Exploring Insurance and Natural Disaster Tweets Using Text Analytics

This study explores twitter data about insurance and natural disasters to gain business insights using text analytics. The program R was used to obtain tweets that included the word ‘insurance' in combination with other natural disaster words (e.g., snow, ice, flood, etc.). Tweets related to six top Canadian insurance companies as well as the top five insurance companies from the rest of the world, including the new entrant Google Insurance, was collected for this study. A total of 11,495 natural disaster tweets and 19,318 insurance company tweets were analyzed using association rule mining. The authors' analysis identified several strong rules that have implications for insurance products and services. These findings show the potential text mining applications offer for insurance companies in designing their products and services.

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A Novel Market Basket Analysis Using Adaptive Association Rule Mining Algorithm

International Journal of Scientific Research ◽

10.15373/22778179/sep2012/9 ◽

2012 ◽

Vol 1 (4) ◽

pp. 25-28

Author(s):

M.Dhanabhakyam M.Dhanabhakyam ◽

◽

Dr.M.Punithavalli Dr.M.Punithavalli

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Market Basket Analysis ◽

Rule Mining ◽

Market Basket ◽

Mining Algorithm

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Study of Various Parallel Implementations of Association Rule Mining Algorithm

American Journal Of Advanced Computing ◽

10.15864/ajac.v2i1.94 ◽

2015 ◽

Vol 2 (1) ◽

Author(s):

Sarbani Dasgupta

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Rule Mining ◽

Mining Algorithm ◽

Parallel Implementations

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Prediksi Code Defect Perangkat Lunak Dengan Metode Association Rule Mining dan Cumulative Support Thresholds

Jurnal Buana Informatika ◽

10.24002/jbi.v6i2.408 ◽

2015 ◽

Vol 6 (2) ◽

Author(s):

Rizal Setya Perdana ◽

Umi Laili Yuhana

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Rule Mining ◽

Program Code

Kualitas perangkat lunak merupakan salah satu penelitian pada bidangrekayasa perangkat lunak yang memiliki peranan yang cukup besar dalamterbangunnya sistem perangkat lunak yang berkualitas baik. Prediksi defectperangkat lunak yang disebabkan karena terdapat penyimpangan dari prosesspesifikasi atau sesuatu yang mungkin menyebabkan kegagalan dalam operasionaltelah lebih dari 30 tahun menjadi topik riset penelitian. Makalah ini akandifokuskan pada prediksi defect yang terjadi pada kode program (code defect).Metode penanganan permasalahan defect pada kode program akan memanfaatkanpola-pola kode perangkat lunak yang berpotensi menimbulkan defect pada data setNASA untuk memprediksi defect. Metode yang digunakan dalam pencarian polaadalah memanfaatkan Association Rule Mining dengan Cumulative SupportThresholds yang secara otomatis menghasilkan nilai support dan nilai confidencepaling optimal tanpa membutuhkan masukan dari pengguna. Hasil pengujian darihasil pemrediksian defect kode perangkat lunak secara otomatis memiliki nilaiakurasi 82,35%.

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