Current State of Text Sentiment Analysis from Opinion to Emotion Mining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, but it should also encompass emotion mining. Opinion mining involves the use of natural language processing and machine learning to determine the attitude of a writer towards a subject. Emotion mining is also using similar technologies but is concerned with detecting and classifying writers emotions toward events or topics. Textual emotion-mining methods have various applications, including gaining information about customer satisfaction, helping in selecting teaching materials in e-learning, recommending products based on users emotions, and even predicting mental-health disorders. In surveys on sentiment analysis, which are often old or incomplete, the strong link between opinion mining and emotion mining is understated. This motivates the need for a different and new perspective on the literature on sentiment analysis, with a focus on emotion mining. We present the state-of-the-art methods and propose the following contributions: (1) a taxonomy of sentiment analysis; (2) a survey on polarity classification methods and resources, especially those related to emotion mining; (3) a complete survey on emotion theories and emotion-mining research; and (4) some useful resources, including lexicons and datasets.
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.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.003 |
| 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 it