A Study of User Text Sentiment Dictionary for Food Recommendation Service on Big Data Environment (빅데이터 환경에서의 음식 추천 서비스를 위한 사용자 텍스트 감성사전에 관한 연구)

Cho Jin-Kwan (조진관)

doi:10.34163/jkits.2019.14.4.002

A Study of User Text Sentiment Dictionary for Food Recommendation Service on Big Data Environment

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2019, 14(4), pp.327-334
DOI : 10.34163/jkits.2019.14.4.002
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Received : June 6, 2019
Accepted : August 9, 2019
Published : August 31, 2019

Cho Jin-Kwan ¹

¹경동대학교

Accredited

ABSTRACT

In general, it is practice to assess food tastes based on sensory tests, however, this method has a considerable disadvantage in which it is pricey and need to required more time. In addition, important disparity appear in relying on each evaluator. We make a good food tastes up, we are necessary to process as following; Firstly, a pre-taste User text sentiment dictionary is based on establishment a kind of food studies and then gather this information on twitter data of social network service, internet and social media so on. Secondary, many information are based on anticipate in original food tastes data by web-based and mobile-based from their systems. Food name of providing on social network service and internet is different each of names with their food tastes. After all, This data is divided into four different names look over stemming that new savors and foods words are found in order to add keyword to taste sentiment dictionary. Accordingly, frequency measurement of newly formated taste keyword is important based on sensitivity of filtering data by utilizing a pre-taste word emotional dictionary determined by the weight of the food taste keyword and it also illustrates the taste of foods by taste dictionary.

KEYWORDS

Food tastes, User text sentiment dictionary, Twitter data of social network of service, Internet and social media, Filtering data

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* References for papers published after 2023 are currently being built.

[journal] K. Dave / 2003 / Mining the peanut gallery: opinion extraction and semantic classification of product reviews / 2(3) : 519~528

[confproc] C. Ding / 2006 / Orthogonal nonnegative matrix t-factorizations for clustering / KDD ’06 : 126~135

[confproc] C. Ding / 2005 / On the equivalence of nonnegative matrix factorization and spectral clustering / Proceedings of the SIAM Data Mining Conference : 606~610

[journal] C. Ding / 2008 / On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing / Computation Statistics and Data Analysis 52(8) : 3913~3927

[confproc] A. Esuli / 2006 / Sentiwordnet: A publicly available lexical resource for opinion mining / LREC06 3(2) : 417~422

[confproc] Hatzivassiloglou / 1997 / Predicting the semantic orientation of adjectives / EACL ’97 : 174~181

[journal] Hofmann / 2001 / Unsupervised learning by probabilistic latent semantic analysis / Machine Learning 42(1) : 177~196

[confproc] M. Hu / 2004 / Mining and summarizing customer reviews / KDD ’04 : 168~177

[other] K. Marx / 2004 / Using wordnet to measure semantic orientation of adjectives / National Institute : 1115~1118

[journal] D. Lee / 1999 / Learning the parts of objects by non-negative matrix factorization / Nature 401(6755) : 788~791

[confproc] T. Li / 2007 / Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization / ICDM ’07 : 577~582

[confproc] B. Pang / 2002 / Thumbs up?: sentiment classification using machine learning techniques / EMNLP ’02 : 79~86

[confproc] P. D. Turney / 2002 / Thumbs up or thumbs down?:semantic orientation applied to unsupervised classification of reviews / ACL ’02 : 417~424

[confproc] T. Wilson / 2005 / Recognizing contextual polarity in phrase-level sentiment analysis / HLT ’05 : 347~354

[confproc] W. Xu / 2003 / Document clustering based on non-negative matrix factorization / SIGIR ’03 : 267~273

[confproc] A. Esuli / 2006 / SentiWordnet: A publicly available lexical resource for opinionmining / Proceedings of the 5th Conference on Language Resources and Evaluation (LREC06) : 417~422

[journal] S. Mohammad / 2013 / Crowdsourcing a word-emotion association lexicon / Computational Intelligence 29(3) : 436~465

[confproc] X. Vum / 2014 / Construction of Vietnamese Sentiwordnet by using Vietnamese dictionary / The 40th Conference of the Korea Information Processing Society : 745~748

[confproc] S. Baccianella / 2010 / Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining / Proceedings of LREC 10 : 17~23

[confproc] M. Hu / 2004 / Mining and Summarizing customer reviews / Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD04) : 168~177

[journal] P. J. Stone / 1968 / The general inquirer: A computer approach to content analysis / American Journal of Sociology 73(5) : 634~635

[confproc] B. T. Kieu / 2010 / Sentiment, analysis for Vietnamese / 2010 Second International Conference on Knowledge and Systems Engineering : 152~157

[confproc] T. Vu / 2018 / VnCoreNLP: A Vietnamese Natural language processing toolkit / Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics, Demonstrations : 56~60

[confproc] T. D. Nguyen / 2014 / An empirical study on sentiment analysis for Vietnamese / 2014 International Conference on Advanced Technologies for Communications (ATC 2014) : 309~314

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Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Study of User Text Sentiment Dictionary for Food Recommendation Service on Big Data Environment

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Citation status

* References for papers published after 2023 are currently being built.

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Study of User Text Sentiment Dictionary for Food Recommendation Service on Big Data Environment

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.