Wissam Mammar Kouadri
ABSTRACT
In this work, we propose a Weakly Semantic-based approach for Sentiment Analysis (WSSA), a novel approach that analyzes sentiment by considering weak labels from different sources (sentiment analysis tools) and aggregates them based on features such as the consistency between sources, the semantic equivalence between documents, and experts’ domain knowledge in order to improve the sentiments analysis tools results. The aggregation is achieved using a Probabilistic Soft Logic reasoner to infer the documents’ polarity.
- Stephen H. Bach, Matthias Broecheler, Bert Huang, and Lise Getoor. 2017. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. J. Mach. Learn. Res. 18(2017), 109:1–109:67.Google Scholar
- Erik Cambria, Soujanya Poria, Devamanyu Hazarika, and Kenneth Kwok. 2018. SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In Proceedings of AAAI.Google ScholarCross Ref
- Haibo Ding and Ellen Riloff. 2018. Weakly supervised induction of affective events by optimizing semantic consistency. In Thirty-Second AAAI Conference on Artificial Intelligence.Google ScholarCross Ref
- Andrea Esuli and Fabrizio Sebastiani. 2006. Sentiwordnet: A publicly available lexical resource for opinion mining.. In LREC, Vol. 6. Citeseer, 417–422.Google Scholar
- Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, and Wei Wang. 2020. Language-agnostic bert sentence embedding. arXiv preprint arXiv:2007.01852(2020).Google Scholar
- CJ Hutto Eric Gilbert. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Eighth International Conference on Weblogs and Social Media (ICWSM-14).Google Scholar
- Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018. Adversarial Example Generation with Syntactically Controlled Paraphrase Networks. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 1875–1885.Google ScholarCross Ref
- Aditya Kalyanpur, Bijan Parsia, Matthew Horridge, and Evren Sirin. 2007. Finding all justifications of OWL DL entailments. In The Semantic Web. Springer, 267–280.Google Scholar
- Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882(2014).Google Scholar
- Wissam Maamar Kouadri, Mourad Ouziri, Salima Benbernou, Karima Echihabi, Themis Palpanas, and Iheb Ben Amor. 2020. Quality of Sentiment Analysis Tools: The Reasons of Inconsistency. Proc. VLDB Endow. 14, 4 (2020), 668–681.Google ScholarDigital Library
- Soujanya Poria, Erik Cambria, Grégoire Winterstein, and Guang-Bin Huang. 2014. Sentic patterns: Dependency-based rules for concept-level sentiment analysis. Knowledge-Based Systems 69 (2014), 45–63.Google ScholarDigital Library
- Aliaksei Severyn and Alessandro Moschitti. 2015. Twitter sentiment analysis with deep convolutional neural networks. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 959–962.Google ScholarDigital Library
- Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing. 1631–1642.Google Scholar
- Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011. Lexicon-based methods for sentiment analysis. Computational linguistics 37, 2 (2011), 267–307.Google Scholar
Index Terms
- WSSA: Weakly Supervised Semantic-based approach for Sentiment Analysis
Recommendations
Incorporating Sentiment Prior Knowledge for Weakly Supervised Sentiment Analysis
This article presents two novel approaches for incorporating sentiment prior knowledge into the topic model for weakly supervised sentiment analysis where sentiment labels are considered as topics. One is by modifying the Dirichlet prior for topic-word ...
Exploring weakly supervised latent sentiment explanations for aspect-level review analysis
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge ManagementIn sentiment analysis, aspect-level review analysis has been an important task because it can catalogue, aggregate, or summarize various opinions according to a product's properties. In this paper, we explore a new concept for aspect-level review ...
Weakly Supervised Joint Sentiment-Topic Detection from Text
Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST)...
Comments