Machine Learning Techniques to Determine the Polarity of Messages on Social Networks

Varga, Jesus; Lezama, Omar Bonerge Píneda; Payares, Karen

doi:10.1007/978-981-15-8443-5_10

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To this end, Polarity-related Independent Cascade (IC-P) diffusion model [11], techniques based on emotional calm states in which disruptive users are added as potential customers for the entities [12], are proposed. The characteristics found in the texts and the social network features: URLs, hashtags, and emoticons, among others, deserve a detailed analysis to discover texts' polarity [13]. Lexicons created by experts are used to construct dictionaries of newly coined words and emoticons to classify tweets emotionally [14].…”

Section: Related Workmentioning

confidence: 99%

Detecting Topics and Polarity From Twitter: A University Faculty Case

Ruíz,

Galan,

García-Beltrán

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Social networks have become a powerful communication tool, with millions of people exchanging information, opinions, and experiences daily. Companies, organizations, and even people have turned this tool into a marketing platform to position themselves and gain popularity. However, not only do companies present products or services to society, but society also provides feedback. This feedback has a significant impact as well. It is impossible to process all this vast information manually in time, but it is crucial. This information is precious even for governmental or state public entities such as universities. Potential future students will use social media to learn about the institution's general feel. Therefore, this study presents a new dataset called CEIMaT2021, which compiles all the tweets in Spanish related to the

show abstract

Section: Related Workmentioning

confidence: 99%

Detecting Topics and Polarity From Twitter: A University Faculty Case

Ruíz,

Galan,

García-Beltrán

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To this end, Polarity-related Independent Cascade (IC-P) diffusion model (Li et al, 2014), techniques based on emotional calm states in which disruptive users are added as potential customers for the entities (Abas, Addou, & Rachik, 2020), are proposed. The characteristics found in the texts and the social network features: URLs, hashtags and emoticons, among others, deserve a particular analysis to discover texts' polarity (Varga, Lezama, & Payares, 2021). Lexicons created by experts are used to construct dictionaries of newly coined words and emoticons to classify tweets in an emotional way (Yang, Ko, & Chung, 2019).…”

Section: Related Workmentioning

confidence: 99%

Detecting topics and polarity from Twitter: the ETSII-UPM case

Ruíz

Galán

Beltrán

et al. 2023

Preprint

View full text Add to dashboard Cite

Social networks have become a powerful communication tool, with millions of people exchanging information, opinions and experiences daily. Companies, organizations and even people have turned this tool into a marketing platform to position themselves and gain popularity. However, not only do companies present products or services to society, but society also provides feedback. This feedback has a significant impact as well. It is impossible to process all this vast information manually in time, but it is crucial. This information is precious even for governmental or state public entities such as universities. Potential future students will use social media to find out about the general feel of the institution. Therefore, this study presents a new dataset called CEIMaT2021, which compiles all the tweets in Spanish related to the Technical School of Industrial Engineering of the Universidad Politécnica de Madrid (ETSII-UPM). This dataset is designed for two of the main tasks of Online Reputation Management: (I) automatic detection of topics and (II) polarity. In addition, this study shows for these tasks that the SVM algorithm obtains a better performance for topic detection. Meanwhile, XGB obtains better results for polarity detection.

show abstract

Optimizing weighted lazy learning and Naive Bayes classification using differential evolution algorithm

Bai

Bain

2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

Machine Learning Techniques to Determine the Polarity of Messages on Social Networks

Cited by 3 publications

References 16 publications

Detecting Topics and Polarity From Twitter: A University Faculty Case

Detecting Topics and Polarity From Twitter: A University Faculty Case

Detecting topics and polarity from Twitter: the ETSII-UPM case

Optimizing weighted lazy learning and Naive Bayes classification using differential evolution algorithm

Contact Info

Product

Resources

About