Ranking user’s comments by use of proposed weighting method

zahra hariri


The main challenges which are posed in opinion mining is information retrieval of large volumes of ideas and categorize and classify them for use in related fields. The ranking can help the users to make better choices and manufacturers in order to help improve the quality. As one of the pre-processing techniques in the field of classification, weighting methods have a crucial role in ranking ideas and comments. So, we decided to offer a new weighting method to improve some other similar methods, especially Dirichlet weighting method. In this paper, the proposed method will be described in detail, and the comparison with the three weighting methods: Dirichlet, Pivoted and Okapi also described. The proposed weighting method has higher accuracy and efficiency in comparison to similar methods. In the following, user comments of online newspapers are ranked and classified by use of proposed method. The purpose is to provide more efficient and more accurate weighting method, therefor the results of ranking will be more reliable and acceptable to users.


Opinion mining; Information retrieval; ranking comments; weighting methods; weighting methods constraints

Full Text:




c Evaluation of Information Retrieval Models”,ACM Transaction on Information Systems, Vol 29, No 2, Article 7,April 2011.

Alexandru Tatar, Panayotis Antoniadis, Marcelo Dias de Amorim, and Serge Fdida," Ranking news articles based on popularity Prediction", International Conference on Advances in Social Networks Analysis and Mining, IEEE/ACM ,2012, pp 106 – 110, ISBN/ISSN: 978-1-4673-2497-7 .

Chiao-Fang Hsu, Elham Khabiri, James Caverlee," Ranking Comments on the Social Web",Proceedings of the 2009 International Conference on Computational Science and Engineering , Vol 04, ISBN: 978-0-7695-3823-5 , pp 90-97 .

Robertson, A.M. and Willett, P., “An Upperbound to the Performance of Ranked-Output Searching: Optimal Weighting of Query Terms Using a Genetic Algorithm”, Journal of Documentation, Vol. 52, pp. 405–420, 1996.

Ghose, A. and P. Ipeirotis. Designing novel review ranking systems: predicting the usefulness and impact of reviews. In Proceedings of the International Conference on Electronic Commerce, 2007.

Giorgos Giannopoulos, Ingmar Weber, Alejandro Jaimes, Timos Sellis" Diversifying User Comments on News Articles",Lecture Notes in Computer Science Vol 7651, 2012, pp 100-113.

Stefan Siersdorfer, Sergiu Chelaru, Jose San Pedro," How Useful are Your Comments?- Analyzing and Predicting YouTube Comments and Comment Ratings",WWW '10 Proceedings of the 19th international conference on World wide web ,2010,ISBN: 978-1-60558-799-8 , pp 891-900 .

Pooja Kherwa, Arjit Sachdeva, Dhruv Mahajan Nishtha Pande, Prashast Kumar, “An approach towards comprehensive sentimental data analysis and opinion mining”, Advance Computing Conference (IACC), 2014 IEEE International, 21-22 Feb. 2014,ISBN: 978-1-4799-2571-1,pp:606-612.

Liu, Bing, "Sentiment analysis: A multi-faceted problem". IEEE Intelligent Systems, 2010.25,3: 76-80.

Lululemon Black Friday cheap nfl jerseys Lululemon factory Outlet ny Black Friday discount tiffany outlet wholesale soccer jerseys online oakley black friday cheap nhl jerseys china cheap nfl jerseys north face black friday sale cheap nfl jerseys online Jordans Black Friday Sale 2015 Cheap Moncler Cyber Monday moncler outlet cheap soccer jerseys moncler outlet black friday cheap authentic nfl jerseys north face cyber monday Louboutin Black Friday canada wholesale cheap nfl jerseys lululemon cyber monday 2015 cheap nfl jerseys from china 2015 Cheap Moncler Black Friday Sale Moncler Cyber Monday 2015 cheap jerseys Lululemon Cyber Monday Sale jordans cyber monday deals 2015 cheap nike nfl jerseys Black Friday deals Lululemon 2015 jordan black friday 2015 Moncler Jackets Black Friday Sale 2015 Louboutin Pas Cher Black Friday 2015 Canada Lululemon north face black friday cheap wholesale soccer jerseys