scholarly journals Hadoop Based Generic Template for Performing Sentiment Analysis Using Apache PIG

2021 ◽  
Vol 9 (VII) ◽  
pp. 3667-3774
Author(s):  
Gadige Vishal Sai
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Streaming Data ◽  
Social Media Platforms ◽  
Social Media Monitoring ◽  
Data Tracking ◽  
Media Monitoring ◽  
Apache Pig ◽  
The Given ◽  
Transactional Data

Every day over 2.5 quintillion data is generated using various channels like online surveys, transactional data tracking, social media monitoring, etc. Out of these majority of the data is generated using social media platforms. This raw data contains information that can be used for industrial, economic, social and business purposes. To facilitate this, sentiment analysis has become a prospect for various tech-based industry giants to review and analyze their products. Hadoop has been established as one of the best tools for storing, processing, and streaming data in the market. In this paper, we present a generic approach to performing sentiment analysis using Apache PIG which classifies the given data taken from a dataset to either positive or negative to get the people’s sentiment over an object or an issue.

scholarly journals The social life of COVID-19: Early insights from social media monitoring data collected in Poland

2020 ◽  
Vol 26 (4) ◽  
pp. 3056-3065
Author(s):  
Joanna Burzyńska ◽  
Anna Bartosiewicz ◽  
Magdalena Rękas
Keyword(s):  
Social Media ◽  
Social Life ◽  
Global Pandemic ◽  
The Social ◽  
Social Media Platforms ◽  
Social Media Monitoring ◽  
Media Monitoring ◽  
Novel Coronavirus ◽  
Online Conversation ◽  
Media Data

Research has revealed that social media data may be promising in many health threats and help to understand how people respond to them. As the outbreak of a novel coronavirus disease (COVID-19) is a global pandemic, a real-time social media monitoring is needed to know the scale of this phenomenon. We have reported the frequency, reach and impact of online mentions about the COVID-19 illness taken from social media platforms: Facebook, Instagram, Twitter, blogs, forums, and news portals to highlight and better understand the scope of coronavirus discussion in Poland. We used SentiOne social listening tool to gather the data and perform the monitoring between 24 February 2020 to 25 March 2020. We found a total of 1,415,750 mentions related to COVID-19 which gives the average 47,192 mentions per day. 95.36% (1,350,059) of mentions were people’s updates and expressions, 4.64% (65,691) mentions were articles from news portals and social media. Males have dominated the online conversation about COVID-19 (65.32% vs 34.68% females). At the same time, women were more likely to discuss the topic on social media platforms such as: Facebook, Twitter, and Instagram. We concluded with theoretical and practical implications.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

scholarly journals Importance of University Social Media Communications in Czech Republic

2021 ◽  
Vol 15 ◽  
pp. 67-73
Author(s):  
Radomila Soukalová ◽  
Jiří Ježek
Keyword(s):  
Social Media ◽  
Czech Republic ◽  
Effective Communication ◽  
Target Groups ◽  
The Czech Republic ◽  
Secondary Research ◽  
Media Communications ◽  
Social Media Monitoring ◽  
Media Monitoring

This article currently focuses on the problems of university communications with target groups in the Czech Republic. This issue has been chosen with respect to the ongoing demographic crisis causing a decrease in the number of prospective university applicants. The topic reflects new trends in effective communication of university, i.e. social media communications and concentrates on prospective university applicants. The presented study introduces the results of sub-analyses carried out within selected Czech universities. The sub-analyses dealt with the problems of university social media profiles and their conceptuality, approach of individual universities towards profile administration and the importance of involving fans into communications on Czech university profiles. The necessary data have been gained using both primary and secondary research as well as with help of social media monitoring by Newton Media. The study concludes with the identification of common and different attributes of Czech universities´ social media communications and with suggestions as to how to make these communications more effective.

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