scholarly journals Context-aware social media user sentiment analysis

2020 ◽  
Vol 25 (4) ◽  
pp. 528-541 ◽  
Author(s):  
Bo Liu ◽  
Shijiao Tang ◽  
Xiangguo Sun ◽  
Qiaoyun Chen ◽  
Jiuxin Cao ◽  
...  
2021 ◽  
pp. 016555152199061
Author(s):  
Abdalsamad Keramatfar ◽  
Hossein Amirkhani ◽  
Amir Jalaly Bidgoly

Real-time messaging and opinion sharing in social media websites have made them valuable sources of different kinds of information. This source provides the opportunity for doing different kinds of analysis. Sentiment analysis as one of the most important of these analyses gains increasing interests. However, the research in this field is still facing challenges. The mainstream of the sentiment analysis research on social media websites and microblogs just exploits the textual content of the posts. This makes the analysis hard because microblog posts are short and noisy. However, they have lots of contexts which can be exploited for sentiment analysis. In order to use the context as an auxiliary source, some recent papers use reply/retweet to model the context of the target post. We claim that multiple sequential contexts can be used jointly in a unified model. In this article, we propose a context-aware multi-thread hierarchical long short-term memory (MHLSTM) that jointly models different kinds of contexts, such as tweep, hashtag and reply besides the content of the target post. Experimental evaluations on a real-world Twitter data set demonstrate that our proposed model can outperform some strong baseline models by 28.39% in terms of relative error reduction.


2021 ◽  
Vol 13 (7) ◽  
pp. 3836
Author(s):  
David Flores-Ruiz ◽  
Adolfo Elizondo-Salto ◽  
María de la O. Barroso-González

This paper explores the role of social media in tourist sentiment analysis. To do this, it describes previous studies that have carried out tourist sentiment analysis using social media data, before analyzing changes in tourists’ sentiments and behaviors during the COVID-19 pandemic. In the case study, which focuses on Andalusia, the changes experienced by the tourism sector in the southern Spanish region as a result of the COVID-19 pandemic are assessed using the Andalusian Tourism Situation Survey (ECTA). This information is then compared with data obtained from a sentiment analysis based on the social network Twitter. On the basis of this comparative analysis, the paper concludes that it is possible to identify and classify tourists’ perceptions using sentiment analysis on a mass scale with the help of statistical software (RStudio and Knime). The sentiment analysis using Twitter data correlates with and is supplemented by information from the ECTA survey, with both analyses showing that tourists placed greater value on safety and preferred to travel individually to nearby, less crowded destinations since the pandemic began. Of the two analytical tools, sentiment analysis can be carried out on social media on a continuous basis and offers cost savings.


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