DCU: Aspect-based Polarity Classification for SemEval Task 4 (original) (raw)
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Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016
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SemEval-2015 Task 12: Aspect Based Sentiment Analysis
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 486–495, Denver, Colorado, June 4-5, 2015. Association for Computational Linguistics., 2015
SemEval-2015 Task 12, a continuation of SemEval-2014 Task 4, aimed to foster research beyond sentence-or text-level sentiment classification towards Aspect Based Sentiment Analysis. The goal is to identify opinions expressed about specific entities (e.g., laptops) and their aspects (e.g., price). The task provided manually annotated reviews in three domains (restaurants, laptops and hotels), and a common evaluation procedure. It attracted 93 submissions from 16 teams. 1 A subset of the datasets has been annotated with aspects at the sentence level.
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Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015
This paper describes our participation in SemEval-2015 Task 12, and the opinion mining system sentiue. The general idea is that systems must determine the polarity of the sentiment expressed about a certain aspect of a target entity. For slot 1, entity and attribute category detection, our system applies a supervised machine learning classifier, for each label, followed by a selection based on the probability of the entity/attribute pair, on that domain. The target expression detection, for slot 2, is achieved by using a catalog of known targets for each entity type, complemented with named entity recognition. In the opinion sentiment slot, we used a 3 class polarity classifier, having BoW, lemmas, bigrams after verbs, presence of polarized terms, and punctuation based features. Working in unconstrained mode, our results for slot 1 were assessed with precision between 57% and 63%, and recall varying between 42% and 47%. In sentiment polarity, sentiue's result accuracy was approximately 79%, reaching the best score in 2 of the 3 domains.
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Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014
In this paper, we present our contribution in SemEval2014 ABSA task, some supervised methods for Aspect-Based Sentiment Analysis of restaurant and laptop reviews are proposed, implemented and evaluated. We focus on determining the aspect terms existing in each sentence, finding out their polarities, detecting the categories of the sentence and the polarity of each category. The evaluation results of our proposed methods exhibit a significant improvement in terms of accuracy and f-measure over all four subtasks regarding to the baseline proposed by SemEval organisers.
UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016
This paper describes our system used in the Aspect Based Sentiment Analysis (ABSA) task of SemEval 2016. Our system uses Maximum Entropy classifier for the aspect category detection and for the sentiment polarity task. Conditional Random Fields (CRF) are used for opinion target extraction. We achieve state-of-the-art results in 9 experiments among the constrained systems and in 2 experiments among the unconstrained systems.
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Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014
In this paper, we present the system we have developed for the SemEval-2014 Task 4 dedicated to Aspect-Based Sentiment Analysis. The system is based on a robust parser that provides information to feed different classifiers with linguistic features dedicated to aspect categories and aspect categories polarity classification. We mainly present the work which has been done on the restaurant domain 1 for the four subtasks, aspect term and category detection and aspect term and category polarity.
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016
This paper presents our contribution to the Se-mEval 2016 task 5: Aspect-Based Sentiment Analysis. We have addressed Subtask 1 for the restaurant domain, in English and French, which implies opinion target expression detection, aspect category and polarity classification. We describe the different components of the system, based on composite models combining sophisticated linguistic features with Machine Learning algorithms, and report the results obtained for both languages.
UMDuluth-CS8761-12: A Novel Machine Learning Approach for Aspect Based Sentiment Analysis
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015
This paper provides a detailed description of the approach of our system for the Aspect-Based Sentiment Analysis task of SemEval-2015. The task is to identify the Aspect Category (Entity and Attribute pair), Opinion Target and Sentiment of the reviews. For the In-domain subtask that is provided with the training data, the system is developed using a supervised technique Support Vector Machine and for the Out-of-domain subtask for which the training data is not provided, it is implemented based on the sentiment score of the vocabulary. For In-domain subtask, our system is developed specifically for restaurant data.
V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task 12
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015
This paper presents our participation in SemEval-2015 task 12 (Aspect Based Sentiment Analysis). We participated employing only unsupervised or weakly-supervised approaches. Our attempt is based on requiring the minimum annotated or hand-crafted content, and avoids training a model using the provided training set. We use a continuous word representations (Word2Vec) to leverage in-domain semantic similarities of words for many of the involved subtasks.