About the effects of sentiments on topic detection in social networks
Bibliographic record
Abstract
Topic detection from large textual data volumes extracted from Social Networks is an interesting research topic in the context of Big Data.The textual content present in Social Networks contains diverse information that can be exploited in order to obtain useful information.Topic detection and sentiment analysis in social networks are topics of widespread research.The study of both is sometimes intertwined as, usually, user messages revolve around a particular topic and express certain attitude of the user towards the topic discussed.However, this assumption is not valid for all messages as some of them express only general feelings or attitudes and do not refer to something in particular that covers up the topic discussed.In fact, these messages can influence the topic detection process.In this paper, we propose to obtain topics from massive quantities of text data extracted from social networks, without using previous information, and only with the use of unsupervised data mining techniques.We analyze the influence of sentiments in messages and how they affect the topic detection task.Terms related to sentiments provide useful information for a variety of applications, but not for topic detection where they represent a source of unnecessary noise.Experiments are conducted on data obtained from Twitter social network.
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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 | 0.015 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".