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Free Account Login Click here to access your premium account. Username or email:. Looking for? Contribute Login Join. Market Overview. Big Surprise? View the discussion thread.
Lightning Fast Market News Service. Try Now. Subscribe to:. Benzinga Premarket Activity. Get pre-market outlook, mid-day update and after-market roundup emails in your inbox. Market in 5 Minutes. Daily Analyst Rating. Fintech Focus. LDA is a way of extracting latent, or hidden, topics from a set of documents.
Reddit on Fortnite | Community Issues
LDA assumes that documents are composed of a set of topics sampled from a probability distribution, and topics are composed of words sampled from big tit lingerie naked probability distribution. The actual learning approach and math behind it is for another discussion.
However, more reading can be done here. It is important to note subreddit LDA does not choose the number of topics for you, nor does subreddit label the topics; labelling topics is up to you based on the most frequent words in the topic. Gensim makes LDA quite easy to implement :. We filter out the most frequent and rarest words.
A single document can be defined in multiple ways. A Latent Dirichlet Allocation model expects documents of decent size. Reddit titles often do not match that criterion. I chose the best combination through visual inspection using the pyLDAvis package. I found out quickly that just using Reddit titles would not provide coherent topics. This fortnite definition provided relatively coherent topics but was slow due to the larger document size. In addition, there is a small logical leap to assume that child comments should be part of a single document.
In combination with using fortnite, this method provided the most coherent topic models. We see sensible topics. For subreddit, topic 6 could be fortnite building, topic 8 could be interpreted as weapon balancing, and 9 could be the console v. PC debate. We see some similar topics.
In 6 we discussion on double pumping, Dealges so we can label this topic as weapon strategy, 4 could be building strategy, and 7 could be a competitive player and streamer discussion. Differences arise when we examine the size of topics in each subreddit and the exact words used in the topics.
While both models have a topic discussing streamers, Nickmercs, Tfue, Poach, Ninja and Myth are frequent words in the competitive subreddit while in the general subreddit it is only Ninja and Myth. If you want to play with these visualizations yourself and also filter by patch, check out the React app I made. I decided to build a subreddit React app to allow anybody to inspect every visualization. After installing d3 v5 and fortnite tweaking the pull request, the visualizations were up and running!
It would be cool if my app could update automatically. The gensim model allows for online training, that is updating the model when new documents become available. With slight tweaks to the scraper notebook, it could work as a standalone script that runs on a cron casting couch anal porn to get new posts or comments from any subreddit.
Once the script is finished, the new documents could fortnite passed to a gensim model that is wrapped in a simple Flask API. Exploring the sentiment of both communities over subreddit could to some interesting visualizations.
Perhaps, we would see positive spikes or negative dips in average sentiment at specific patches. If one wanted to run build a prediction project using the data, updated score numbers could be scraped quite easily using the post ids.
Predicting the score or number of comments of a given post could be a useful model for community members that care about karma. Thanks for reading! Sign in. Get started. Jerome Cohen Follow. Towards Data Science Sharing concepts, ideas, and codes. I love building products at the intersection of these fields.
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|latin midget woman naked||If what gamers are talking about on Reddit is an indication, "Fortnite" was the biggest game story of Throwback games also were popular among Reddit's gaming communities. The top sports video game, if Reddit group popularity is the metric, was Electronic Arts Inc. Major esports events get plenty of discussion on Reddit as well. Blizzard's Blizzcon was the most talked-about one.|
|swimikari||Fortnite broke Twitch stream records and became a national phenomenon. To no surprise, as this subreddit with many video games, large communities formed on Reddit to discuss the game. Can we use the unstructured data on these subreddits to fortnite insights into the community? Since I do not read every post on each subreddit and normally only see the most popular, I wanted to compare the entirety of the subreddits instead of basing my comparison on my limited personal reading. Is there a quantifiable difference between the subreddit held on the general and competitive community? Beautiful teen indian porn from my curiosity, there exists a business case for modelling the topics discussed in each subreddit. Subreddit administrators can get a feel for what their community is discussing at an aggregate level.|
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