Constructing and Analyzing Uncertain Social Networks from Unstructured Textual Data

2013 ◽  
pp. 41-61 ◽  
Author(s):  
Fredrik Johansson ◽  
Pontus Svenson
Keyword(s):  
Social Networks ◽  
Textual Data

scholarly journals Análisis estadístico de datos textuales aplicado al uso de redes sociales

2015 ◽  
pp. 1-27
Author(s):  
María Luisa Hernández Maldonado ◽  
Herminia Domínguez Palmeros ◽  
Antonia Olivia Jarvio Fernández
Keyword(s):  
Social Networks ◽  
Statistical Analysis ◽  
Correspondence Analysis ◽  
Individual Characteristics ◽  
Redes Sociales ◽  
Textual Data ◽  
Open Question

Las preguntas abiertas son frecuentes en un cuestionario. Su utilización es necesaria particularmente cuando se busca conocer las motivaciones y la opinión de los entrevistados sobre algún asunto. Tradicionalmente el tratamiento de este tipo de respuestas consiste en cerrar la variable, complicándose el proceso a medida que el tamaño de la muestra aumenta. En estos casos, se utiliza un análisis estadístico de datos textuales que con ayuda del análisis de correspondencias, permite descubrir tendencias, desviaciones y asociaciones entre individuo y palabras. Para mostrar la metodología se presenta un estudio sobre el uso de redes sociales, tomando en cuenta una pregunta abierta y las características individuales de los entrevistados. El  análisis muestra que, eventualmente, algunas personas forman parte de una red social por conocer gente y por comunicarse, por diversión o trabajo, por facilidad, hacer tareas o comunicarse con personas lejanas y maestros. A otros no les gustan pero reconocen la necesidad de utilizarlas.AbstractOpen questions are frequent in a questionnaire. Its use is necessary when it is intended to know motivations and interviewees’ opinion about some matter. Traditionally, the treatment to this type of responses consists of closing the variable, the process gets complicated as soon as the sample increases. In these cases, it is used a statistical analysis of textual data that, with help of the correspondence analysis, allows to discover tendencies, deviations and associations between individual and words. To show the methodology, it is presented a study of social networks use, considering an open question and individual characteristics of the interviewees. The analysis shows that, eventually some people are part of social networks for meeting people and for communicating, for fun or work, for ease, for doing homework or to communicate with distant people and teachers. Others do not like them but they recognize the need of using them.Recibido: 27 de marzo de 2014Acpetado: 23 de septiembre de 2014

Social-Spatial Group Queries with Keywords

2022 ◽  
Vol 8 (1) ◽  
pp. 1-32
Author(s):  
Sajid Hasan Apon ◽  
Mohammed Eunus Ali ◽  
Bishwamittra Ghosh ◽  
Timos Sellis
Keyword(s):  
Social Networks ◽  
Social Groups ◽  
Real Life ◽  
Real Data ◽  
Data Sets ◽  
Interesting Insight ◽  
Points Of Interest ◽  
The Social ◽  
Textual Data ◽  
Aware Group

Social networks with location enabling technologies, also known as geo-social networks, allow users to share their location-specific activities and preferences through check-ins. A user in such a geo-social network can be attributed to an associated location (spatial), her preferences as keywords (textual), and the connectivity (social) with her friends. The fusion of social, spatial, and textual data of a large number of users in these networks provide an interesting insight for finding meaningful geo-social groups of users supporting many real-life applications, including activity planning and recommendation systems. In this article, we introduce a novel query, namely, Top- k Flexible Socio-Spatial Keyword-aware Group Query (SSKGQ), which finds the best k groups of varying sizes around different points of interest (POIs), where the groups are ranked based on the social and textual cohesiveness among members and spatial closeness with the corresponding POI and the number of members in the group. We develop an efficient approach to solve the SSKGQ problem based on our theoretical upper bounds on distance, social connectivity, and textual similarity. We prove that the SSKGQ problem is NP-Hard and provide an approximate solution based on our derived relaxed bounds, which run much faster than the exact approach by sacrificing the group quality slightly. Our extensive experiments on real data sets show the effectiveness of our approaches in different real-life settings.

Towards an Embedding-Based Approach for the Geolocation of Texts and Users on Social Networks

2021 ◽  
pp. 206-234
Author(s):  
Sarra Hasni
Keyword(s):  
Neural Networks ◽  
Social Networks ◽  
Spatial Analysis ◽  
Recurrent Neural Networks ◽  
Contextual Information ◽  
Language Models ◽  
New Approach ◽  
Textual Data ◽  
Word Forms ◽  
Statistical Language Models

The geolocation task of textual data shared on social networks like Twitter attracts a progressive attention. Since those data are supported by advanced geographic information systems for multipurpose spatial analysis, new trends to extend the paradigm of geolocated data become more emergent. Differently from statistical language models that are widely adopted in prior works, the authors propose a new approach that is adopted to the geolocation of both tweets and users through the application of embedding models. The authors boost the geolocation strategy with a sequential modelling using recurrent neural networks to delimit the importance of words in tweets with respect to contextual information. They evaluate the power of this strategy in order to determine locations of unstructured texts that reflect unlimited user's writing styles. Especially, the authors demonstrate that semantic proprieties and word forms can be effective to geolocate texts without specifying local words or topics' descriptions per region.

Building a contextual dimension for OLAP using textual data from social networks

2018 ◽  
Vol 93 ◽  
pp. 118-133 ◽  
Author(s):  
Karel Gutiérrez-Batista ◽  
Jesús R. Campaña ◽  
Maria-Amparo Vila ◽  
Maria J. Martin-Bautista
Keyword(s):  
Social Networks ◽  
Textual Data ◽  
Contextual Dimension

Could spatial features help the matching of textual data?

2020 ◽  
Vol 24 (5) ◽  
pp. 1043-1064
Author(s):  
Jacques Fize ◽  
Mathieu Roche ◽  
Maguelonne Teisseire
Keyword(s):  
Social Networks ◽  
Spatial Information ◽  
Extraction Methods ◽  
Heterogeneous Data ◽  
Similarity Estimation ◽  
Matching Process ◽  
Spatial Features ◽  
New Information ◽  
Textual Data ◽  
Textual Representation

Textual data is available to an increasing extent through different media (social networks, companies data, data catalogues, etc.). New information extraction methods are needed since these new resources are highly heterogeneous. In this article, we propose a text matching process based on spatial features and assessed through heterogeneous textual data. Besides being compatible with heterogeneous data, it comprises two contributions: first, spatial information is extracted for comparison purposes and subsequently stored in a dedicated spatial textual representation (STR); and then two transformations are applied on STR to improve the spatial similarity estimation. This article outlines the proposed approach with new contributions: (i) a new geocoding methods using general co-occurrences between entities, and (ii) a thorough evaluation followed by (iii) an in-depth discussion. The results obtained on two corpora demonstrate that good spatial matches (≈ 80% precision on major criteria) can be obtained between the most similar STRs with further enhancement achieved via STR transformation.

Analysis of Online Social Networks for the Identification of Sarcasm

2018 ◽  
pp. 92-105 ◽  
Author(s):  
Pulkit Mehndiratta
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Future Research ◽  
Research Directions ◽  
Textual Data ◽  
Future Research Directions

With the ever-increasing acceptance of online social networks (OSNs), a new dimension has evolved for communication amongst humans. OSNs have given us the opportunity to monitor and mine the opinions of a large number of online active populations in real time. Many diverse approaches have been proposed, various datasets have been generated, but there is a need of collective understanding of this area. Researchers are working around the globe to find a pattern to judge the mood of the user; the still serious problem of detection of irony and sarcasm in textual data poses a threat to the accuracy of the techniques evolved till date. This chapter aims to help the reader to think and learn more clearly about the aspects of sentiment analysis, social network analysis, and detection of irony or sarcasm in textual data generated via online social networks. It argues and discusses various techniques and solutions available in literature currently. In the end, the chapter provides some answers to the open-ended question and future research directions related to the analysis of textual data.

scholarly journals Sentiment Categorization through Natural Language Processing : A Survey

2019 ◽  
pp. 104-107
Author(s):  
Jyovita Christi ◽  
Gayatri Jain
Keyword(s):  
Social Networks ◽  
Feature Extraction ◽  
Sentiment Analysis ◽  
Language Processing ◽  
High Speed ◽  
Opinion Mining ◽  
Smart Phones ◽  
The Past ◽  
Textual Data ◽  
The Given

Sentiment is an attitude, thought, or judgment prompted by feeling. Sentiment analysis, which is also known as opinion mining, studies people’s sentiments towards certain entities. Sentiment Analysis isn’t an unfamiliar term anymore. Today, smart phones, high speed Internet and various forums and social networks, have made it very common for people to give voice to their opinions. Therefore, a lot of textual data is available in various forms where people express their opinions. Analysing this data to know the underlying sentiment behind it has also become quite popular these days. Various techniques and applications have been created in the past and even today to perform sentiment analysis. This paper contributes towards understanding some of the modern techniques and in knowing which technique to use under what circumstances. It also studies feature extraction which is an important aspect of sentiment analysis. Feature extraction allows us to identify the features in the given text and analyse the sentiment for each feature.

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