README.md

metadata

title: README
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title: "Tachygraphy Micro-text Analysis & Normalization" emoji: "⚡" colorFrom: "pink" colorTo: "blue" sdk: "static" pinned: false

Tachygraphy Micro-text Analysis & Normalization

Welcome to the Tachygraphy Micro-text Analysis & Normalization Project. This page outlines our project’s key stages, sources, sample analysis examples, and team information.

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Dashboard

Project Stages

1. Sentiment Polarity Analysis
2. Emotion Mood-tag Analysis
3. Text Transformation & Normalization
4. Stacked all 3 stages with their best models
5. Data Correction & Collection

Sources & Deployment Links

Training Source	Kaggle Collections	Hugging Face Org
GitHub @ Tachygraphy Micro-text Analysis & Normalization	Kaggle Dataset	Hugging Face @ Tachygraphy Micro-text Normalization

Deployment Source	Streamlit Deployment	Hugging Face Space Deployment
GitHub Deployment Repo	Streamlit App	Hugging Face Space

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Project Overview

Tachygraphy—originally developed to expedite writing—has evolved over centuries. In the 1990s, it reappeared as micro‑text, driving faster communication on social media with its “Anytime, Anyplace, Anybody, and Anything (4A)” characteristic. This project focuses on the analysis and normalization of micro‑text (the prevalent informal communication today) to improve NLP tasks such as sentiment analysis, emotion detection, and overall text transformation for clear 4A message decoding.

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Sample Examples

Sample Example 1

Below is a Graphviz diagram illustrating a sample analysis:

digraph {
    graph [bgcolor="white", rankdir=TB, splines=true, nodesep=0.8, ranksep=0.8];
    node [shape=box, style="rounded,filled", fontname="Helvetica", fontsize=9, margin="0.15,0.1"];

    Input [label="Input:\nbruh, floods in Kerala, rescue ops non‑stop 🚁", fillcolor="#ffe6de", fontcolor="#000000"];
    Output [label="Output:\nBrother, the floods in Kerala are severe,\nand rescue operations are ongoing continuously.", fillcolor="#ffe6de", fontcolor="#000000"];
    Sentiment [label="Sentiment:\nNEUTRAL", fillcolor="#ecdeff", fontcolor="black"];

    Anger [label="Anger: 0.080178231", fillcolor="#deffe1", fontcolor="black"];
    Disgust [label="Disgust: 0.015257259", fillcolor="#deffe1", fontcolor="black"];
    Fear [label="Fear: 0.601871967", fillcolor="#deffe1", fontcolor="black"];
    Joy [label="Joy: 0.00410547", fillcolor="#deffe1", fontcolor="black"];
    Neutral [label="Neutral: 0.0341026", fillcolor="#deffe1", fontcolor="black"];
    Sadness [label="Sadness: 0.245294735", fillcolor="#deffe1", fontcolor="black"];
    Surprise [label="Surprise: 0.019189769", fillcolor="#deffe1", fontcolor="black"];

    edge [color="#7a7a7a", penwidth=3];

    Input -> Output;
    Input -> Sentiment;
    Sentiment -> Anger;
    Sentiment -> Disgust;
    Sentiment -> Fear;
    Sentiment -> Joy;
    Sentiment -> Neutral;
    Sentiment -> Sadness;
    Sentiment -> Surprise;
}

Sample Example 2

digraph { graph [bgcolor="white", rankdir=TB, splines=true, nodesep=0.8, ranksep=0.8]; node [shape=box, style="rounded,filled", fontname="Helvetica", fontsize=9, margin="0.15,0.1"];

Input [label="Input:\nu rlly think all that talk means u tough? lol, when I step up, u ain't gon say sh*t", fillcolor="#ffe6de", fontcolor="black"];
Output [label="Output:\nyou really think all that talk makes you tough [lol](laughed out loud) when i step up you are not going to say anything", fillcolor="#ffe6de", fontcolor="black"];
Sentiment [label="Sentiment:\nNEGATIVE", fillcolor="#ecdeff", fontcolor="black"];

Anger [label="Anger: 0.14403291", fillcolor="#deffe1", fontcolor="black"];
Disgust [label="Disgust: 0.039282672", fillcolor="#deffe1", fontcolor="black"];
Fear [label="Fear: 0.014349542", fillcolor="#deffe1", fontcolor="black"];
Joy [label="Joy: 0.048965044", fillcolor="#deffe1", fontcolor="black"];
Neutral [label="Neutral: 0.494852662", fillcolor="#deffe1", fontcolor="black"];
Sadness [label="Sadness: 0.021111647", fillcolor="#deffe1", fontcolor="black"];
Surprise [label="Surprise: 0.237405464", fillcolor="#deffe1", fontcolor="black"];

edge [color="#7a7a7a", penwidth=3];

Input -> Output;
Input -> Sentiment;
Sentiment -> Anger;
Sentiment -> Disgust;
Sentiment -> Fear;
Sentiment -> Joy;
Sentiment ->

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