Bibliographic record
Abstract
Electric Vehicles (EVs) are envisioned to play a large role in the transition from fossil fuel to renewables based transportation. However, their sales thus far are nominal compared to traditional car sales. It has been difficult for manufacturers to measure owners' initial perceptions in order to build improved vehicles more drivers are likely to adopt. Sentiments towards EVs have mostly been determined using either field trials or large surveys of drivers, both of which are problematic. We build a system that mines EV owners' sentiments from online forums. Our system has three main uses. First, it graphs the percentage of positive and negative opinions for each vehicle feature of interest, e.g., battery capacity, giving the user a high level product overview. There is currently no easily-consumable review system for EVs. Second, it allows the user to read opinions about the specific features they are most interested in without searching though irrelevant text. In our case study, we find only 3% of the comments on EV ownership forums express opinions on the features. The system therefore reduces the space of text the user must read by 97%, even assuming they wish to read all opinions about all features. Finally, in addition to mining the same perceptions found during expensive field trials, our system finds perceptions that were only realized after the owners possessed their EVs for an extended period of time, i.e., perceptions not available during shorter trials. The system extracts and classifies opinions with a precision and recall of 60%, which is on par or better than previous opinion mining systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.015 |
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".