Steps for Content Analysis via Desktop Export:

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Export Your Data: Use Telegram Desktop to export your chat history in JSON format. This provides a structured, machine-readable representation of your conversations, including message text, sender information, timestamps, and media metadata.
Choose Your Analysis Tool: Python is a versatile language with excellent libraries for data analysis (e.g., Pandas, NLTK). You can also use tools like R or even russia telegram phone number list spreadsheet software for basic analysis.
Parse the JSON Data: Use Python's json library (or equivalent in other tools) to read and parse the exported JSON files. This will allow you to access the message content and associated metadata.
Perform Your Analysis: Depending on your goals, you can then use various techniques:
Keyword Counting: Iterate through the message text and count the occurrences of different words.
Time-Based Analysis: Analyze message timestamps to understand communication frequency over days, weeks, or months.
Sender/Receiver Analysis: Track the number of messages sent and received by different participants in your chats.
Basic Sentiment Analysis (with external libraries): Integrate Python libraries like VADER or TextBlob to perform sentiment analysis on the message text.