Large Scale and Parallel Sentiment Analysis Based on Label Propagation in Twitter Data
Bibliographic record
Abstract
Sentiment analysis is a promising branch in natural language processing, but it becomes challenging when dealing with data from Twitter due to the big volume, rapidly changing language style and a lack of training data. As a result, it is difficult to utilize the traditional lexicon-based approach and supervised learning method for the problems mentioned above. In this paper, we propose the label propagation algorithm in order to solve the last two problems based on graph structure and apply GraphX, an API in Spark framework for graph parallel computing, to address the first problem. The results show that the label propagation algorithm is robust and scalable in our parallel implementation. Meanwhile, our approach which utilizes the lexicon and noisy label like emoticons outperform the baseline significantly. For the future works, we plan to test more algorithms in clusters and optimize the way of taking advantage of the social network by adding a community detection procedure before the classification to improve the accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".