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Mining advices from weblogs

Published: 29 October 2012 Publication History

Abstract

Weblog, one of the fastest growing user generated contents, often contains key learnings gleaned from people's past experiences which are really worthy to be well presented to other people. One of the key learnings contained in weblogs is often vented in the form of advice. In this paper, we aim to provide a methodology to extract sentences that reveal advices on weblogs. We observed our data to discover the characteristics of advices contained in weblogs. Based on this observation, we define our task as a classification problem using various linguistic features. We show that our proposed method significantly outperforms the baseline. The presence or absence of imperative mood expression appears to be the most important feature in this task. It is also worth noting that the work presented in this paper is the first attempt on mining advices from English data.

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Lenhart, A. and Fox, S. Bloggers: A portrait of the internet's new storytellers. Pew Interent and American Life Project. Available at http://www.pewinterent.org., 2006.
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cover image ACM Conferences
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management
October 2012
2840 pages
ISBN:9781450311564
DOI:10.1145/2396761
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 29 October 2012

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  1. advice mining
  2. text mining

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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  • (2023)A semi-supervised method to generate a persian dataset for suggestion classificationLanguage Resources and Evaluation10.1007/s10579-023-09688-758:2(839-858)Online publication date: 29-Sep-2023
  • (2022)A semi-supervised method to generate Persian dataset for suggestion classificationMachine Learning with Applications10.1016/j.mlwa.2022.100296(100296)Online publication date: Apr-2022
  • (2022)A Comprehensive Analysis of Aspect-Oriented Suggestion Extraction from Online ReviewsDeep Learning Applications, Volume 410.1007/978-981-19-6153-3_5(111-134)Online publication date: 26-Nov-2022
  • (2021)Generating Tips from Product ReviewsProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441755(310-318)Online publication date: 8-Mar-2021
  • (2021)Aspect Oriented Suggestion Extraction from Online Reviews2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA52953.2021.00250(1561-1568)Online publication date: Dec-2021
  • (2021)Beyond the Polarities: Sentiment Analysis of French Restaurant Reviews Using BERT-based Models2021 8th International Conference on Behavioral and Social Computing (BESC)10.1109/BESC53957.2021.9635309(1-8)Online publication date: 29-Oct-2021
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  • (2017)Extracting and Ranking Travel Tips from User-Generated ReviewsProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052632(987-996)Online publication date: 3-Apr-2017
  • (2017)Mining graphs from travel blogs: a review in the context of tour planningInformation Technology & Tourism10.1007/s40558-017-0095-217:4(429-453)Online publication date: 5-Dec-2017
  • (2015)Mining Massive Web Log Data of an Official Tourism Web Site as a Step towards Big Data Analysis in TourismProceedings of the ASE BigData & SocialInformatics 201510.1145/2818869.2818906(1-4)Online publication date: 7-Oct-2015
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