BrainJS/brain.js

Proper way to detect sentiments?

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#366 opened on Feb 20, 2019

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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?

Contributor guide

Tech stack
javascripttypescriptnodejs
Domain
machine learningai
Issue type
research
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
needs investigation
Prerequisites
basic JavaScriptneural network conceptsbrain.js basics
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
40
Research direction
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.