Sentiment Analysis of Tweets for Estimating Criticality and Security of Events

Subramaniyaswamy, V.; Ravi, Logesh; Abejith, M.; Umasankar, Sunil; Umamakeswari, A.

doi:10.4018/joeuc.2017100103

Cited by 42 publications

(18 citation statements)

References 9 publications

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“…Results showed that the accuracy of the unsupervised approach and lexicon-based approach could achieve 80.68% and 75.20%, respectively. Subramaniyaswamy et al [21] used a lexicon approach of sentiment analysis to predict possible violent events in the future from tweets. However, they only analyzed three proportions of sentiments: positive, negative, and neutral.…”

Section: Related Workmentioning

confidence: 99%

A Sequential Emotion Approach for Diagnosing Mental Disorder on Social Media

2020

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Mental disorder has been affecting numerous individuals; however, mental health care is in a passive state where only a minority of individuals actively seek professional help. Due to the rapid development of social networks, individuals accustomed to expressing their raw feelings on social media include patients who are suffering great pain from mental disorders. To distinguish individuals who merely feel sad and others who have mental disorders, the symptoms of mental disorder are taken into consideration. These symptoms constantly arise as a regular pattern like shifting of emotions or repeating of one representative emotion during a certain time. We proposed a Mental Disorder Identification Model (MDI-Model) to identify the four most commonly occurring mental disorders in the world: anxiety disorder, bipolar disorder, depressive disorder, and obsessive-compulsive disorder (OCD). The MDI-Model compares the sequential emotion pattern from users to identify mental disorders to detect those who are in a high risk. Tweets of diagnosed mental disorder users were analyzed to evaluate the accuracy of the MDI-Model, furthermore, the tweets of users from six different occupations were analyzed to verify the precision and predict the tendency of mental disorder among the different occupations. Results show that the MDI-Model can efficiently diagnose users with high precision in different mental statuses as severe, moderate, and mild stage, or tendency of mental disorder and mentally healthy status.

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Section: Related Workmentioning

confidence: 99%

A Sequential Emotion Approach for Diagnosing Mental Disorder on Social Media

2020

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“…Different experiments have been performed on social network data to clarify their user's behaviour and to establish various comprehensive management plans [16]. In addition, Sentiment analysis is one of the most relevant strategies utilized today in various disciplines in our lives [17] relying on supervised machine learning strategies cantered on dataset evaluation. Our research aims to empower social networking functionalities and integrate it with the Internet-of-Things technology to analyse tweets simultaneously to categorize sentiment of specific users.…”

Section: Iiiv Sentiment Analysis Techniques For Social Mediamentioning

confidence: 99%

Rule-based Recommendation System based on Semantic Web of Things

Salama¹,

Shaheen²,

Al-Feel³

2020

International Journal of Engineering Research and Technology

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Social networking has certainly been the most apparent concept of virtual human life where all users may communicate with each other. Thus all users of social networks aim to handle and monitor the demands of their lives through their platforms for virtual lifestyle. And since the Internet of Things is now considered one of the latest technologies which connect everything around us together as objects in the form of an IP address that enables contact between them. On the other side, IoT also struggles from the variability of data obtained from various sensors. Addressing this problem includes the alignment of semantic web technology. We, therefore, aim to take advantage of the Internet of Things and Semantic Web technologies revolution by developing an integrated model based on the semantic web. Through our proposed research, we intend to create a rule-based recommendation framework that advises users to take the most convenient action that IoT applications should execute on their current social network activities utilizing the built-in definition logic rules. By virtue of this social recommender, Users of IoT applications can monitor their apps only by communicating their social status via their social media instead of using special IoT apps on their mobile devices. As a result, there is more integration between IoT resources and Social Network resources.

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“…After analyzing various clustering algorithms, we have proposed a min-cut clustering algorithm for keyphrase extraction [29]. For persuasive recommendation, a travel plan is created based upon active target user, user id and location [30] [33]. Many types of wearable sensor devices contain unobtrusive sensors, smart textiles and printable electronic tattoos for health detection [31].…”

Section: Graph Modelsmentioning

confidence: 99%

A Novel Method to Detect Public Health in Online Social Network Using Graph-based Algorithm

Devika¹,

Sinduja²,

Subramaniyaswamy³

2019

EAI Endorsed Transactions on Pervasive Health and Technology

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INTRODUCTION: Twitter has played an important role in the social life of people. The health-related tweets are extracted and find the spread of epidemic disease on network. It can provide as a starting place of individual data to learn the physical condition of users. OBJECTIVES: Key objective is to develop graph-based algorithm to detect public health in online social network. METHODS: The proposed method collect the tweets relating to general health in twitter using the min-cut algorithm. The algorithm finds the minimum cut off an undirected edge-weighted graph. The runtime of the algorithm seems to be faster than other graph algorithms. Min-cut is reliable and good in network optimization and prevents redundancy. RESULTS: To evaluate the performance, we utilize the health dataset on the detection of epidemic disease. The proposed method using a graph-based algorithm is the best in terms of accuracy, precision, and recall. With respect to the confusion matrix, Min-cut provides the highest true positive when compared to Text rank and K-Means algorithm. CONCLUSION: Proposed health detection method using graph-based algorithm is better than Text Rank and K-Means in all aspects.

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Sentiment Analysis of Tweets for Estimating Criticality and Security of Events

Cited by 42 publications

References 9 publications

A Sequential Emotion Approach for Diagnosing Mental Disorder on Social Media

A Sequential Emotion Approach for Diagnosing Mental Disorder on Social Media

Rule-based Recommendation System based on Semantic Web of Things

A Novel Method to Detect Public Health in Online Social Network Using Graph-based Algorithm

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