Harnessing Convolutional Neural Networks for Sentiment Analysis of Tweets on the Metaverse
Bibliographic record
Abstract
In the digital era, understanding public sentiment towards emerging technologies such as the Metaverse is crucial for businesses and developers. Traditional sentiment analysis methods often struggle to accurately interpret the nuances of digital communication, particularly when assessing rapid technological advancements. This study harnesses the power of Convolutional Neural Networks (CNNs) to address this challenge, focusing on sentiment analysis of tweets related to the Metaverse. By employing advanced preprocessing techniques including tokenization and vectorization, and by using a CNN model, we have analyzed a dataset of tweets to discern public opinion with high precision. The CNN demonstrated remarkable effectiveness, achieving an overall accuracy of ${9 7 \%}$ with precision, recall, and F1-scores exceeding 0.96 for both positive and negative sentiments. These results underline the potential of CNNs in extracting meaningful insights from social media data, which can be pivotal for shaping marketing strategies and understanding consumer behavior towards new technologies like the Metaverse. The findings suggest that leveraging such deep learning models could significantly enhance the accuracy and depth of sentiment analysis in digital communication landscapes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".