Sentiment Analysis using NLP and Machine Learning Techniques on Social Media Data

Kavitha, M.; Naib, Bharat Bhushan; Mallikarjuna, Basetty; Kavitha, R.; Srinivasan, R.

doi:10.1109/icacite53722.2022.9823708

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the survey was conducted in Polish and the NLP models for sentiment analysis are trained mainly based on English [48], automatic translations [49] using two translators were used. The two publicly available translators were compared: Google Translator [50] and MyMemory Translator [51].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Sentiment Analysis Methods Used to Investigate the Quality of Teaching Aids Based on Virtual Simulators of Embedded Systems

Radecki,

Rybicki

2024

Electronics

View full text Add to dashboard Cite

Virtual simulators of embedded systems and analyses of student surveys regarding their use at the early stage of the process of learning embedded systems, are presented in this article. The questionnaires were prepared in the Polish language, and the answers were automatically translated into English using two publicly available translators. The results of users’ experiences and feelings related to the use of virtual simulators are shown on the basis of detected sentiment using three chosen analysis methods: the Flair NLP library, the Pattern library, and the BERT NLP model. The results of the selected sentiment detection methods were compared and related to users reference answers, which gives information about the methods quality of the methods and their possible use in the automated review analysis process. This paper comprises detailed sentiment analysis results with a broader statistical approach for each question. Based on the students feedback and sentiment analysis, a new version of the TMSLAB v.2 virtual simulator was created.

show abstract

Section: Introductionmentioning

confidence: 99%