Finding user personal interests by tweet-mining using advanced machine learning algorithm in R

Krithika, L. B.; Roy, Pradeep Kumar; Jerlin, M. Asha

doi:10.1088/1757-899x/263/4/042071

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…The studies that have been done previously have analyzed users' interests from the information on Twitter [2]- [7]. Makki et al [8] by inducing the users' interests from their profile information which is written by the users themselves, and some also [9], [10] consider the users' tweets.…”

Section: A Tweet Attribute Extractionmentioning

confidence: 99%

Twitter User’s Hobby Estimation Based on Sequential Statements Using Deep Neural Networks

Bando¹,

Matsumoto²,

Yoshida³

et al. 2019

IJMLC

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With more and more frequency, users communicate with each other on social media. Many users start on Twitter or Facebook to find friends who have the same hobby. Our study proposes a method to estimate the users' interests (hobby) based on tweets on Twitter. One tweet does not, in and of itself, contain a lot of information, and some tweets are not related to the user's hobby. Therefore, we propose a reliable hobby estimation method by extracting features from multiple, sequential tweets. The proposed method uses Recurrent Neural Networks (RNN) which can accommodate time-series information. We also used a Convolutional Neural Networks (CNN) which can treat contextual information. We used an averaged vector of word distributed representation as a feature. Using the proposed method based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), we obtained a 23.72% improvement as compared with a baseline method using a Random Forest (RF) regression as a machine learning algorithm.

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Section: A Tweet Attribute Extractionmentioning

confidence: 99%