Mood detection and prediction using conventional machine learning techniques on COVID19 data.

Emotion detection is a promising field of research in multiple perspectives such as psychology, marketing, network analysis and so on. Multiple models have been suggested over the years for accurate and efficient mood detection. Identifying emotion, or mood, from text has progressed from a simple frequency distribution analysis to far more complicated learning approaches. The main aim of all these text mining and analysis is twofold. First is to categorise existing text into broad classes of emotions, such as happy, sad, angry, surprised and so on. The second aim is to accurately predict the moods of real-time streaming text. The novelty of the work lies in the extensive comparison of nine conventional learning methods with respect to performance metrics precision, recall, F1 and accuracy as well as studying the variance of mood over time using a wide array of moods (25). Using conventional classifiers allow near real-time predictions, can work on considerably less training data, and has the flexibility of feature engineering, as deep learning methods have feature engineering embedded in the model. Since a single line of text can be associated with multiple emotions, this article compares the performance of classifiers in predicting multiple moods for streaming text with likelihood-based ranking. An android application named Citizens' Sense was developed for text collection and analysis. The performance of mood classifiers are tested further using Twitter data related to COVID19. Based on the precision, recall, F1 and accuracy of the classifiers, it can be seen that Random Forest, Decision Tree and Complement Naive Bayes classifiers are marginally better than the other classifiers. The variance of mood over time, and predicted moods for text support this finding.

Impact of second-order network motif on online social networks.

The behaviour of individual users in an online social network is a major contributing factor in determining the outcome of multiple network phenomenon. Group formation, growth of the network, information propagation, and rumour blocking are some of the many network behavioural traits that are influenced by the interaction patterns of the users in the network. Network motifs capture one such interaction pattern between users in online social networks (OSNs). For this work, four second-order (two-edged) network motifs have been considered, namely, message receiving pattern, message broadcasting pattern, message passing pattern, and reciprocal message pattern, to analyse user behaviour in online social networks. This work provides and utilizes a node interaction pattern-finding algorithm to identify the frequency of aforementioned second-order network motifs in six real-life online social networks (Facebook, GPlus, GNU, Twitter, Enron Email, and Wiki-vote). The frequency of network motifs participated in by a node is considered for the relative ranking of all nodes in the online social networks. The highest-rated nodes are considered seeds for information propagation. The performance of using network motifs for ranking nodes as seeds for information propagation is validated using statistical metrics Z-score, concentration, and significance profile and compared with baseline ranking methods in-degree centrality, out-degree centrality, closeness centrality, and PageRank. The comparative study shows the performance of centrality measures to be similar or better than second-order network motifs as seed nodes in information diffusion. The experimental results on finding frequencies and importance of different interaction patterns provide insights on the significance and representation of each such interaction pattern and how it varies from network to network.

Serum Ferritin: A Backstage Weapon in Diagnosis of Dengue Fever.

AIMS: This retrospective study evaluates ferritin as a surrogate marker for dengue infection (NS1 and IgM negative stage) as opposed to other febrile illnesses of infective or inflammatory etiology (OFI). METHODOLOGY: Data of all patients admitted to medical ward and medical ITU during the dengue outbreak were collected. Patients admitted between 5 and 10 days of febrile illness without a diagnosis were included. Patients with NS1 positivity (Days 2-8) and/or positive IgM for dengue (Days 6-10) were considered to be dengue cases and those with other confirmed diagnoses were considered in the OFI group. Ferritin, CRP, TC of WBC, platelet count, SGOT, SGPT, and albumin levels were analysed for both groups. RESULTS: We examined 30 cases of clinically and serologically confirmed dengue fever and 22 cases of OFI. Ferritin level in dengue cohort was significantly higher than the OFI group (p < 0.0001). The best cut-off for ferritin level to differentiate dengue from OFI was found to be 1291. The sensitivity at this cut-off is 82.6% and the specificity at this cut-off is 100%. CONCLUSION: Ferritin may serve as a significant marker for differentiating between dengue fever and OFI, in absence of a positive NS1 antigen or a positive IgM antibody for dengue.