Text-to-Insights Dashboard

 Text-to-Insights Dashboard 

1. Introduction

Objective: Develop a dashboard that processes textual paragraphs and extracts key insights in the form of keywords, topics, or phrases.
Purpose: Simplify textual data analysis for non-technical users by presenting clear and concise insights from paragraphs.

2. Project Workflow

1. Problem Definition:
   - Extract key information from textual data for better decision-making.
   - Ensure the insights are clear, accurate, and relevant.
2. Data Collection:
   - Source: News articles, user feedback, research papers, or other textual documents.
3. Data Preprocessing:
   - Tokenization, stopword removal, lemmatization, and named entity recognition (NER).
4. Key Insight Extraction:
   - Methods: TF-IDF, topic modeling (LDA), or transformer-based NLP models.
5. Dashboard Design:
   - Visualize keywords, topics, and statistics in an interactive user interface.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
  - NLP: NLTK, SpaCy, Scikit-learn, Gensim, Hugging Face Transformers
  - Visualization: Streamlit, Plotly, Matplotlib
  - Data Handling: Pandas, NumPy
  - Dashboard Development: Streamlit or Dash

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy matplotlib gensim spacy streamlit sklearn transformers
```
Download NLP resources:
```
import nltk
nltk.download('punkt')
nltk.download('stopwords')

import spacy
spacy.cli.download("en_core_web_sm")
```

Step 2: Load and Preprocess Text Data

Load and clean the data:
```
import pandas as pd

data = pd.read_csv("text_data.csv")
print(data.head())
```
Preprocess the text:
```
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

stop_words = set(stopwords.words('english'))

def preprocess(text):
    words = word_tokenize(text.lower())
    filtered_words = [word for word in words if word.isalpha() and word not in stop_words]
    return " ".join(filtered_words)

data['cleaned_text'] = data['paragraph'].apply(preprocess)
```

Step 3: Extract Insights with TF-IDF

Generate keyword weights:
```
from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(max_features=10)
tfidf_matrix = vectorizer.fit_transform(data['cleaned_text'])
data['keywords'] = [
    ", ".join(vectorizer.get_feature_names_out()[tfidf_matrix[i].toarray().flatten().argsort()[-5:]])
    for i in range(tfidf_matrix.shape[0])
]
```

Step 4: Topic Modeling with LDA

Identify hidden topics:
```
from gensim import corpora
from gensim.models import LdaModel

texts = [text.split() for text in data['cleaned_text']]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

lda_model = LdaModel(corpus, num_topics=3, id2word=dictionary, passes=15)
topics = lda_model.print_topics(num_words=5)
print(topics)
```

Step 5: Build Interactive Dashboard

Create a Streamlit dashboard:
```
import streamlit as st

st.title("Text-to-Insights Dashboard")

uploaded_file = st.file_uploader("Upload Text File", type=["csv"])
if uploaded_file:
    data = pd.read_csv(uploaded_file)
    st.dataframe(data.head())

    data['cleaned_text'] = data['paragraph'].apply(preprocess)
    data['keywords'] = [
        ", ".join(vectorizer.get_feature_names_out()[tfidf_matrix[i].toarray().flatten().argsort()[-5:]])
        for i in range(tfidf_matrix.shape[0])
    ]
    st.dataframe(data[['paragraph', 'keywords']])
```

5. Expected Outcomes

1. A functional dashboard that accepts textual data and outputs key insights.
2. Visualization of keywords, topics, and statistical metrics in an interactive format.
3. Simplified process for extracting actionable insights from large textual datasets.

6. Additional Suggestions

- Extend the dashboard to support real-time data streams such as APIs or live feeds.
- Experiment with transformer models like BERT for better contextual keyword extraction.
- Allow users to download the insights as CSV or JSON files.