-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
sys.path.append('/content/social-network-url-clustering/src')
from data_loading.interactions import get_metadata,…
-
Hi,
This is a great project, thanks!
I've been playing with it using Observables. A proof of concept of in browser text analysis of youtube captions, is still [in progress not yet published](https:…
-
#ClimateChange 2000 tweets analysis
1. TF word clouds Unigram Bigram Trigram
2. TF-IDF word cloud
3. Sentiment analysis (any one lexicon)
4. Comparison/Contrast word clouds based on sentiment
5.…
-
**Is your feature request related to a problem? Please describe.**
The goal of this issue is to enrich the text analysis of dataprep.eda.
**Describe the solution you'd like**
1. plot(df, x): me…
-
-
Bullish Sentiments: 17,368
Bearish Sentiments: 8,542
Neutral Sentiments: 12,181
It would be nice if we could balance the datasets and increase them all, to for instance 25k each
- [ ] Find bul…
-
Hi Elliot,
First of all, thank you for interesting project!
However, looks like there is a bug here.
It's said in Readme:
> Marisa-trie is used to make the final trained model.pkl memory-e…
q0o0p updated
4 years ago
-
Thinking about ways to describe text topics.
The "topics" we'll try to explain are those defined by text in a given _cluster_. Text could be an individual or a group of (even all) essay responses&mda…
-
# Pitch
## Summary
This week I want to analyze dialogues from The Office. I found a data set containing every line that every character has said in every scene of every episode of the TV show. I…
-
Submitting Author: Jerry Yu (@jy1909 )
All current maintainers: (@MoNorouzi23, @allan8392, @nassimgha)
Package Name: text_processing_util_mds24
One-Line Description of Package: This package is desi…