How to Use Telegram Data to Build Custom Analytics

[fatimahislam]

Building custom analytics from Telegram data allows individuals and organizations to gain deeper, more tailored insights than generic platform statistics can offer. This process involves extracting raw data, processing it, and then applying various analytical techniques to identify trends, user behavior, and content performance specific to your needs. This is particularly valuable for community managers, researchers, or anyone running a large Telegram group or channel.

1. Data Acquisition: The Essential First Step
The foundation of custom analytics is raw data. For Telegram, the telegram data most effective method is using the Telegram Desktop application's "Export Telegram Data" feature.

Crucial Format: Always select the "Machine-readable JSON" format. This structured data is easily parsed by programming languages, making it ideal for automation and in-depth analysis. While HTML is good for quick visual checks, it's not suitable for programmatic analysis.
Targeted Export:
Chats/Channels/Groups: Select the specific conversations you want to analyze. This will give you messages, timestamps, sender IDs, and more.
Media and Files: Including these allows you to analyze not just text, but also the types and frequency of shared content.
Time Range: If you're interested in a specific period, define it to manage the data volume.
2. Data Preprocessing: Transforming Raw Data into Usable Format
Once you have the JSON files, they need to be cleaned and structured. This typically involves scripting, with Python being the preferred language due to its strong data handling and NLP libraries (e.g., pandas, json, nltk, spaCy).

Parsing JSON: Write a Python script to open and parse the .json files. Extract relevant fields from each message object, such as:
id: Unique message identifier.
date: Timestamp (crucial for time-series analysis).
type: e.g., message, service, photo.
from: Sender's display name.
from_id: Unique user ID (essential for tracking individual users).
text: The message content itself.
media_type: For non-text messages.
Structuring Data: Convert the parsed data into a pandas DataFrame. This tabular format is ideal for manipulation, filtering, and aggregation.
Example: A DataFrame with columns like timestamp, user_id, username, message_text, message_type.
Data Cleaning (Textual Data):
Lowercasing: Standardize text (e.g., "Hello" and "hello" become "hello").
Remove Punctuation/Special Characters: Clean text to focus on words.
Remove Stop Words: Eliminate common words ("the," "is," "a") that add little analytical value.
Tokenization: Break sentences into individual words.
Lemmatization/Stemming: Reduce words to their base form (e.g., "running," "ran" → "run"). This groups similar words.
Handling Emojis: Decide whether to remove, normalize, or analyze emoji usage.
3. Building Custom Analytics: Uncovering Insights
With clean, structured data, you can build a wide array of custom analytics:

Activity Metrics:
Total Messages Over Time: Plot daily, weekly, or hourly message counts to identify peak activity and quiet periods.
Active Users: Count unique user_ids per period to see participation levels.
Message Per User: Calculate the average number of messages sent per active user.
Most Active Users: Identify top contributors based on message count.
Content Analytics:
Frequent Keywords/Phrases: Use tokenization and frequency counts to identify dominant topics. N-grams (sequences of words) can reveal common phrases.
Word Clouds: A visual representation of common words, highlighting prevalent themes.
Sentiment Analysis: Apply NLP models (e.g., pre-trained models from TextBlob, VADER, or spaCy) to gauge the emotional tone of messages. This can show overall sentiment or sentiment shifts around specific topics.
Content Type Distribution: Analyze the ratio of text messages, photos, videos, stickers, and files to understand preferred communication mediums.
Topic Modeling (Advanced): Use algorithms like LDA (Latent Dirichlet Allocation) to automatically discover hidden themes or "topics" within large text datasets. This is powerful for understanding the discourse without prior knowledge of topics.
User Behavior Analysis:
Engagement Patterns: Analyze reply patterns, mentions (@username), and quote usage to understand how users interact with each other.