal.

scholarly journals Low Testosterone on Social Media: Application of Natural Language Processing to Understand Patients’ Perceptions of Hypogonadism and Its Treatment

10.2196/21383 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e21383
Author(s):  
Vadim Osadchiy ◽  
Tommy Jiang ◽  
Jesse Nelson Mills ◽  
Sriram Venkata Eleswarapu
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Principal Component ◽  
Online Discussions ◽  
Testosterone Replacement ◽  
Testosterone Replacement Therapy ◽  
The Social ◽  
Low Testosterone

Background Despite the results of the Testosterone Trials, physicians remain uncomfortable treating men with hypogonadism. Discouraged, men increasingly turn to social media to discuss medical concerns. Objective The goal of the research was to apply natural language processing (NLP) techniques to social media posts for identification of themes of discussion regarding low testosterone and testosterone replacement therapy (TRT) in order to inform how physicians may better evaluate and counsel patients. Methods We retrospectively extracted posts from the Reddit community r/Testosterone from December 2015 through May 2019. We applied an NLP technique called the meaning extraction method with principal component analysis (MEM/PCA) to computationally derive discussion themes. We then performed a prospective analysis of Twitter data (tweets) that contained the terms low testosterone, low T, and testosterone replacement from June through September 2019. Results A total of 199,335 Reddit posts and 6659 tweets were analyzed. MEM/PCA revealed dominant themes of discussion: symptoms of hypogonadism, seeing a doctor, results of laboratory tests, derogatory comments and insults, TRT medications, and cardiovascular risk. More than 25% of Reddit posts contained the term doctor, and more than 5% urologist. Conclusions This study represents the first NLP evaluation of the social media landscape surrounding hypogonadism and TRT. Although physicians traditionally limit their practices to within their clinic walls, the ubiquity of social media demands that physicians understand what patients discuss online. Physicians may do well to bring up online discussions during clinic consultations for low testosterone to pull back the curtain and dispel myths.

scholarly journals Low Testosterone on Social Media: Application of Natural Language Processing to Understand Patients’ Perceptions of Hypogonadism and Its Treatment (Preprint)

2020 ◽  
Author(s):  
Vadim Osadchiy ◽  
Tommy Jiang ◽  
Jesse Nelson Mills ◽  
Sriram Venkata Eleswarapu
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Principal Component ◽  
Online Discussions ◽  
Testosterone Replacement ◽  
Testosterone Replacement Therapy ◽  
The Social ◽  
Low Testosterone

BACKGROUND Despite the results of the Testosterone Trials, physicians remain uncomfortable treating men with hypogonadism. Discouraged, men increasingly turn to social media to discuss medical concerns. OBJECTIVE The goal of the research was to apply natural language processing (NLP) techniques to social media posts for identification of themes of discussion regarding low testosterone and testosterone replacement therapy (TRT) in order to inform how physicians may better evaluate and counsel patients. METHODS We retrospectively extracted posts from the Reddit community r/Testosterone from December 2015 through May 2019. We applied an NLP technique called the meaning extraction method with principal component analysis (MEM/PCA) to computationally derive discussion themes. We then performed a prospective analysis of Twitter data (tweets) that contained the terms low testosterone, low T, and testosterone replacement from June through September 2019. RESULTS A total of 199,335 Reddit posts and 6659 tweets were analyzed. MEM/PCA revealed dominant themes of discussion: symptoms of hypogonadism, seeing a doctor, results of laboratory tests, derogatory comments and insults, TRT medications, and cardiovascular risk. More than 25% of Reddit posts contained the term doctor, and more than 5% urologist. CONCLUSIONS This study represents the first NLP evaluation of the social media landscape surrounding hypogonadism and TRT. Although physicians traditionally limit their practices to within their clinic walls, the ubiquity of social media demands that physicians understand what patients discuss online. Physicians may do well to bring up online discussions during clinic consultations for low testosterone to pull back the curtain and dispel myths.

scholarly journals Natural language processing for analysis of student online sentiment in a postgraduate program

2020 ◽  
Vol 2 (2) ◽  
pp. 15-30
Author(s):  
Truc D Pham ◽  
Darcy Vo ◽  
Frank Li ◽  
Karen Baker ◽  
Binglan Han ◽  
...  
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Experience ◽  
Research Question ◽  
The Social ◽  
Natural Language Processing Tool ◽  
Course Experience

Higher education institutes are continually looking for new and better ways to support and understand the learning experience of their students. One possible option is to use sentiment analysis tools to investigate the attitudes and emotions of students when they are interacting on social media about their course experience. In this study, we analysed the social media posts, from a closed programme-based community, of more than 300 students in a single programme cohort by processing the dataset with the Google cloud-based Natural Language Processing API for sentiment analysis. The sentiment scores and magnitudes were then visualised to help explore the research question ‘How does a natural language processing tool help analyse student online sentiment in a postgraduate program?’ The results have provided a better understanding of students’ online sentiment relating to the activities and assessments of the programme as well as the variation of that sentiment over the timeline of the programme.

scholarly journals Identificação de Perfis Depressivos em Redes Sociais Utilizando Aprendizado de Máquina: um Mapeamento Sistemático

2020 ◽  
Author(s):  
Vinicius Casani ◽  
Rafael Gomes Mantovani ◽  
Alinne Cristinne Correa Souza ◽  
Francisco Carlos Monteiro Souza
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Natural Language Processing ◽  
Social Class ◽  
Natural Language ◽  
Language Processing ◽  
Psychological Disorder ◽  
The Social

Depression is a psychological disorder that affects millions of peoplein theworld, regardless of their age, social class or nationality. In theliterature, different techniques have been studying to analyze andrecognize this disease such as Natural Language Processing, SentimentAnalysis, and Machine Learning. In this paper, we describe asystematic mapping to identify evidence regarding techniques thatare often used to identify depressive profiles.We analyzed 472 studiesand we selected 25 primary studies. These studies indicate thatthe SVM and NB techniques have been most used to detect possibledepressive profiles in social networks. Furthermore, Twitter andFacebook with 35,5% and 22,6%, respectively were the social mediamost have been used by users’ express their feelings regarding themost varied subjects.

scholarly journals Intelligent Estimation of Social Media Sentimental Features using Deep Learning with Natural Language Processing Strategies

2021 ◽  
Vol 10 (6) ◽  
pp. 74-79
Author(s):  
Sarojini Yarramsetti ◽  
Anvar Shathik J ◽  
Renisha. P.S.
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Social Network ◽  
Natural Language ◽  
Language Processing ◽  
Digital World ◽  
The Social ◽  
Learning Principles ◽  
The Voice

In this digital world, experience sharing, knowledge exploration, taught posting and other related social exploitations are common to every individual as well as social media/network such as FaceBook, Twitter, etc plays a vital role in such kinds of activities. In general, many social network based sentimental feature extraction details and logics are available as well as many researchers work on that domain for last few years. But all those research specification are narrowed in the sense of building a way for estimating the opinions and sentiments with respect to the tweets and posts the user raised on the social network or any other related web interfacing medium. Many social network schemes provides an ability to the users to push the voice tweets and voice messages, so that the voice messages may contain some harmful as well as normal and important contents. In this paper, a new methodology is designed called Intensive Deep Learning based Voice Estimation Principle (IDLVEP), in which it is used to identify the voice message content and extract the features based on the Natural Language Processing (NLP) logic. The association of such Deep Learning and Natural Language Processing provides an efficient approach to build the powerful data processing model to identify the sentimental features from the social networking medium. This hybrid logic provides support for both text based and voice based tweet sentimental feature estimations. The Natural Language Processing principles assists the proposed approach of IDLVEP to extracts the voice content from the input message and provides a raw text content, based on that the deep learning principles classify the messages with respect to the estimation of harmful or normal tweets. The tweets raised by the user are initially sub-divided into two categories such as voice tweets and text tweets. The voice tweets will be taken care by the NLP principles and the text enabled tweets will be handled by means of deep learning principles, in which the voice tweets are also extracted and taken care by the deep learning principle only. The social network has two different faces such as provides support to developments as well as the same it provides a way to access that for harmful things. So, that this approach of IDLVEP identifies the harmful contents from the user tweets and remove that in an intelligent manner by using the proposed approach classification strategies. This paper concentrates on identifying the sentimental features from the user tweets and provides the harm free social network environment to the society.

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

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