Suggestion Miner at SemEval-2019 Task 9: Suggestion Detection in Online Forum using Word Graph

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 1242–1246 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 1242 Suggestion Miner at SemEval-2019 Task 9: Suggestion Detection in Online Forum using Word Graph Usman Ahmed1, Humera Liaquat2, Luqman Ahmed3 and Syed Jawad Hussain1 1Department of Computer Science and IT, The University of Lahore, Islamabad, Campus 2Department of Computer Science, COMSAT, Islamabad, Pakistan 3Department of Computer Science, UET, Taxila, Pakistan usman.ahmed@cs.uol.edu.pk humaira.699@gmail.com aluqman734@gmail.com jawad.hussain@cs.uol.edu.pk Abstract This paper describes the suggestion miner sys- tem that participates in SemEval 2019 Task 9 - SubTask A - Suggestion Mining from Online Reviews and Forums. The system participated in the subtasks A. This paper discusses the re- sults of our system in the development, eval- uation and post evaluation. Each class in the dataset is represented as directed unweighted graphs. Then, the comparison is carried out with each class graph which results in a vec- tor. This vector is used as features by a ma- chine learning algorithm. The model is evalu- ated on, hold on strategy. The organizers ran- domly split (8500 instances) training set (pro- vided to the participant in training their sys- tem) and testing set (833 instances). The test set is reserved to evaluate the performance of participants systems. During the evaluation, our system ranked 31 in the Coda Lab result of the subtask A (binary class problem). The binary class system achieves evaluation value 0.35. 1 Introduction Suggestion mining or identification of suggestions within the text is a relatively new area which is gaining popularity among many private and pub- lic sector organizations, service providers and con- sumers/customers at large due to its number of uses. Suggestion mining, in general, refers to the extraction of tips, advise or recommendations from an unstructured text, which can lead to a number of use cases (Negi et al., 2018). Currently, suggestion mining is considered to be an intrinsic part of any decision-making process, used by dif- ferent entities to get an insight into people’s per- spectives and improve their products or services (Negi et al., 2018), (Jijkoun et al., 2010). To get reviews and suggestions, organizations either ask them from users explicitly or extract them from online reviews, blogs, discussion forums, social media etc. This is because, these platforms are progressively gaining popularity (due to their ex- peditious advancements and ease of use) for ob- taining public opinions towards events, brands, products, services, entities etc. Consider some ex- amples which have been seen among many online reviews, giving useful suggestions to whom it may concern: ”I would recommend doing the upgrade to be sure you have the best chance at trouble-free operation.”, ”Be sure to specify a room at the back of the hotel” (Negi et al., 2018), ”Make sure you bring plenty of sun tan lotion-very expensive in the gift shop” (Negi and Buitelaar, 2015). We can clearly see that these reviews contain suggestions and recommendations for others to make use of a service or a product …

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