Clustering Arabic Tweets for Saudi National Vision 2030

Ferwana, Ibtihal

doi:10.30534/ijatcse/2019/3781.42019

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The word embedding that has been obtained through the use of Word2vec, will be fitted in the classification model by the use of a vectorizer [18]. The classifier will transform features obtained from the word embedding process into a high dimensional feature space [19]. The obtained comment data from play.google.com website is split into training data and testing data with a ratio of 80:20 respectively.…”

Section: Svm Algorithmmentioning

confidence: 99%

Classification of User Comment Using Word2vec and SVM Classifier

Kurnia¹

2020

IJATCSE

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Social media provides data that can be used for text classification, but these social media do not have a rating system. Comments taken from social media such as Twitter, does not provide a rating system which can help to classify comments based on their rating score. The goal of this study is to build a comment classification model using Word2vec and SVM classifier that can classify comments based on a rating scale from 1-5. The training data will be taken from user comments from play.google.com website in which each comment already has a rating. The purpose of the model is to classify comments from social media about mobile network applications. Comment classification is performed in order to help these businesses, as well as the users, to know the overall satisfaction of users who use these applications. The best F1 score obtained from this research using class elimination and stop word removal is 0.795.

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Section: Svm Algorithmmentioning

confidence: 99%

Classification of User Comment Using Word2vec and SVM Classifier

Kurnia¹

2020

IJATCSE

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“…For instance LinkedIn is a platform for professionals to display their profile. Also Twitter is a social media platform where user can express their thought, feeling and decision [9]. RSs vary in the manner they break down information to create ideas of relationship between users and items.…”

Section: Basic Conceptsmentioning

confidence: 99%

Towards A Job Recommender Model: An Architectural-Based Approach

Adeagbo¹

2019

IJATCSE

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The increase in the number of job Websites that have a multiplier effect on the number of online job information, especially in Nigeria is causing information overload in the area of information management for those seeking for a job. There is a need to fetch job information from different job Websites and suggest job information based on the profile of the user. In the architecture presented in this paper, the user profiles was extracted from social networks and job information from job Websites in Nigeria. Jaccard Similarity Coefficient was used to measure the similarity between terms in the profile of the user and job information to make a recommendation of the job for each user. The architecture complies with service-oriented architecture principles and drew on the Jaccard coefficient for similarity measurement, and content-filtering retrieval feature for the recommendation for its formulation. In determining the preset threshold, similarity coefficients of 275 sample of job information were measure against 10 user profile using classification accuracy. The threshold was set to 0.1 and varied up to 1 per job information to determine the point at which the level of correctness was the highest. The result showed that the lowest level of correctness was achieved at 0.1 thresholds while the highest level of correctness was achieved at 0.8 thresholds. The implementation of job Recommender application(s) from this architecture was able to reduce the problem of information overload and asymmetry. Users of the implemented application was also freed from the problem of subscribing to more than one job website.

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Clustering Arabic Tweets for Saudi National Vision 2030

Cited by 2 publications

References 8 publications

Classification of User Comment Using Word2vec and SVM Classifier

Classification of User Comment Using Word2vec and SVM Classifier

Towards A Job Recommender Model: An Architectural-Based Approach

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