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**Title: Implement Sentiment Analysis on Twitter Data**
**Description:**
This issue covers the implementation of a sentiment analysis pipeline for Twitter data using machine learning models.…
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Right now we've got URL extraction, language extraction, hashtag extraction, and image extraction. We should have a few more features documented. I think this could begin with:
- plain text extraction…
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Re discussion with Josh Minot, want to use a logistic regression model to identify some words and potentially ngrams which can be used to differentiate Q and non-Q content on twitter, purpose being to…
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Right now, our script for URL extraction is as follows:
```
import org.warcbase.spark.matchbox._
import org.warcbase.spark.matchbox.TweetUtils._
import org.warcbase.spark.rdd.RecordRDD._
val tweets …
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* Article publication date (to supplement/replace tweet first_occurrence)
* Article keywords
* Article summary
* Article top image
.. This library looks awesome.
https://github.com/codelucas/…
dmarx updated
7 years ago
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Aggiungere qua i link utili per documentazioni e varie..
Analisi testo / parole chiave / topic:
- https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation
- https://medium.com/@osas.usen/topic-…
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I was thinking that simple methodology would be to generate sequences for sentiment spans and measure accuracy based on some *overlap* measure, e.g. Jaccard, or rougue.
While imperfect, this would …
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Why did I choose this paper? Because it analyzes the effect of tweet length on topic modeling methods.
### Main problem:
Which model is better for topic detection in the short text (tweet)?
Does …
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### Deep Learning Simplified Repository (Proposing new issue)
:red_circle: **Project Title** : Classification of Elon Musk Tweets using NLP
:red_circle: **Aim** : Create a classification model using…
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OCTIS version: 1.11.0
Python version: 3.8.15
Operating System: 'posix'
### Description - What I Did
I read in my own data and save it as .txt with one document per line. Then I define the prep…