scholarly journals Improving Accuracy of Sentiment Analysis for Depression Recommendation using Multi-Domain Fuzzy Rules

2020 ◽  
Vol 9 (4) ◽  
pp. 2434-2438
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Depression Score ◽  
Fuzzy Decision ◽  
History Of ◽  
Depression Level ◽  
Election Results ◽  
Improving Accuracy ◽  
Depression History

Social media & e-commerce has opened up the doors for human behavioral analysis in ways which were not possible before. Companies have the ability to track user’s mood and suggest advertisements which can trigger buying decisions based on it. This is possible due to user’s real time social media updates. Users nowadays are willing to provide information like their location, their age, nearby friends information, their mood, their buying patterns, etc. Companies do not intentionally collect all this information, but it has become a matter of social pride to post it as social media status and updates. The information available can be put to use in multiple forms- predict election results, movie success, product liking/disliking, travel destination recommendation, health care, etc. In our work, we utilize this textual information posted by different users and analyze their depression level focusing on negative sentiments. In order to perform this task, we have considered user’s tweets, any links which they might have posted, the time of the tweet, their age group and any previous depression history of the user. All these parameters are given to a novel fuzzy decision tree that uses sentiment analysis and game theory-based scoring in order to evaluate the depression score for the user. We analyzed the system on different real-time users, and observed that the system predicts depression level with more than 90% accuracy. Our work can be used to generate a prototype to identify if a person is in a depressive state and figure out the intensity of his/her depression.

scholarly journals Emotions in Social Media: An Analysis of Tweet Responses to MH370 Search Suspension Announcement

2019 ◽  
Vol 57 (2) ◽  
pp. 194-211 ◽  
Author(s):  
Su Lin Yeo ◽  
Augustine Pang ◽  
Michelle Cheong ◽  
Jerome Quincy Yeo
Keyword(s):  
Social Media ◽  
Real Time ◽  
Crisis Communication ◽  
Mapping Model ◽  
Media Attention ◽  
Crisis Mapping ◽  
History Of ◽  
Social Listening

Considered one of the deadliest incidents in the history of aviation crises and labelled a “continuing mystery,” the ongoing search for the missing Malaysia Airlines Flight 370 offers no closure. With endless media attention, and negative reactions of stakeholders to every decision made by the airline, this study investigates the types of emotions found in social media posted by publics to the MH370 search suspension announcement. It content analyzed 5,062 real-time tweet messages guided by the revised integrated crisis mapping model. Our findings indicated that, in addition to the four original emotions posited, there was a fifth emotion because of the long-drawn crisis and only two dominant emotions were similar to the model. A redrawn version to better encapsulate all the emotions is offered for one quadrant in the model. Implications for both crisis communication scholarship and the importance of social listening for organizations are discussed.

scholarly journals AthPPA: A Data Visualization Tool for Identifying Political Popularity over Twitter

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 312
Author(s):  
Alexandros Britzolakis ◽  
Haridimos Kondylakis ◽  
Nikolaos Papadakis
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Data Visualization ◽  
Visualization Tool ◽  
Political Leaders ◽  
Political Event ◽  
Data Analyst ◽  
Industrial Sectors ◽  
Social Media Platforms

Sentiment Analysis is an actively growing field with demand in both scientific and industrial sectors. Political sentiment analysis is used when a data analyst wants to determine the opinion of different users on social media platforms regarding a politician or a political event. This paper presents Athena Political Popularity Analysis (AthPPA), a tool for identifying political popularity over Twitter. AthPPA is able to collect in-real-time tweets and for each tweet to extract metadata such as number of likes, retweets per tweet etc. Then it processes their text in order to calculate their overall sentiment. For the calculation of sentiment analysis, we have implemented a sentiment analyzer that is able to identify the grammatical issues of a sentence as well as a lexicon of negative and positive words designed specifically for political sentiment analysis. An analytic engine processes the collected data and provides different visualizations that provide additional insights on the collected data. We show how we applied our framework to the three most prominent Greek political leaders in Greece and present our findings there.

scholarly journals Down with the #Dogefather: Evidence of a Cryptocurrency Responding in Real Time to a Crypto-Tastemaker

2021 ◽  
Vol 16 (6) ◽  
pp. 2230-2240
Author(s):  
Michael Cary
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Media Usage ◽  
Saturday Night ◽  
Negative Perceptions ◽  
Saturday Night Live

Recent research in cryptocurrencies has considered the effects of the behavior of individuals on the price of cryptocurrencies through actions such as social media usage. However, some celebrities have gone as far as affixing their celebrity to a specific cryptocurrency, becoming a crypto-tastemaker. One such example occurred in April 2021 when Elon Musk claimed via Twitter that “SpaceX is going to put a literal Dogecoin on the literal moon”. He later called himself the “Dogefather” as he announced that he would be hosting Saturday Night Live (SNL) on 8 May 2021. By performing sentiment analysis on relevant tweets during the time he was hosting SNL, evidence is found that negative perceptions of Musk’s performance led to a decline in the price of Dogecoin, which dropped 23.4% during the time Musk was on air. This shows that cryptocurrencies are affected in real time by the behaviors of crypto-tastemakers.

scholarly journals Role of Social Sentiment Analysis in Stock Trends Forecasting

2019 ◽  
Vol 8 (2S5) ◽  
pp. 144-147
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Product Reviews ◽  
Stock Market Prediction ◽  
The People ◽  
Market Trends ◽  
Election Results ◽  
Analysis Of Social Networks

Social media like Face book, Twitter have attracted attention from various sectors of study in recent years. Most of the people share ideas, opinions on various topics such as Stock Market Prediction, Digital marketing, Movie review, Election Results Prediction and Product reviews etc,. Forecasting Financial Market is considered to be one of the significant applications of Sentiment Analysis on Social Data like Face book, Twitter. It is essential to accurately predict the movements in stock trends, as the stock market trends are volatile. In the past few years several researches have been carried out for predicting the future trends of stock market through sentiment analysis on social media comments. This paper gives the survey on the various techniques, tools and methodologies adopted by several researchers on Stock Market Prediction based on sentiment analysis of Social networks

scholarly journals Extraction Opinion of Social Media in Higher Education Using Sentiment Analysis

bit-Tech ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Thomas Edison Tarigan ◽  
Robby C Buwono ◽  
Sri Redjeki
Keyword(s):  
Higher Education ◽  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Naive Bayes ◽  
Training Data ◽  
Test Results ◽  
Bayes Classifier ◽  
Tertiary Institution ◽  
Management Performance

The purpose of this research is to extract social media Twitter opinion on a tertiary institution using sentiment analysis. The results of sentiment analysis will provide input to universities as a form of evaluation of management performance in managing institutions. Sentiment analysis generated using the Naïve Bayes Classifier method which is classified into 4 classes: positive, normal, negative and unknown. This study uses 1000 data tweets used for training data needs. The data is classified manually to determine the sentiment of the tweet. Then 20 tweet data is used for testing. The results of this study produce a system that can classify sentiments automatically with 75% test results for sentiment, some obstacles in processing real-time tweets such as duplicate tweets (spam tweets), Indonesian structures that are quite complex and diverse.

scholarly journals Real-time tracking and mining of users’ actions over social media

2020 ◽  
Vol 17 (2) ◽  
pp. 403-426
Author(s):  
Ejub Kajan ◽  
Noura Faci ◽  
Zakaria Maamar ◽  
Mohamed Sellami ◽  
Emir Ugljanin ◽  
...  
Keyword(s):  
Social Media ◽  
Web 2.0 ◽  
Sentiment Analysis ◽  
Real Time ◽  
Data Analytics ◽  
Web 2.0 Technologies ◽  
Media Companies ◽  
Real Time Tracking

With the advent of Web 2.0 technologies and social media, companies are actively looking for ways to know and understand what users think and say about their products and services. Indeed, it has become the practice that users go online using social media like Facebook to raise concerns, make comments, and share recommendations. All these actions can be tracked in real-time and then mined using advanced techniques like data analytics and sentiment analysis. This paper discusses such tracking and mining through a system called Social Miner that allows companies to make decisions about what, when, and how to respond to users? actions over social media. Questions that Social Miner allows to answer include what actions were frequently executed and why certain actions were executed more than others.

Social media content and sentiment analysis on consumer security breaches

2016 ◽  
Vol 23 (4) ◽  
pp. 855-869 ◽  
Author(s):  
Jianqiang Hao ◽  
Hongying Dai
Keyword(s):  
Social Media ◽  
Empirical Study ◽  
Sentiment Analysis ◽  
Real Time ◽  
Public Perception ◽  
Public Awareness ◽  
Content Type ◽  
Security Breaches ◽  
Short Period ◽  
Significant Difference

Purpose Security breaches have been arising issues that cast a large amount of financial losses and social problems to society and people. Little is known about how social media could be used a surveillance tool to track messages related to security breaches. This paper aims to fill the gap by proposing a framework in studying the social media surveillance on security breaches along with an empirical study to shed light on public attitudes and concerns. Design/methodology/approach In this study, the authors propose a framework for real-time monitoring of public perception to security breach events using social media metadata. Then, an empirical study was conducted on a sample of 1,13,340 related tweets collected in August 2015 on Twitter. By text mining a large number of unstructured, real-time information, the authors extracted topics, opinions and knowledge about security breaches from the general public. The time series analysis suggests significant trends for multiple topics and the results from sentiment analysis show a significant difference among topics. Findings The study confirms that social media monitoring provides a supplementary tool for the traditional surveys which are costly and time-consuming to track security breaches. Sentiment score and impact factors are good predictors of real-time public opinions and attitudes to security breaches. Unusual patterns/events of security breaches can be detected in the early stage, which could prevent further destruction by raising public awareness. Research limitations/implications The sample data were collected from a short period of time on Twitter. Future study could extend the research to a longer period of time or expand key words search to observe the sentiment trend, especially before and after large security breaches, and to track various topics across time. Practical implications The findings could be useful to inform public policy and guide companies responding to consumer security breaches in shaping public perception. Originality/value This study is the first of its kind to undertake the analysis of social media (Twitter) content and sentiment on public perception to security breaches.

Real Time Sentiment Analysis

2020 ◽  
Vol 11 (1) ◽  
pp. 27-35
Author(s):  
Sandip Palit ◽  
Soumadip Ghosh
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Unstructured Data ◽  
Societal Issues ◽  
The Social ◽  
The World ◽  
Social Media Platforms ◽  
Primary Medium ◽  
Different Parts

Data is the most valuable resource. We have a lot of unstructured data generated by the social media giants Twitter, Facebook, and Google. Unfortunately, analytics on unstructured data cannot be performed. As the availability of the internet became easier, people started using social media platforms as the primary medium for sharing their opinions. Every day, millions of opinions from different parts of the world are posted on Twitter. The primary goal of Twitter is to let people share their opinion with a big audience. So, if the authors can effectively analyse the tweets, valuable information can be gained. Storing these opinions in a structured manner and then using that to analyse people's reactions and perceptions about buying a product or a service is a very vital step for any corporate firm. Sentiment analysis aims to analyse and discover the sentiments behind opinions of various people on different subjects like commercial products, politics, and daily societal issues. This research has developed a model to determine the polarity of a keyword in real time.

scholarly journals Deep Learning-Based Election Results Prediction Using Twitter Activity

2021 ◽  
Author(s):  
Haider Ali ◽  
Haleem Farman ◽  
Hikmat Yar ◽  
Zahid Khan ◽  
Shabana Habib ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
General Election ◽  
Learning Approaches ◽  
Social Media Platforms ◽  
General Elections ◽  
Election Results ◽  
Twitter Activity ◽  
The Stability

Abstract Nowadays, political parties have widely adopted social media for their party promotions and election campaigns. During the election, Twitter and other social media platforms are used for political coverage to promote the party and its candidates. This research discusses and estimates the stability of many volumetric social media approaches to forecast election results from social media activities. Numerous machine learning approaches are applied to opinions shared on social media for predicting election results. This paper presents a machine learning model based on sentiment analysis to predict Pakistan's general election results. In a general election, voters vote for their favorite party or candidate based on their personal interests. Social media has been extensively used for the campaign in Pakistan general election 2018. Using a machine learning technique, we provide a five-step process to analyze the overall election results, whether fair or unfair. The work is concluded with detailed experimental results and a discussion on the outcomes of sentiment analysis for real-world forecasting and approval for general elections in Pakistan.

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