A spatiotemporal approach for social media sentiment analysis

First Monday ◽  
2021 ◽  
Author(s):  
Andre Alves ◽  
Cláudio de Souza Baptista ◽  
Davi Oliveira Serrano de Andrade ◽  
Maxwell Guimarães De Oliveira ◽  
Aillkeen Bezerra De Oliveira
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Temporal Analysis ◽  
Accurate Analysis ◽  
Opinion Change ◽  
Flow Maps ◽  
Spatial And Temporal Analysis ◽  
Analysis Technique ◽  
Temporal Dimensions ◽  
Media Sentiment

The rapid growth of user-generated unstructured data through social media has raised several challenges and research opportunities. These data constitute a rich source of information for sentiment analysis and help the understanding of spontaneously expressed opinions. In the past few years, many scientific proposals have addressed sentiment analysis issues. However, most of them do not take into account both spatial and temporal dimensions, which would enable a more accurate analysis. To the best of our knowledge, this approach has not received much attention in the literature. In this article, we formalized a spatiotemporal sentiment analysis technique and applied this technique to a case study of tweets about the FIFA 2014 World Cup. Our approach exploits the summarization of sentiment analysis using the spatial and temporal dimensions and automatically generates opinion change flow maps through both dimensions. The results enable the tracking of opinion change flow maps through spatial and temporal analysis.

scholarly journals #London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

2018 ◽  
Vol 3 (1) ◽  
pp. 75-99 ◽  
Author(s):  
Anna Kovacs-Gyori ◽  
Alina Ristea ◽  
Clemens Havas ◽  
Bernd Resch ◽  
Pablo Cabrera-Barona
Keyword(s):  
Social Media ◽  
Urban Planning ◽  
Sentiment Analysis ◽  
Temporal Analysis ◽  
Spatial And Temporal Patterns ◽  
Sports Events ◽  
Spatial And Temporal Analysis ◽  
User Groups ◽  
Planning Processes ◽  
Negative Sentiment

The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach.

scholarly journals Social Media Sentiment Analysis: The Hajj Tweets Case Study

2021 ◽  
Vol 17 (3) ◽  
pp. 265-274
Author(s):  
Mohammad Ashraf Ottom ◽  
Khalid M.O. Nahar
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Sentiment

Impact of Deep Learning on Semantic Sentiment Analysis

2021 ◽  
pp. 97-117
Author(s):  
Neha Gupta ◽  
Rashmi Agrawal
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Semantic Technologies ◽  
Sources Of Information ◽  
Accurate Analysis ◽  
Online Social Media ◽  
Semantic Orientation ◽  
Semantically Enhanced

Online social media (forums, blogs, and social networks) are increasing explosively, and utilization of these new sources of information has become important. Semantics plays a significant role in accurate analysis of an emotion speech context. Adding to this area, the already advanced semantic technologies have proven to increase the precision of the tests. Deep learning has emerged as a prominent machine learning technique that learns multiple layers or data characteristics and delivers state-of-the-art output. Throughout recent years, deep learning has been widely used in the study of sentiments, along with the growth of deep learning in many other fields of use. This chapter will offer a description of deep learning and its application in the analysis of sentiments. This chapter will focus on the semantic orientation-based approaches for sentiment analysis. In this work, a semantically enhanced methodology for the annotation of sentiment polarity in Twitter/ Facebook data will be presented.

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