Academia.eduAcademia.edu

Outline

Title
Abstract
FAQs
Introduction
Language Rule Model Method
Deep Learning Method
Hybrid Method
Result and Discussion
Conclusion
References
All Topics
Computer Science

Aspect-Based Sentiment Analysis Methods in Recent Years

Profile image of zohreh Madhoushizohreh Madhoushi

2019, Asia-Pacific Journal of Information Technology & Multimedia

https://doi.org/10.17576/APJITM-2019-0801-07
visibility

…

description

18 pages

link

1 file

Abstract

Sentiment Analysis (SA) is the computational treatment of opinions, sentiments and subjectivity of text. Aspectbased Sentiment Analysis (ABSA) is a specific SA that aims to extract most important aspects of an entity and predict the polarity of each aspect from the text. A review of the recent state-of-the-art in ABSA, shows the remarkable growing in finding both aspect, and the corresponding sentiment. Current methods are categorized based on their proposed algorithms and models. For each discussed study, aspect extraction method and sentiment prediction method, the dataset, domain and the reported performance is included. The main goal of this work is to review ABSA techniques with brief details. The main contributions of this paper consist of the refined categorizations of a great number of recent articles, comparing them and the illustration of the recent trend of research in the ABSA.

FAQs

sparkles

AI

What recent trends have been identified in aspect-based sentiment analysis research?add

The review highlights a shift towards deep learning models that outperform traditional methods, with attention mechanisms being prominently utilized. A growing number of articles focus on joint extraction of aspects and sentiments, enhancing efficiency and accuracy.

How does the accuracy of different ABSA methods vary across domains?add

Performance results indicate that high accuracies are attainable when focusing on specific domains, such as electronics or hospitality. However, accuracy diminishes significantly across multiple domains, indicating a challenge for generalization.

What methodologies are being used to evaluate aspect extraction and sentiment estimation?add

The review explores several evaluation metrics including precision, recall, and less common metrics like Mean Absolute Error and nDCG. Notably, inconsistencies in evaluation methods across studies complicate direct comparisons of results.

What challenges do ABSA systems face in extracting implicit aspects?add

ABSA systems show a substantial inability to extract implicit aspects and accurately predict implicit sentiment polarities. Many existing methods remain limited, failing to address the complexities involved in implicit sentiment detection.

How are language rule methods categorized within ABSA techniques?add

Language rule methods are classified based on filtering high-frequency nouns and phrases to identify aspects, often complemented by pruning techniques. These methods typically emphasize manual parameter tuning, limiting their adaptability to unseen datasets.

Related papers

Survey on Aspect Detection for Aspect-Based Sentiment Analysis
Flavius Frasincar

Artificial Intelligence Review, 2023

Sentiment analysis is an important tool to automatically understand the user-generated content on the Web. The most fine-grained sentiment analysis is concerned with the extraction and sentiment classification of aspects and has been extensively studied in recent years. In this work, we provide an overview of the first step in aspect-based sentiment analysis that assumes the extraction of opinion targets or aspects. We define a taxonomy for the extraction of aspects and present the most relevant works accordingly, with a focus on the most recent state-of-the-art methods. The three main classes we use to classify the methods designed for the detection of aspects are pattern-based, machine learning, and deep learning methods. Despite their differences, only a small number of works belong to a unique class of methods. All the introduced methods are ranked in terms of effectiveness. In the end, we highlight the main ideas that have led the research on this topic. Regarding future work, we deemed that the most promising research directions are the domain flexibility and the end-to-end approaches.

View PDFchevron_right
User Modeled Aspect based Aspect Sentiment Analysis
YASHVARDHAN SHARMA

Rapid increase in blogs, forum and social networking sites has drastically changed the way people communicate and express their opinions. This trend had inspired many research works targeting at the automated analysis of opinions. In this paper, we present aspect based sentimental approach which will bring out the attitude of the document according to user priority. The objective is to classify review into polarity class considering preferences of the user. As we are dealing with aspect based sentimental analysis, we will classify the polarity of the sentiments for each aspect and then, on the basis of user priority we will decide the overall polarity. The objective or irrelevant text is filtered out with respect to the given query. The proposed system does not require any labeled data, as raw text is taken as input in the form of multiple documents. The only supervision taken under consideration is using WordNet, Part of Speech Tagger. Our System achieves considerable improvement over the baseline and has better accuracy compared to the existing system, on the same dataset.

View PDFchevron_right
Survey On Aspect Based Sentiment Analysis Using Machine Learning Techniques
Syam Mohan

European Journal of Molecular & Clinical Medicine, 2021

View PDFchevron_right
Survey on Aspect-Level Sentiment Analysis
Kim Schouten

IEEE Transactions on Knowledge and Data Engineering, volume 28, number 3, 2016

The field of sentiment analysis, in which sentiment is gathered, analyzed, and aggregated from text, has seen a lot of attention in the last few years. The corresponding growth of the field has resulted in the emergence of various subareas, each addressing a different level of analysis or research question. This survey focuses on aspect-level sentiment analysis, where the goal is to find and aggregate sentiment on entities mentioned within documents or aspects of them. An in-depth overview of the current state-of-the-art is given, showing the tremendous progress that has already been made in finding both the target, which can be an entity as such, or some aspect of it, and the corresponding sentiment. Aspect-level sentiment analysis yields very finegrained sentiment information which can be useful for applications in various domains. Current solutions are categorized based on whether they provide a method for aspect detection, sentiment analysis, or both. Furthermore, a breakdown based on the type of algorithm used is provided. For each discussed study, the reported performance is included. To facilitate the quantitative evaluation of the various proposed methods, a call is made for the standardization of the evaluation methodology that includes the use of shared data sets. Semantically-rich concept-centric aspect-level sentiment analysis is discussed and identified as one of the most promising future research direction.

View PDFchevron_right
A Study of Different Approaches to Aspect-based Opinion Mining
aaghotkar ghotkar

International journal of computer applications, 2016

In recent years, Opinion mining has been an active research area in Text mining and analysis, natural language processing. Opinion mining is the computational study of people's opinion expressed in written language or text towards entities and their aspects. With the growth of internet, social networking sites, blogs, discussion forums, e-commerce websites have gained a tremendous importance and have provided platform for people to express and share their opinion on entities and their aspects. As opinionated web content is increasing rapidly in the form of reviews, comments, blogs, status updates, tweets, etc. it is practically impossible for people or organization to analyze all opinions at a time to make good decisions. Hence, there is a need for effective automated system to evaluate opinions and generate accurate results. This paper describes opinion mining and focuses on the sub topic aspect-based opinion mining, tasks in aspect-based opinion mining, current stateof-the-art methods used for aspect-based opinion mining, advantages and disadvantages of these methods and latest research challenges in aspect-based opinion mining. Our experimental results based on some of the aspect extraction techniques, gives an idea of which aspect extraction techniques are efficient and yield accurate results in practical opinion mining applications.

View PDFchevron_right
Aspect Based Sentiment Analysis
International Journal of Scientific Research in Science and Technology IJSRST

International Journal of Scientific Research in Science and Technology, 2022

Sentiment analysis, which addresses the computational treatment of opinion, sentiment, and subjectivity in text, has received considerable attention in recent years. In contrast to the traditional coarse-grained sentiment analysis tasks, such as document-level sentiment classification, we are interested in the fine-grained aspect-based sentiment analysis that aims to identify aspects that users comment on and these aspects’ polarities. Aspect-based sentiment analysis relies heavily on syntactic features. However, the reviews that this task focuses on are natural and spontaneous, thus posing a challenge to syntactic parsers. In this paper, we address this problem by proposing a framework of adding a sentiment sentence compression (Sent Comp) step before performing the aspect-based sentiment analysis. We apply a discriminative conditional random field model, with certain special features, to automatically compress sentiment sentences. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in textual data. sentiment analysis proves to be an incredible asset for users to extract essential information and assists organizations with understanding the social sentiment of their brand, product or service while monitoring online conversations.

View PDFchevron_right
A Survey on Aspect-Based Sentiment Classification
Flavius Frasincar

ACM Computing Surveys, 2023

With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

View PDFchevron_right
LISA: Language-Independent Method for Aspect-based Sentiment Analysis
Mohammadreza Shams

IEEE Access, 2020

Understanding ''what others think'' is one of the most eminent pieces of knowledge in the decision-making process required in a wide spectrum of applications. The procedure of obtaining knowledge from each aspect (property) of users' opinions is called aspect-based sentiment analysis which consists of three core sub-tasks: aspect extraction, aspect and opinion-words separation, and aspect-level polarity classification. Most successful approaches proposed in this area require a set of primary training or extensive linguistic resources, which makes them relatively costly and time consuming in different languages. To overcome the aforementioned challenges, we propose an unsupervised paradigm for aspect-based sentiment analysis, which is not only simple to use in different languages, but also holistically performs the subtasks for aspect-based sentiment analysis. Our methodology relies on three coarse-grained phases which are partitioned to manifold fine-grained operations. The first phase extracts the prior domain knowledge from dataset through selecting the preliminary polarity lexicon and aspect word sets, as representative of aspects. These two resources, as primitive knowledge, are assigned to an expectation-maximization algorithm to identify the probability of any word based on the aspect and sentiment. To determine the polarity of any aspect in the final phase, the document is firstly broken down to its constituting aspects and the probability of each aspect/polarity based on the document is calculated. To evaluate this method, two datasets in the English and Persian languages are used and the results are compared with various baselines. The experimental results show that the proposed method outperforms the baselines in terms of aspect, opinion-word extraction and aspect-level polarity classification. INDEX TERMS Aspect-based sentiment analysis, aspect extraction, polarity classification, topic modeling.

View PDFchevron_right
Opinion Context Extraction for Aspect Sentiment Analysis
Nirmalie Wiratunga

2018

Sentiment analysis is the computational study of opinionated text and is becoming increasing important to online commercial applications. However, the majority of current approaches determine sentiment by attempting to detect the overall polarity of a sentence, paragraph, or text window, but without any knowledge about the entities mentioned (e.g. restaurant) and their aspects (e.g. price). Aspect-level sentiment analysis of customer feedback data when done accurately can be leveraged to understand strong and weak performance points of businesses and services, and can also support the formulation of critical action steps to improve performance. In this paper we focus on aspect-level sentiment classification, studying the role of opinion context extraction for a given aspect and the extent to which traditional and neural sentiment classifiers benefit when trained using the opinion context text. We propose four methods to aspect context extraction using lexical, syntactic and sentimen...

View PDFchevron_right
KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis
Matteo Gaeta

Artificial Intelligence Review, 2022

The analysis of the opinions of customers and users has been always of great interest in supporting decision-making in many fields, especially in marketing. Sentiment analysis (SA) is the umbrella term for techniques and approaches that analyze user’s sentiments, emotions, opinions in text or other media. The need for a better understanding of these opinions paved the way to novel approaches that focus on the analysis of the sentiment related to specific features of a product, giving birth to the field of aspect-based sentiment analysis (ABSA). Although the increasing interest in this discipline, there is still confusion regarding the basic concepts of ABSA: terms like sentiment, affect, emotion, opinion, are used as synonyms while they represent different concepts. This often leads to an incorrect analysis of the users’ opinions.This work presents an overview of the state-of-the-art techniques and approaches for ABSA, highlighting the main critical issues related to current trends ...

View PDFchevron_right

References (68)

  1. Abu Latiffi, M. I. & Yaakub, M. R. 2018. Sentiment Analysis: An Enhancement of Ontological- Based Using Hybrid Machine Learning Techniques. Asia-Pacific Journal of Information Technology and Multimedia. 7(2):61-69.
  2. Bagheri, A., Saraee, M. & De Jong, F. 2014. ADM-LDA: An Aspect Detection Model Based on Topic Modelling Using the Structure of Review Sentences. Journal of Information Science. 40(5): 621-636.
  3. Baziotis, C., Pelekis, N. & Doulkeridis, C. 2017. DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison. pp. 390-395. 10.18653/v1/S17- 2065.
  4. Blair-Goldensohn, S., Hannan, K., Mcdonald, R., Neylon, T., Reis, G. A. & Reynar, J. 2008. Building a Sentiment Summarizer for Local Service Reviews. Proceedings of WWW Workshop on NLP in the Information Explosion Era. pp. 1-10
  5. Blei, D. M., Ng, A. Y. & Jordan, M. I. 2003. Latent Dirichlet Allocation. The Journal of Machine Learning Research. 3:993-1022.
  6. Brody, S. & Elhadad, N. 2010. An Unsupervised Aspect-Sentiment Model for Online Reviews. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguisticn. 804-812.
  7. Cambria, E., Schuller, B., Xia, Y. & Havasi, C. 2013. New Avenues in Opinion Mining and Sentiment Analysis,"in IEEE Intelligent Systems, 28(2): 15-21. doi: 10.1109/MIS.2013.30
  8. Chen, Z., Mukherjee, A. & Liu, B. 2014. Aspect Extraction with Automated Prior Knowledge Learning. Proceedings of ACL, pp. 347-358.
  9. Choi, Y. and Cardie, C. 2010. Hierarchical sequential learning for extracting opinions and their attributes. In Proceedings of the ACL 2010 Conference Short Papers (ACLShort '10). Association for Computational Linguistics, Stroudsburg, PA, USA, 269-274.
  10. de Albornoz J.C., Plaza L., Gervás P., Díaz A. (2011) A Joint Model of Feature Mining and Sentiment Analysis for Product Review Rating. In: Clough P. et al. (eds) Advances in Information Retrieval. ECIR 2011. Lecture Notes in Computer Science, vol 6611. Springer, Berlin, Heidelberg.
  11. Dhanush, D., Thakur, A. K. & Diwakar, N. P. 2016. Aspect-Based Sentiment Summarization with Deep Neural Networks. International Journal of Engineering Research and Technology, pp. 371-375.
  12. Ding, X., Liu, B. & Yu, P. S. 2008. A Holistic Lexicon-Based Approach to Opinion Mining. Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231-240.
  13. Du, H., Xu, X., Cheng, X., Wu, D., Liu, Y. & Yu, Z. 2016. Aspect-Specific Sentimental Word Embedding for Sentiment Analysis of Online Reviews. Proceedings of the 25th International Conference Companion on World Wide Web, pp. 29-30.
  14. Du, W. & Tan, S. 2009. An Iterative Reinforcement Approach for Fine-Grained Opinion Mining. Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 486-493.
  15. Esuli, A. & Sebastiani, F. 2006. Sentiwordnet: A Publicly Available Lexical Resource for Opinion Mining. In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC'06), pp. 417-422.
  16. Hai, Z., Chang, K. & Kim, J.-J. 2011. Implicit feature identification via co-occurrence association rule mining. In Proceedings of the 12th international conference on Computational linguistics and intelligent text processing -Volume Part I (CICLing'11), Alexander Gelbukh (Ed.), Vol. Part I. Springer-Verlag, Berlin, Heidelberg, pp. 393-404.
  17. Hu, M. & Liu, B. 2004. Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '04). ACM, New York, NY, USA, pp. 168-177.
  18. DOI=http://dx.doi.org/10.1145/1014052.1014073.
  19. Jakob, N. & Gurevych, I. 2010. Using anaphora resolution to improve opinion target identification in movie reviews. In Proceedings of the ACL 2010 Conference Short Papers (ACLShort '10). Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 263-268.
  20. Jebbara, S. & Cimiano, P. 2016 Aspect-based sentiment analysis using a two-step neural network architecture. Semantic Web Evaluation Challenge, pp. 153-167.
  21. Jin, W., Ho, H. H. & Srihari, R. K. 2009. OpinionMiner: a novel machine learning system for web opinion mining and extraction. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '09). ACM, New York, NY, USA, pp. 1195-1204. DOI: https://doi.org/10.1145/1557019.1557148.
  22. Kiritchenko, S., Zhu, X., Cherry, C. & Mohammad, S. 2014. Nrc-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). pp. 437-442.
  23. Lal, M. & Asnani, K. 2014. Aspect Extraction & Segmentation in Opinion Mining. International Journal Of Engineering And Computer Science 3(5): 5873-5878.
  24. Landauer, T. K., Mcnamara, D. S., Dennis, S. & Kintsch, W. 2013. Handbook of Latent Semantic Analysis. New York. Taylor & Francis.
  25. Lek, H. H. & Poo, D. C. 2013. Aspect-Based Twitter Sentiment Classification. Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on, pp. 366- 373.
  26. Li, F., Han, C., Huang, M., Zhu, X., Xia, Y.-J., Zhang, S. & Yu, H. 2010. Structure-Aware Review Mining and Summarization. Proceedings of the 23rd International Conference on Computational Linguistics, pp. 653-661.
  27. Liu, B. 2012. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. 5(1): 1-167.
  28. Liu, B., Hu, M. & Cheng, J. 2005. Opinion observer: analyzing and comparing opinions on the Web. In Proceedings of the 14th international conference on World Wide Web (WWW '05). ACM, New York, NY, USA, pp. 342-351. DOI: https://doi.org/10.1145/1060745.1060797.
  29. Liu, K., Xu, L. & Zhao, J. 2012. Opinion Target Extraction Using Word-Based Translation Model. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1346-1356.
  30. Liu, P., Qian, X. & Meng, H. 2014. Seeing Stars from Reviews by a Semantic-based Approach with MapReduce Implementation. ASE BigData/SocialInformatics/PASSAT/BioMedCom 2014 Conference, Harvard University, December 14-16, 2014, pp. 1-5.
  31. Liu, P., Joty, S. & Meng, H. 2015. Fine-Grained Opinion Mining with Recurrent Neural Networks and Word Embeddings. Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), pp. 1433-1443.
  32. Lizhen, L., Wei, S., Hanshi, W., Chuchu, L. & Jingli, L. 2014. A Novel Feature-Based Method for Sentiment Analysis of Chinese Product Reviews. China Communications, 11(3): 154-164.
  33. Lu, Y., Zhai, C. & Sundaresan, N. 2009. Rated aspect summarization of short comments. In Proceedings of the 18th international conference on World wide web (WWW '09). ACM, New York, NY, USA, pp. 131-140. DOI: https://doi.org/10.1145/1526709.1526728.
  34. Luhn, H. P. 1957. A Statistical Approach to Mechanized Encoding and Searching of Literary Information. IBM Journal of Research and Development, 1(4): 309-317.
  35. Luong, M.-T., Pham, H. and Manning, C. D. (2015) 'Effective Approaches to Attention-based Neural Machine Translation', Emnlp, (September), pp. 11. doi: 10.18653/v1/D15-1166.
  36. Ma, Y., Peng, H. & Cambria, E. 2018. Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM. The Thirty-Second AAAI Conference on Artificial Intelligence. pp. 5876-5883.
  37. Marrese-Taylor, E., Velásquez, J. D. & Bravo-Marquez, F. 2014. A Novel Deterministic Approach for Aspect-Based Opinion Mining in Tourism Products Reviews. Expert Systems with Applications. 41(17): 7764-7775.
  38. Medhat, W., Hassan, A. & Korashy, H. 2014. Sentiment Analysis Algorithms and Applications: A Survey. Ain Shams Engineering Journal. 5(4): 1093-1113.
  39. Moghaddam, S. & Ester, M. 2010. Opinion Digger: An Unsupervised Opinion Miner from Unstructured Product Reviews. Proceedings of the 19th ACM international conference on Information and knowledge management. pp. 1825-1828.
  40. Moghaddam, S. & Ester, M. 2011. ILDA: Interdependent LDA Model for Learning Latent Aspects and Their Ratings from Online Product Reviews. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp. 665-674.
  41. Moghaddam, S. & Ester, M. 2013. Opinion Mining in Online Reviews: Recent Trends. Tutorial at WWW2013. [31 December 2018]
  42. Pavlopoulos, J. & Androutsopoulos, I. 2014. Multi-Granular Aspect Aggregation in Aspect- Based Sentiment Analysis. Proceedings of EACL, pp. 78-87.
  43. Peng, J., Chunxia, Z., Hongping, F., Zhendong, N. & Qing, Y. 2010. An Approach Based on Tree Kernels for Opinion Mining of Online Product Reviews. In Proceedings of the 2010 IEEE International Conference on Data Mining (ICDM '10). IEEE Computer Society, Washington, DC, USA, pp. 256-265. DOI=http://dx.doi.org/10.1109/ICDM.2010.104.
  44. Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I. & Manandhar, S. 2014. Semeval-2014 Task 4: Aspect Based Sentiment Analysis. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27-35.
  45. Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S. & Androutsopoulos, I. 2015. Semeval-2015 Task 12: Aspect Based Sentiment Analysis. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Association for Computational Linguistics, Denver, Colorado, pp. 486-495.
  46. Popescu, A.-M. & Etzioni, O. 2007. Extracting product features and opinions from reviews. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT '05). Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 339-346. DOI: https://doi.org/10.3115/1220575.1220618
  47. Poria, S., Cambria, E., Ku, L.-W., Gui, C. & Gelbukh, A. 2014. A Rule-Based Approach to Aspect Extraction from Product Reviews. SocialNLP 2014 28, pp. 1-10.
  48. Poria, S., Cambria, E., & Gelbukh, A. 2016. Aspect Extraction for Opinion Mining with a Deep Convolutional Neural Network. Knowledge-Based System. 108:42-49.DOI: http://dx.doi.org/10.1016/j.knosys.2016.06.009
  49. Qiu, G., Liu, B., Bu, J. & Chen, C. 2011. Opinion Word Expansion and Target Extraction through Double Propagation. Computational Linguistics 37(1): 9-27.
  50. Ruder, S., Ghaffari, P. & Breslin, J.G.(2016b). INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis. Proceedings of SemEval- 2016, pp. 330-336. Available at: http://www.aclweb.org/anthology/S16-1053 [11 November 2018].
  51. Ruder, S., Ghaffari, P., & Breslin, J. G. (2016a). A Hierarchical Model of Reviews for Aspect- based Sentiment Analysis, In Proceedings of the Conference on Empirical Methods on Natural Language Processing (EMNLP 2016), pp. 1-7.
  52. Saeidi, M., Bouchard, G., Liakata, M. and Riedel, S., 2016. Sentihood: Targeted aspect based sentiment analysis dataset for urban neighbourhoods. arXiv preprint arXiv:1610.03771. [11 November 2018].
  53. Salton, G. 1989. Automatic Text Processing: The Transformation, Analysis, and Retrieval Of. Reading: Addison-Wesley.
  54. Sauper, C. & Barzilay, R. 2014. Automatic Aggregation by Joint Modeling of Aspects and Values. arXiv preprint arXiv:1401.6422 . pp. 89-127.
  55. Schouten, K. & Frasincar, F. 2016. Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3): 813-830.
  56. Shariaty, S. & Moghaddam, S. 2011. Fine-Grained Opinion Mining Using Conditional Random Fields. Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, pp. 109-114.
  57. Sparck Jones, K. 1972. A Statistical Interpretation of Term Specificity and Its Application in Retrieval. Journal of Documentation 28(1): 11-21.
  58. Tang, D., Qin, B. & Liu, T. 2015. Deep learning for sentiment analysis: Successful approaches and future challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5(6): 292-303.
  59. Wang, Y., Huang, M., Zhao, L., and Zhu, X. 2014. Attention-based LSTM for Aspect-level Sentiment Classification. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606-615.
  60. Wang, W., Pan, S. J., Dahlmeier, D. & Xiao, X. 2016. Recursive Neural Conditional Random Fields for Aspect-Based Sentiment Analysis. arXiv preprint arXiv:1603.06679, pp. 616- 626.
  61. Wu, Y., Zhang, Q., Huang, X. & Wu, L. 2009. Phrase Dependency Parsing for Opinion Mining. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. 3: pp. 1533-1541.
  62. Xu, J., Chen, D., Qiu, X. and Huang, X., 2016. Cached long short-term memory neural networks for document-level sentiment classification. arXiv preprint arXiv:1610.04989, pp. 1-10.
  63. Yaakub, M. R., Li, Y., Algarni, A. & Peng, B. 2012. Integration of Opinion into Customer Analysis Model. Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on, pp. 164-168.
  64. Yu, J., Zha, Z.-J., Wang, M. & Chua, T.-S. 2011. Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 1496-1505.
  65. Zhang, F., Zhang, Z. & Lan, M. 2014. Ecnu: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 252-258.
  66. Zhang L., Liu B. (2014) Aspect and Entity Extraction for Opinion Mining. In: Chu W. (eds) Data Mining and Knowledge Discovery for Big Data. Studies in Big Data, vol 1. Springer, Berlin, Heidelberg, pp. 1-49.
  67. Zhao, W. X., Jiang, J., Yan, H. & Li, X. 2010. Jointly Modeling Aspects and Opinions with a Maxent-Lda Hybrid. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 56-65.
  68. Zhuang, L., Jing, F. & Zhu, X.-Y. 2006. Movie review mining and summarization. In Proceedings of the 15th ACM international conference on Information and knowledge management (CIKM '06). ACM, New York, NY, USA, pp. 43-50. DOI=http://dx.doi.org/10.1145/1183614.1183625

Related papers

Feature Extraction Method for Aspect-Based Sentiment Analysis
Win Lei Khine

2019

In our daily life, we take opinions of our friends and we are influenced in decision making process. Opinion is the view or the judgment about something. Opinion Mining (OM) or Sentiment Analysis (SA) is the computational analysis of public’s opinion, emotion, sentiments, and attitude toward entities and their attributes expressed in written text. These entities may be products, services, organizations, individuals, events, issues, or topics. In sentiment analysis, formal and informal opinion text like product reviews, news articles, tweets, forum discussions, blogs, and Facebook posts are also applicable to all domains. The main purpose of sentiment analysis is to extract the main opinions, on which the decision can be made very right. Paper intends to classify sentiment polarity on product review datasets by using Mutual Information as a feature selection method. Because product reviews are highly focused and they are opinion rich. After the feature selection, we aim to classify t...

View PDFchevron_right
A Comprehensive Survey on Aspect Based Sentiment Analysis
Kaustubh yadav

2016

Sentiment analysis is a growing field in natural language processing to analyze and determine the polarity of given text or data in sentence level or document level. Sentiment analysis is widely used in social media, as it became an excellent source for people and individual to analysis also known as feature based sentiment analysis is a fine grained analysis where the aim is to identify different aspects expressed in document or sentence level. This approach allows the user to extract the most important aspects from the opinion expressed. This paper presents a brief survey of aspect-based sentiment analysis and its various approaches, metrics used for evaluation and latest research

View PDFchevron_right
Aspect Based Sentiment Analysis Survey
IOSR Journals

Sentiment analysis or Opinion mining is becoming an important task both from academics and commercial standpoint. In recent years text mining has become most promising area for research. There is an exponential growth with respect to World Wide Web, Mobile Technologies, Internet usage and business on electronic commerce applications. Because of which web opinion sources like online shopping portals, discussion forums, peer-to-peer networks, groups, blogs, micro blogs and social networking applications are extensively used to share the information, experience and opinions. In sentiment analysis, the opinion is evaluated to its positivity, negativity and neutrality with respect to the complete document or object. But this level of analysis does not provide the necessary detailed information for many applications. To obtain more fine grained analysis we need to go to Aspect Based Sentiment Analysis (ABSA). Aspect Based Sentiment analysis introduces a suite of problems which require deeper NLP capabilities and also produces a rich set of results.

View PDFchevron_right
Aspect Based Sentiment Analysis to Extract Meticulous Opinion Value
Deepali Virmani

Opinion Mining and Sentiment Analysis is a process of identifying opinions in large unstructured/structured data and then analysing polarity of those opinions. Opinion mining and sentiment analysis have found vast application in analysing online ratings, analysing product based reviews, e-governance, and managing hostile content over the internet. This paper proposes an algorithm to implement aspect level sentiment analysis. The algorithm takes input from the remarks submitted by various teachers of a student. An aspect tree is formed which has various levels and weights are assigned to each branch to identify level of aspect. Aspect value is calculated by the algorithm by means of the proposed aspect tree. Dictionary based method is implemented to evaluate the polarity of the remark. The algorithm returns the aspect value clubbed with opinion value and sentiment value which helps in concluding the summarized value of remark.

View PDFchevron_right
Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method
John Pavlopoulos

Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM), 2014

Given a set of texts discussing a particular entity (e.g., customer reviews of a smartphone), aspect based sentiment analysis (ABSA) identifies prominent aspects of the entity (e.g., battery, screen) and an average sentiment score per aspect. We focus on aspect term extraction (ATE), one of the core processing stages of ABSA that extracts terms naming aspects. We make publicly available three new ATE datasets, arguing that they are better than previously available ones. We also introduce new evaluation measures for ATE, again arguing that they are better than previously used ones. Finally, we show how a popular unsupervised ATE method can be improved by using continuous space vector representations of words and phrases.

View PDFchevron_right

Related topics

  • Computer Science
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved