BrainJS/brain.js

Proper way to detect sentiments?

Open

#366 ouverte le 20 févr. 2019

Voir sur GitHub
 (1 commentaire) (0 réactions) (0 assignés)TypeScript (14 095 stars) (1 099 forks)batch import
help wantedneeds more info

Description

Hello, I'm trying to create a more complicated version of the Sentiment detector, using this example here: https://scrimba.com/p/pVZJQfg/c3n6qfg

However, I'm getting odd results. It seems to work well when there is very little data. But once I put a lot of data, it returns results like "T T T T T T T T T T T T" or some odd words created by mixing input and output.

What can I do to ensure the integrity of the outputs when I have a lot of data?

Guide contributeur

Stack technique
javascripttypescriptnodejs
Domaine
machine learningai
Type d'issue
research
DifficultéDifficulté estimée pour un nouveau contributeur, de 1 pour un très petit changement à 5 pour un travail expert.
3
Temps estiméFourchette de temps approximative pour investiguer, implémenter, tester et préparer une pull request.
1-3 hours
Statut d'activitéDisponibilité apparente de l'issue : fraîche, active, ancienne, bloquée ou en attente d'un mainteneur.
stale
ClartéClarté avec laquelle l'issue explique le changement attendu, les critères d'acceptation et la prochaine étape.
needs investigation
Prérequis
basic JavaScriptneural network conceptsbrain.js basics
Accessibilité débutantScore de 1 à 100 estimant l'accessibilité de cette issue pour un premier contributeur.
40
Direction de recherche
This issue is about troubleshooting a sentiment detector using brain.js. The user reports that with large datasets, the output becomes garbled. To investigate, first review brain.js documentation on training recurrent neural networks and data preprocessing. Check if there are any known issues with large datasets in brain.js (e.g., memory constraints or normalization problems). Look at the example linked in the issue to see if there's a preprocessing step missing. Test with a smaller dataset to compare behavior, and consider whether the network architecture (e.g., layers, neurons) needs adjustment for larger data.