Performance analysis of sentiment classification based neural network
Bibliographic record
Abstract
Deep learning has more significant advantages for word embedding technology than sentiment analysis. This paper studies the application of deep learning on the word embedding problem in context, mainly discusses the RNN model with Word2Vec and without Word2Vec, then compares and analyzes their performance in the experiment, mainly evaluating the accuracy and test loss of seven models. This paper compares and illustrates the model which gets the different results in experiments, complementing the model and re-running the model, and analyzing the reasons for the difference in the performance of each model. The seven models are a single-layer neural network, multiple-layer (two and three) feed-forward neural networks, Convolutional Neural Network (CNN)- A feedforward neural network, which consists of single or multiple convolutional layers, pooling layers, and a fully connected layer on top, so this model is good at image processing. Long Short Term Memory (LSTM)- A temporal recurrent neural network, the advantage of the model is it could solve the gradient disappearance and explosion problem when it handles the long-sequence problem. Bi-directional Long Short Term Memory (Bi-LSTM)-Composed of forwarding LSTM and backward LSTM, it is very common for sequence labelling tasks that are related to the top and bottom, which are often used to model context information in NLP. Bi-directional Encoder Representation from Transformers (BERT)- A bidirectional language model. Finally, this paper analyses and evaluates these models with a specific illustration and research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".