Approaches and Applications for Sentiment Analysis

With the increasing penetration of the internet, an ever-growing number of people are voicing their opinions in the numerous blogs, tweets, forums, social networking, and consumer review websites. Each such opinion has a sentiment (positive, negative, or neutral) associated with it. But the problem is that the amount of data is simply overwhelming. Methods like supervised machine learning and lexical-based approaches are available for measuring sentiments that have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services. This chapter presents sentiment analysis applications and challenges with their approaches and tools. The techniques and applications discussed in this chapter will provide a clear-cut idea to the sentiment analysis researchers to carry out their work in this field.

NEURAL NETWORK INFORMATION-MEASURING SYSTEM IN TASKS OF RESOURCE PREDICTION

It was established that methods based on artificial neural networks (HC) find the most widespread in predicting thermal processes in power cable networks. Analysis of influence of various functions of HC activation on forecast error of thermoflux processes in power cable networks was carried out. It is established that the minimum error of thermal processes prediction in power cable networks is HC with function of logsig activation in hidden layer and pureline in output layer.

Spread of Misinformation in Social Networks: Analysis Based on Weibo Tweets

Social networks are filled with a large amount of misinformation, which often misleads the public to make wrong decisions, stimulates negative public emotions, and poses serious threats to public safety and social order. The spread of misinformation in social networks has also become a widespread concern among scholars. In the study, we took the misinformation spread on social media as the research object and compared it with true information to better understand the characteristics of the spread of misinformation in social networks. This study adopts a deep learning method to perform content analysis and emotion analysis on misinformation dataset and true information dataset and adopts an analytic network process to analyze the differences between misinformation and true information in terms of network diffusion characteristics. The research findings reveal that the spread of misinformation on social media is influenced by content features and different emotions and consequently produces different changes. The related research findings enrich the existing research and make a certain contribution to the governance of misinformation and the maintenance of network order.

Digital Identities of Young People from the South of Spain: An Online Sexual Differentiation

Through a mixed methodological approach, we want to know how adolescents aged between 14 and 16 years from the south of Spain express and identify themselves on social networks, with respect to their sex. As such differences can determine gender inequality, we will analyse differences between females and males regarding the expression of identity on social networks. Analysis of obtained results demonstrates that many relevant attributes still emerge such as the socio-cultural representation of gender as sex in social networks. Differences emerged between the identity expressions of females and males which can generate inequalities favouring females and males. This implies a series of repercussions and, ultimately, defines the so-called digital gender divide. Taking into account these results we could intervene in the population of children to carry out prevention activities focused on social networks.

Graph convolutional networks: analysis, improvements and results

A graph can represent a complex organization of data in which dependencies exist between multiple entities or activities. Such complex structures create challenges for machine learning algorithms, particularly when combined with the high dimensionality of data in current applications. Graph convolutional networks were introduced to adopt concepts from deep convolutional networks (i.e. the convolutional operations/layers) that have shown good results. In this context, we propose two major enhancements to two of the existing graph convolutional network frameworks: (1) topological information enrichment through clustering coefficients; and (2) structural redesign of the network through the addition of dense layers. Furthermore, we propose minor enhancements using convex combinations of activation functions and hyper-parameter optimization. We present extensive results on four state-of-art benchmark datasets. We show that our approach achieves competitive results for three of the datasets and state-of-the-art results for the fourth dataset while having lower computational costs compared to competing methods.

Transcriptome Based Classification Model and Co-Expression Networks Analysis Identify CXCR4alt Subtype and Blnkaf in B-Cell Precursor Acute Lymphoblastic Leukemia

Background B-cell precursor acute lymphoblastic leukemia (B-ALL) is a genetically heterogeneous group of acute leukemia with stage-specific phenotypes and cytogenetic features. Although the research on the molecular profile of B-ALL benefits diagnosis and risk stratification, the idiographic leukemogenesis beyond the transcriptome remains unknown. Genomic lesions in B-ALL frequently involve genes belonging to transcription factors, such as TCF3, EBF1, PAX5, and IKZF1. The investigation of dysregulated transcriptional networks behind various B-ALL subtypes may help unravel the specific process of leukemogenesis. Methods A random forest model was trained on a well-defined molecular subtype B-ALL cohort (n = 504) to improve the molecular classification. The subtype-specific transcriptional network was constructed by weighted correlation network analysis (WGCNA) once the B-ALL subtypes were genetically determined by the random forest model. Additionally, alternative splicing analysis from RNA-seq was emphasized since aberrant splicing events could lead to abnormalities in transcription factors or tumor suppressor genes. Results The random forest model performs well for the classification of most B-ALL subtypes (Figure 1A). It also benefits the classification of Ph-like B-ALL, which displays a gene expression profile similar to BCR-ABL1 B-ALL, as it achieves 100% accuracy on well-known Ph-like cases characterized by ABL-class gene fusions, PAX5-JAK2, EBF1-PDGFRB, and IGH-EPOR. We successfully separated a candidate molecular subtype characterized by CXCR4 alteration (CXCR4alt) for the first time, through our novel classification model (Figure 1B). This newly identified CXCR4alt subtype accounts for 2% of B-ALL cases (11/504), characterized by CXCR4 C-terminal mutation R334X or FLNA overexpression. Both C-terminal mutation and upregulated FLNA contribute to delayed CXCR4 receptor internalization, enhanced CXCL12-CXCR4 signaling, and then continuously activates the downstream MAPK pathway. It is further supported by the high expression of the two oncogenic MAPK signaling pathway genes KIAA1549 and KIAA1549L from the co-expression network of CXCR4alt in these cases. Transcriptional co-expression networks constructed by WGCNA and network hub genes for most B-ALL subtypes also help to elucidate the mechanism of leukemogenesis (Figure 2). We identified an alternative first exon of BLNK (BLNKaf) that leads to loss of function as a shared event in specific subtypes, such as BCR-ABL1, BCR-ABL1-like, and PAX5alt; while in pre-BCR signaling positive subtypes, such as TCF3-PBX1 and MEF2D-r, only express normal BLNK transcripts. Discussion By comprehensive transcriptome-based classification model and co-expression networks analysis, we identified a novel defined CXCR4alt subtype with an incidence of 2% in B-ALL. We also observed that BLNKaf might supply a practical marker for monitoring pre-BCR signaling. Our report emphasizes the role of transcriptome-based machine learning and WGCNA in mining the molecular mechanism of B-ALL. The molecular pathogenesis and clinical significance of these newly identified molecular subtypes and molecular abnormalities are worthy of further investigation. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.