Ontology-based Sentiment Analysis Process for Social Media Content
Bibliographic record
Abstract
Social media provides a platform where users share an abundance of information on anything and everything. The information may consist of users’ emotions, feedbacks, reviews, and personal experiences. In this research a novel Ontology-based Sentiment Analysis Process for Social Media content (OSAPS) with negative sentiments is presented. The social media content is automatically extracted from the twitter messages. An ontology-based process is designed to retrieve and analyse the customers’ tweet with negative sentiments. This idea is demonstrated with the identification of customer dissatisfaction of the delivery service issues of the United States Postal Service, Royal Mail of United Kingdom, and Canada post. The tweets related to the delivery service include delay in delivery, lost package/s or improper customer services at the office in person or at call centres. A combination of technologies for twitter extraction, data cleaning, subjective analysis, ontology model building, and sentiment analysis are used. The results from this analysis could be used by the company to take corrective measures for the problems as well as to generate an automated online reply for the issues. A rule-based classifier could be used for generating the automated online replies.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".