Identification and Classification of Radio Pulsar Signals Using Machine Learning

Pang, Di

doi:10.33915/etd.10280

Cited by 2 publications

(1 citation statement)

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“…In addition to the aforementioned modifications, we also experimented with different epoch sizes, optimizers, and learning rates to further optimize the performance of our sentiment classification models [48]. After evaluating various optimizers, we found that Adam worked best for the Hybrid and LSTM model, while Stochastic gradient descent was the most accurate for the RNN model [49].…”

Section: Resultsmentioning

confidence: 99%

The Impact of COVID-19 on E-Learning: Context-Based Sentiment Analysis Discourse Using Text Mining

Rehman,

Aslam,

Abid

et al. 2023

VAWKUM trans. comput. sci.

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Finding the most dominant and pertinent user opinions on a certain topic is crucial to the sentiment analysis success factor. During the pandemic lockdowns around the world, the suspension of academic institutions leads to an exceptional increase in distance education. Academic institutions closed their campuses immediately to mitigate the effects of COVID-19 and prevent its pervasive spread, and educational activities were shifted to online platforms. The effectiveness of online education is a significant topic of interest for both students and their parents, especially in terms of how students and teachers perceive it and how technologically viable it is in a range of social circumstances. Before such a wide adoption of e-learning is possible, these issues must be analyzed from multiple perspectives. The present research aims to evaluate the efficacy of e-learning by examining individuals' perceptions of it. Opinions can be found on websites such as Instagram, Facebook, Twitter, etc. As social media has recently emerged as a significant means of communication. This study addresses factors connected to a significant change in the educational system. 200,000 tweets were gathered from Twitter to evaluate the opinions of Twitter users who were taking part in online learning. This study adopts VADER to analyze the subjectivity and polarity score of tweets, a topic model was also created using the LDA algorithm to determine the themes that were talked about on Twitter the most. The models have been constructed and evaluated using Word2Vec to capture the semantic relationships between words and LSTM and RNN sequential model for sentiment analysis. This study measured the efficiency of a sentiment analysis model using the accuracy metric, the conducted experiments reveal that the proposed hybrid model achieves an overall accuracy of 96.3%. The results also indicate a significant negative impact of the Covid-19 pandemic on individuals' emotions, with 64.4% of the analyzed tweets displaying negative sentiments. These findings provide valuable insights into the relationship between global events and individual emotions on social media platforms.

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Section: Resultsmentioning

confidence: 99%