scholarly journals Multi-Lingual Sentiment Analysis of Social Data Based on Emotion-Bearing Patterns

Author(s):  
Carlos Argueta ◽  
Yi-Shin Chen
2020 ◽  
pp. 54-65 ◽  
Author(s):  
Kamaran H. Manguri ◽  
Rebaz N. Ramadhan ◽  
Pshko R. Mohammed Amin

In the past two decades, the growth of social data on the web has rapidly increased. This leads to researchers to access the data and information for many academic research and commercial uses. Social data on the web contains many real life events that occurred in daily life, today the global COVID-19 disease is spread worldwide. Many individuals including media organizations and government agencies are presenting the latest news and opinions regarding the coronavirus. In this study, the twitter data has been pulled out from Twitter social media, through python programming language, using Tweepy library, then by using TextBlob library in python, the sentiment analysis operation has been done. After the measuring sentiment analysis, the graphical representation has been provided on the data. The data we have collected on twitter are based on two specified hashtag keywords, which are (“COVID-19, coronavirus”). The date of searching data is seven days from 09-04-2020 to 15-04-2020. In the end a visualized presentation regarding the results and further explanation are provided.


Author(s):  
Sujata Patil ◽  
Bhavesh Wagh ◽  
Aditya Bhinge ◽  
Aakash Sahal ◽  
Prof. Madhav Ingale

Social media monitoring has been growing day by day so analyzing social data plays an important role in knowing people's behavior. So we are analyzing Social data such as Twitter Tweets using sentiment analysis which checks the opinion of people related to government schemes that are announced by the Central Government. This paper-based is on social media Twitter datasets of particular schemes and their polarity of sentiments. The popularity of the Internet has been rapidly increased. Sentiment analysis and opinion mining is the field of study that analyses people's opinions, sentiments, evaluations, attitudes, and emotions from written language. User-generated content is highly generated by users. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. It is difficult to analyze or summarize user-generated content. Most of the users write their opinions, thoughts on blogs, social media sites, E-commerce sites, etc. So these contents are very important for individuals, industry, government, and research work to make decisions. This Sentiment analysis and opinion mining research is a hot research area that comes under Natural Language processing. We plot and calculate numbers of positive, negative, and neutral tweets from each event.


2021 ◽  
pp. 194-203
Author(s):  
Tarek Elsaka ◽  
Imad Afyouni ◽  
Ibrahim Abaker Targio Hashem ◽  
Zaher AL-Aghbari

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