Twitter message recommendation based on user interest profiles

Makki, Raheleh; Soto, Axel J.; Brooks, Stephen; Milios, Evangelos

doi:10.1109/asonam.2016.7752266

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Expected outcome of this project is providing clutter free group environment to Twitter users. With the help of various aspects based clustering and the location-based clustering [2] the posts with the help of personalized notifications. Finally, formulating groups of individuals within a group sharing similar interests.…”

Section: Resultsmentioning

confidence: 99%

“…The paper [2] proposes a TWIMER framework the use of language models as a basis for analyzing strategies and techniques for tweet advice based on person's interest profiles. TWIMER consists of In [3] paper, collaborative topic Poisson factorization (CTPF) can be used to build recommender systems through gaining knowledge of from reader histories and content material to advocate personalized articles of hobby.…”

Section: Review Of Literaturementioning

confidence: 99%

“…The psycholinguistic differences contribute to the nature of the posts and hence there will be difference in the audience based on the psycholinguistic perspective of members within a group. Psycholinguistic aspects throw light on various factors that vary based on a person's personality, hobbies, passions, intellects, perception and context of references [2]. Thus it reflects a person's way of responding to a post.…”

Section: ) Psycholinguistic Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

Linguistic-Based and user-Based Recommending Posts using Two-Level Clustering Methods

Joag*¹,

Dalvi²

2019

IJITEE

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Online social networks have produced bunches of online social groups where individuals can collaborate and shuffle their thoughts. In spite of, the real problems that conflicts with the user security and convenience are confidentiality break, groups without inception, confusion created from various groups of which user is a member of and difficulty in moderating groups. This can be moderated to an extent by an automated filtering method required to categorizing group members based on their response patterns. This paper proposes clustering of group posts on stylistic, thematic, emotional, sentimental and psycholinguistic methods and members of the group are categorized based on their responses to the posts belonging to different clustering methods. The categorization affords security just like the conflict associated with irrelevant notifications received from more than one groups, via recommending the users, posts which might be probable to be of interest to them. It also helps to identify the group members meant closer to spreading posts that violate group policies. The categorization post shows increased performance where there are large numbers of members in a social group by performing linguistic clustering. The contribution work is to implement location-aware personalized posts recommendation using users’ behavioral patterns and their geographic location. Another, important work is to implement text-to-speech system converting English text into speech using speech synthesis technique. The system gives rating to the users who shares posts depending on clustering. Also system provides read later functionality to the user side. The system has been tested on Twitter API group data where a significant solution to an unaddressed problem associated with social networking groups is offered.

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Section: Resultsmentioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

Section: ) Psycholinguistic Clusteringmentioning

confidence: 99%

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Linguistic-Based and user-Based Recommending Posts using Two-Level Clustering Methods

Joag*¹,

Dalvi²

2019

IJITEE

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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. Kapanipathi et al [11] proposed the user interests identification method based on a hierarchical relationships present in knowledge-bases.…”

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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Memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks

Arokiaraj

Prasad

2023

Lecture Notes in Electrical Engineering

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Twitter message recommendation based on user interest profiles

Cited by 8 publications

References 16 publications

Linguistic-Based and user-Based Recommending Posts using Two-Level Clustering Methods

Linguistic-Based and user-Based Recommending Posts using Two-Level Clustering Methods

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

Memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks

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