Personalized Recommendations Based on Sentimental Interest Community Detection

Zheng, Jianxing; Wang, Yanjie

doi:10.1155/2018/8503452

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…Recently, sentiment analysis also gained popularity in community detection frameworks. In [45], a recommendation model, resonant sentimental interest community, is proposed to compute the resonance relationship among users for community detection. The proposed system integrates sentiment and semantics relationships among users.…”

Section: Related Workmentioning

confidence: 99%

Class Association and Attribute Relevancy Based Imputation Algorithm to Reduce Twitter Data for Optimal Sentiment Analysis

et al. 2019

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Twitter sentiment analysis is a challenging task that involves various preprocessing steps including dimensionality reduction. Dimensionality reduction helps ensure low computational complexity and performance improvement during the classification process. In Twitter data, each tweet has feature values which may or may not reflect a person's response. Therefore, a large number of sparse data points are generated when tweets are represented as feature matrix, eventually increasing computational overheads and error rates in Twitter sentiment analysis. This study proposes a novel preprocessing technique called class association and attribute relevancy based imputation algorithm (CAARIA) to reduce the Twitter data size. CAARIA achieves the dimensionality reduction goal by imputing those tweets that belong to the same class and also share useful information. The performance of two classifiers (Naïve Bayes and support vector machines) is evaluated on three Twitter datasets in terms of classification accuracy, measured as area under curve, and time efficiency. CAARIA is also compared against two widely used feature selection (dimensionality reduction) techniques, information gain (IG) and Pearson's correlation (PC). The findings reveal that CAARIA outperforms IG and PC in terms of classification accuracy and time efficiency. These results suggest that CAARIA is a robust data preprocessing technique for the classification task.

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Section: Related Workmentioning

confidence: 99%

Class Association and Attribute Relevancy Based Imputation Algorithm to Reduce Twitter Data for Optimal Sentiment Analysis

et al. 2019

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“…Gu et al [31] propose an approach that uses content in microblog or tweets of users and users' social network preferences (popularity) for news recommendations. Also, content in microblogs is taken into account for recommendations by Zheng and Wang [32], but from a sentimental point of view. Retweeting of news are the implicit activities considered in a content-based recommendation system that models user trends [33].…”

Section: State-of-the-artmentioning

confidence: 99%

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

Hernández

Pérez

Granados

et al. 2019

Scientific Programming

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This paper addresses the problem of automatically customizing the sending of notifications in a nondisturbing way, that is, by using only implicit-feedback. Then, we build a hybrid filter that combines text mining content filtering and collaborative filtering to predict the notifications that are most interesting for each user. The content-based filter clusters notifications to find content with topics for which the user has shown interest. The collaborative filter increases diversity by discovering new topics of interest for the user, because these are of interest to other users with similar concerns. The paper reports the result of measuring the performance of this recommender and includes a validation of the topics-based approach used for content selection. Finally, we demonstrate how the recommender uses implicit-feedback to personalize the content to be delivered to each user.

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