Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach
Bibliographic record
Abstract
The sentiment analysis task has been traditionally divided into lexicon or machine learning approaches, but recently the use of word embeddings methods have emerged, that provide powerful algorithms to allow semantic understanding without the task of creating large amounts of annotated test data. One problem with this type of binary classification, is that the sentiment output will be in the form of '1' (positive) or '0' (negative) for the string of text in the tweet, regardless if there are one or more entities referred to in the text. This paper plans to enhance the word embeddings approach with the deployment of a sentiment lexicon-based technique to appoint a total score that indicates the polarity of opinion in relation to a particular entity or entities. This type of sentiment classification is a way of associating a given entity with the adjectives, adverbs, and verbs describing it, and extracting the associated sentiment to try and infer if the text is positive or negative in relation to the entity or entities.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".