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صفحه اصلی
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سی امین کنفرانس بین المللی مهندسی برق
Job Title Prediction from Tweets Using Word Embedding and Deep Neural Networks
نویسندگان :
Shayan Vassef
1
Ramin Toosi
2
Mohammad Ali Akhaee
3
1- دانشگاه تهران
2- دانشگاه تهران
3- دانشگاه تهران
کلمات کلیدی :
text classification،social media analysis،Job title prediction،Twitter analysis
چکیده :
The more social media take its place in our lives, the more important their analysis becomes and the more researchers’ attention is drawn to it. Studies contain various topics such as sentiment analysis, trend prediction, bot detection, etc. Here, for the first time, we propose a novel method to predict the job title of social media users. Twitter, a popular social media, is our target social media. We introduce a dataset consisting of 1314 samples, including users’ tweets and bios. The user's job title is found using Wikipedia crawling. The challenge of multiple job titles per user is handled using a semantic word embedding and clustering method. Then, a job prediction method based on a deep neural network and TF-IDF word embedding is introduced. We also take the advantage of hashtags and emojis in the tweets for job prediction. Results show that the job title of users in Twitter could be well predicted with 54\% accuracy in nine categories.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.7.2
