scholarly journals Fine-grained sentiment analysis of social media with emotion sensing

2016 ◽  
Author(s):  
Zhaoxia Wang ◽  
Chee Seng Chong ◽  
Landy Lan ◽  
Yinping Yang ◽  
Seng Beng Ho ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Fine Grained

scholarly journals Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

2021 ◽  
Vol 11 (22) ◽  
pp. 10694
Author(s):  
Nora Alturayeif ◽  
Hamzah Luqman
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Loss Function ◽  
Psychological Effects ◽  
Fine Grained ◽  
Current Events ◽  
Social Media Platforms ◽  
Sentiment Detection ◽  
Textual Representation ◽  
Almost All

The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72.

Leveraging Fine-Grained Sentiment Analysis for Competitivity

2018 ◽  
Vol 17 (02) ◽  
pp. 1850018 ◽  
Author(s):  
Stephen Nabareseh ◽  
Eric Afful-Dadzie ◽  
Petr Klimek
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Online Advertising ◽  
Fine Grained ◽  
Textual Data ◽  
Telecommunication Companies ◽  
Social Media Tools ◽  
Use Of Social Media

The surge in the use of social media tools by most businesses and corporate society for varied purposes cannot be over emphasised. The two top social media sites heavily patronised by businesses are Facebook and Twitter. For companies to harness the business potential of social media to increase competitive advantage, sentiments behind textual data of their customers, fans and competitors must be monitored and analysed with keen interest. This paper demonstrates how companies in the Telecommunication industry can understand consumer opinions, frustrations and satisfaction through opinion mining analyses and interpret customers’ textual data to enhance competitiveness. Sentiment analysis that classifies positive, negative and neutral sentiments of customers of the top three telecommunication companies in Ghana (MTN, Vodafone and Tigo) is studied. The proposed method extracts “intelligence” from the classified customers’ comments and compares it with responses from the companies. The results show how customer sentiments can be harnessed into successful online advertising projects. Companies can use the results to enhance their responsiveness to customer-centred, improve on the quality of their service, integrate social sentiments into PR plan, develop a strategy for social media marketing and leverage on the advantages of online advertising.

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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