scholarly journals A Model of Extracting Patterns in Social Network Data Using Topic Modelling, Sentiment Analysis and Graph Databases

2017 ◽  
Author(s):  
Assane Wade ◽  
Giovanna Di MarzoSerugendo
Keyword(s):  
Social Network ◽  
Sentiment Analysis ◽  
Network Data ◽  
Graph Databases ◽  
Topic Modelling ◽  
Social Network Data

scholarly journals Questions and features of automated social networks monitoring with support of intelligent user messages analysis.

2014 ◽  
Vol 2014 (4) ◽  
pp. 146-152 ◽  
Author(s):  
Александр Подвесовский ◽  
Aleksandr Podvesovskiy ◽  
Дмитрий Будыльский ◽  
Dmitriy Budylskiy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Software Implementation ◽  
Network Data ◽  
Research Directions ◽  
Social Network Data ◽  
Monitoring Model

An opinion mining monitoring model for social networks introduced. The model includes text mining processing over social network data and uses sentiment analysis approach in particular. Practical usage results of software implementation and its requirements described as well as further research directions.

scholarly journals Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text

2021 ◽  
Vol 11 (11) ◽  
pp. 4768
Author(s):  
Sanaa Kaddoura ◽  
Maher Itani ◽  
Chris Roast
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Arabic Language ◽  
Network Data ◽  
Arabic Text ◽  
Social Network Data ◽  
Dialectal Arabic ◽  
The Impact ◽  
Modern Standard

With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors’ opinions about specific subjects, such as people’s opinions towards steps taken to manage the COVID-19 pandemic. Usually, people use dialectal language in their posts on social networks. Dialectal language has obstacles that make opinion analysis a challenging process compared to working with standard language. For the Arabic language, Modern Standard Arabic tools (MSA) cannot be employed with social network data that contain dialectal language. Another challenge of the dialectal Arabic language is the polarity of opinionated words affected by inverters, such as negation, that tend to change the word’s polarity from positive to negative and vice versa. This work analyzes the effect of inverters on sentiment analysis of social network dialectal Arabic posts. It discusses the different reasons that hinder the trivial resolution of inverters. An experiment is conducted on a corpus of data collected from Facebook. However, the same work can be applied to other social network posts. The results show the impact that resolution of negation may have on the classification accuracy. The results show that the F1 score increases by 20% if negation is treated in the text.

scholarly journals Geographical Topic Modelling on Spatial Social Network Data

2021 ◽  
Vol 193 ◽  
pp. 22-31
Author(s):  
Anastasia A. Funkner ◽  
Liubov O. Elkhovskaya ◽  
Iuliia D. Lenivtceva ◽  
Michil P. Egorov ◽  
Aleksandr D. Kshenin ◽  
...  
Keyword(s):  
Social Network ◽  
Network Data ◽  
Topic Modelling ◽  
Social Network Data
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