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Aspect Based Sentiment Analysis Acknowledgments We would like to give special thanks to our mentors Prof. Smaranda Muresan and Data Scientist Peter Deng for providing their insight and expertise that greatly assisted the research. Introduction & Dataset The rapid growth of e-commerce has led to an upsurge of customer reviews that constitute important source of information for both customers and businesses. To summarise customers’ opinions on products from the overwhelming amount of reviews, the Aspect Based Sentiment Analysis(ASBA) problem is raised. This project recommends a method to extract aspects from reviews, and to identify the sentiment polarity of each customer review expressed towards specific aspects. References Pavlopoulos, Ioannis (2014). “Aspect based sentiment analysis”. In: Athens University of Economics and Business. Pontiki, Maria et al. (2016). “SemEval-2016 task 5: Aspect based sentiment analysis”. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp. 19– 30. SemEval-2016 ABSA Restaurant Reviews-English: Train Data (n.d.). url: http://metashare. ilsp.gr:8080/. Levy, O., & Goldberg, Y. (2014). Dependency - based word embeddings. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Vol. 2, pp. 302 - 308). Aspect Detection Aspect detection aims to derive aspects that are directly discussed in reviews and it only focuses on explicit aspects. The method couples word embedding with clustering techniques. Dependency-based word embedding is chosen because it captures both semantic and syntactic similarities. Then, agglomerative clustering method with cosine similarity is applied to cluster word vectors Conclusions This project explored and experimented with both supervised and unsupervised ABSA methods extensively. Applied various NLP techniques from linear SVM to transfer learning (pre-trained word embeddings) and deep learning. Compared advantages and drawbacks of different approaches. Integrated aspect detection with sentiment analysis and finally proposed chaining agglomerative clustering together with SVM + DNN as a general method to summarise feedbacks from customer reviews by aspects. Aspect Specific Sentiment Analysis Sentiment analysis includes two phases. Phase (i), for each aspect in the candidate list, a binary classifier is trained to detect whether or not a review is describing it. Then aspects are predicted using one vs the rest. Feature weights of a classifier learned in phase (i) are fed as features to classifiers in phase (ii) to detect sentiment polarities. Topic: Aspect Based Sentiment Analysis Given a review as input, detect sentiments expressed towards aspects that are of concern to most customers Word Cloud: word frequency in all reviews Histogram of human-annotated aspects in the data Liutong Zhou (lz2484), Jiachen Xu (jx2318), Zihan Ye (zy2293), Youyang Liu (yl3767) Industry Mentor: Peter Deng Faculty Mentor: Smaranda Muresan Histogram of polarities in the data Dendrogram Words Expansion of Cluster 1 Evaluation of Aspect Detection Food, ambience, service aspects detection results are evaluated by precision, recall and f1-score. Adding expansion words additional to clustering result can significantly increase precisions and recalls for all aspects The optimal number of cluster is chosen to be 15 because there is steep increase in overall similarity at that point. Dependency-Based Context Extraction Example Number of Clusters vs Overall Similarity Algorithm Dictionary Size Impacts on Aspect Detection
