Journal Articles
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Item A novel sentiment analysis of social networks using supervised learning(Springer-Verlag Wien michaela.bolli@springer.at, 2014) Anjaria, M.; Guddeti, R.M.R.Online microblog-based social networks have been used for expressing public opinions through short messages. Among popular microblogs, Twitter has attracted the attention of several researchers in areas like predicting the consumer brands, democratic electoral events, movie box office, popularity of celebrities, the stock market, etc. Sentiment analysis over a Twitter-based social network offers a fast and efficient way of monitoring the public sentiment. This paper studies the sentiment prediction task over Twitter using machine-learning techniques, with the consideration of Twitter-specific social network structure such as retweet. We also concentrate on finding both direct and extended terms related to the event and thereby understanding its effect. We employed supervised machine-learning techniques such as support vector machines (SVM), Naive Bayes, maximum entropy and artificial neural networks to classify the Twitter data using unigram, bigram and unigram + bigram (hybrid) feature extraction model for the case study of US Presidential Elections 2012 and Karnataka State Assembly Elections (India) 2013. Further, we combined the results of sentiment analysis with the influence factor generated from the retweet count to improve the prediction accuracy of the task. Experimental results demonstrate that SVM outperforms all other classifiers with maximum accuracy of 88 % in predicting the outcome of US Elections 2012, and 68 % for Indian State Assembly Elections 2013. © 2014, Springer-Verlag Wien.Item Discovering spammer communities in twitter(Springer New York LLC barbara.b.bertram@gsk.com, 2018) Bindu, P.V.; Mishra, R.; Santhi Thilagam, P.S.Online social networks have become immensely popular in recent years and have become the major sources for tracking the reverberation of events and news throughout the world. However, the diversity and popularity of online social networks attract malicious users to inject new forms of spam. Spamming is a malicious activity where a fake user spreads unsolicited messages in the form of bulk message, fraudulent review, malware/virus, hate speech, profanity, or advertising for marketing scam. In addition, it is found that spammers usually form a connected community of spam accounts and use them to spread spam to a large set of legitimate users. Consequently, it is highly desirable to detect such spammer communities existing in social networks. Even though a significant amount of work has been done in the field of detecting spam messages and accounts, not much research has been done in detecting spammer communities and hidden spam accounts. In this work, an unsupervised approach called SpamCom is proposed for detecting spammer communities in Twitter. We model the Twitter network as a multilayer social network and exploit the existence of overlapping community-based features of users represented in the form of Hypergraphs to identify spammers based on their structural behavior and URL characteristics. The use of community-based features, graph and URL characteristics of user accounts, and content similarity among users make our technique very robust and efficient. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.Item Racism and social media: A study in Indian context(Inderscience Publishers, 2019) Chetty, N.; Alathur, S.Racism is a kind of hatred behaviour, exhibited in written, verbal or physical form against the ethnicity or physical appearance of a group or an individual. Around the world, problematic behavioural incidents occur and in India, media often interpret it as towards people of colour or blacks. There was a lot of discussion about these incidents both online and offline, some groups consider that India is not free from racism and others view it is of misinformation processing. Therefore, there is a requirement of a system to evaluate and determine the possible scenarios towards racism in India. In this regard, using four different sets of keywords we created Twitter datasets. The data collected from social media are analysed to identify the polarity of content and the amount of racism using the software developed in R programming language. Contents are categorised in different polarities such as racist, non-racist and neutral. © © 2019 Inderscience Enterprises Ltd.Item Honour, hate and violence in social media: Insights from India(Inderscience Publishers, 2019) Chetty, N.; Alathur, S.Honour-based hate content is predominantly generated from family hate content and may affect humanity. In the Indian context, analysis of multiple resources such as literature, reported articles and social media sites pertinent to honour-based hate content is less. Therefore, the purpose of this paper is to identify and understand the influencing factors and emotions of honour-based hate content. A review of literature, news articles on honour killing and the analysis of Twitter content are made to attain the purpose. In India, factor like marrying a person against family members' ideologies is observed as dominating among other factors of honour-based hate content. It has been also observed that emotions such as anger, fear, disgust and sadness are used to express hate. Possible impacts of honour-based hate content on family and society are discussed. The analysis of emotions about honour and hate content increases novelty of the article. © 2019 Inderscience Enterprises Ltd.Item Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach(Elsevier B.V., 2023) Jena, P.R.; Majhi, R.The problem of stock market prediction is a challenging task owing to its complex nature and the numerous indirect factors at play. The sentiments regarding socio-political issues such as wars and pandemics can affect stock prices. The spread of the COVID-19 pandemic continues to take a toll on the economy and fluctuations in sentiment of the concerns about the health impacts of the disease can be captured from the microblogging platform, Twitter. We examined how these sentiments during the Covid-19 pandemic and the health impacts arising from the disease along with other macroeconomic indicators provide useful information to predict the stock indices in a more accurate manner. We developed a machine learning model namely, long-short term memory (LSTM) networks to predict the impact of the Covid-19 induced sentiments on the stock values of different sectors in the United States and India. We did the same predictions using the timeseries statistical models such as autoregressive moving average model and the linear regression model. We then compared the performance of the LSTM and the timeseries statistical models to find that the machine learning model has produced more accurate predictions of the stock indices. The performance of the models across the sectors and between the United States and India are compared to draw economic inferences. © 2022 The Author(s)