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    <abstract>This paper discusses on task 1, “Affect in Tweets” sharedtask, conducted in SemEval-2018. This task comprises of various subtasks, which required participants to analyse over different emotions and sentiments based on the provided tweet data and also measure the intensity of these emotions for subsequent subtasks. Our approach in these task was to come up with a model on count based representation and use machine learning techniques for regression and classification related tasks. In this work, we use a simple bag of words technique for supervised text classification model as to compare, that even with some advance distributed representation models we can still achieve significant accuracy. Further, fine tuning on various parameters for the bag of word, representation model we acquired better scores over various other baseline models (Vinayan et al.) participated in the sharedtask.</abstract>
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%0 Conference Proceedings
%T CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets
%A J R, Naveen
%A Ganesh H. B., Barathi
%A Kumar M, Anand
%A K P, Soman
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F j-r-etal-2018-cennlp
%X This paper discusses on task 1, “Affect in Tweets” sharedtask, conducted in SemEval-2018. This task comprises of various subtasks, which required participants to analyse over different emotions and sentiments based on the provided tweet data and also measure the intensity of these emotions for subsequent subtasks. Our approach in these task was to come up with a model on count based representation and use machine learning techniques for regression and classification related tasks. In this work, we use a simple bag of words technique for supervised text classification model as to compare, that even with some advance distributed representation models we can still achieve significant accuracy. Further, fine tuning on various parameters for the bag of word, representation model we acquired better scores over various other baseline models (Vinayan et al.) participated in the sharedtask.
%R 10.18653/v1/S18-1049
%U https://aclanthology.org/S18-1049
%U https://doi.org/10.18653/v1/S18-1049
%P 329-333

Markdown (Informal)

[CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets](https://aclanthology.org/S18-1049) (J R et al., SemEval 2018)

• CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets (J R et al., SemEval 2018)

ACL

• Naveen J R, Barathi Ganesh H. B., Anand Kumar M, and Soman K P. 2018. CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 329–333, New Orleans, Louisiana. Association for Computational Linguistics.