Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts

2016 ◽  
Vol 50 (1) ◽  
pp. 35-65 ◽  
Author(s):  
Preslav Nakov ◽  
Sara Rosenthal ◽  
Svetlana Kiritchenko ◽  
Saif M. Mohammad ◽  
Zornitsa Kozareva ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Texts

Sentiment, Stance, and Intent Detection in Turkish Tweets

2021 ◽  
pp. 206-217
Author(s):  
Dilek Küçük
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Detection Task ◽  
Detection Performance ◽  
Significant Research ◽  
Research Problems ◽  
Media Texts

Sentiment analysis, stance detection, and intent detection on social media texts are all significant research problems with several application opportunities. In this chapter, the authors explore the possible contribution of sentiment and intent information to machine learning-based stance detection on tweets. They first annotate a Turkish tweet dataset with sentiment and proprietary intent labels, where the dataset was already annotated with stance labels. Next, they perform stance detection experiments on the dataset using sentiment and intent labels as additional features. The experiments with SVM classifiers show that using sentiment and intent labels as additional features improves stance detection performance considerably. The final form of the dataset is made publicly available for research purposes. The findings reveal the contribution of sentiment and intent information to the solution of stance detection task on the Turkish tweet dataset employed. Yet, further studies on other datasets are needed to confirm that our findings are generalizable to other languages and on other topics.

scholarly journals Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic

2021 ◽  
Vol 13 (7) ◽  
pp. 3836
Author(s):  
David Flores-Ruiz ◽  
Adolfo Elizondo-Salto ◽  
María de la O. Barroso-González
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Cost Savings ◽  
Mass Scale ◽  
Tourism Sector ◽  
The Social ◽  
Analytical Tools ◽  
Media Data

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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