Translation is not enough: Comparing Lexicon-based methods for sentiment analysis in Persian
Bibliographic record
Abstract
Sentiment analysis is a subfield of data mining and natural language processing with the aim of extracting people's opinion and appraisals from their comments on the Web. Contrary to machine learning approach, lexicon-based methods have some important advantages like domain-independency and being needless of a large annotated training corpus and hence are faster. This makes lexicon-based approach prevalent in the sentiment analysis community. However, for Persian language, in contrast to English, using lexicon-based method is a new discipline. There are limited lexicons available for sentiment analysis in Persian, almost all of them are directly translated from English. In the current study, four lexicons are compared to show the importance of lexicons in the performance of document-level sentiment analysis. Specifically, the Persian version of NRC lexicon, SentiStrength, CNRC, and Adjectives are compared in a pure lexicon-based scenario. Experiments are carried out on the document-level edition of SPerSent dataset. Results show that direct translation used in NRC leads the poorest performance while pre-processing and refining lexicons used in SentiStrength and CNRC improves the performance. Also, the results show that using just adjectives leads to higher results in comparison to using NRC.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".