ABSTRACT
Social media are providing the humus for the sharing of knowledge and experiences and the growth of community activities (e.g., debating about different topics). The analysis of the user-generated content in this area usually relies on Sentiment Analysis. Word embeddings and Deep Learning have attracted extensive attention in various sentiment detection tasks. In parallel, the literature exposed the drawbacks of traditional approaches when content belonging to specific contexts is processed with general techniques. Thus, ad-hoc solutions are needed to improve the effectiveness of such systems. In this paper, we focus on user-generated content coming from the e-learning context to demonstrate how distributional semantic approaches trained on smaller context-specific textual resources are more effective with respect to approaches trained on bigger general-purpose ones. To this end, we build context-trained embeddings from online course reviews using state-of-the-art generators. Then, those embeddings are integrated in a deep neural network we designed to solve a polarity detection task on reviews in the e-learning context, modeled as a regression. By applying our approach on embeddings trained using background corpora from different contexts, we show that the performance is better when the background context is aligned with the regression context.
- M. Atzeni and D. Reforgiato. 2018. Deep Learning and Sentiment Analysis for Human-Robot Interaction. In Europ. Semantic Web Conference. Springer, 14--18.Google Scholar
- K. L. Cela, M. Á. Sicilia, and S. Sánchez. 2015. Social Network Analysis in E-Learning Environments. Educational Psychology Review 27, 1 (2015), 219--246.Google ScholarCross Ref
- D. Dessì, G. Fenu, M. Marras, and D. Reforgiato Recupero. 2018. Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections. Computers in Human Behavior (2018).Google Scholar
- D. Dessì, G. Fenu, M. Marras, and D. Reforgiato Recupero. 2018. COCO: Semantic-Enriched Collection of Online Courses at Scale with Experimental Use Cases. In Trends and Advances in Infor. Systems and Technologies. Springer, 1386--1396.Google Scholar
- G. Dragoni and M. Petrucci. 2017. A Neural Word Embeddings Approach for Multi-Domain Sentiment Analysis. IEEE Trans. Affect. Comput. 8, 4 (2017), 457--470.Google ScholarCross Ref
- A. Dridi and D. Reforgiato. 2017. Leveraging semantics for sentiment polarity detection in social media. Int. Jour. of Machine Learning and Cybernetics (2017).Google Scholar
- G. Esparza, A. de Luna, A. O. Zezzatti, A. Hernandez, J. Ponce, M. Álvarez, E. Cossio, and J. de Jesus Nava. 2017. A sentiment analysis model to analyze students reviews of teacher performance using support vector machines. In Int. Symp. on Distributed Computing and Artificial Intelligence. Springer, 157--164.Google Scholar
- M. Giatsoglou, M. G Vozalis, K. Diamantaras, A. Vakali, G. Sarigiannidis, and K. Chatzisavvas. 2017. Sentiment analysis leveraging emotions and word embeddings. Expert Systems with Applications 69 (2017), 214--224.Google ScholarCross Ref
- S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, and T. Mikolov. 2016. Fasttext. zip: Compressing text classification models. arXiv:1612.03651 (2016).Google Scholar
- Y. Li, Q. Pan, T. Yang, S. Wang, J. Tang, and E. Cambria. 2017. Learning Word Representations for Sentiment Analysis. Cogn. Computation 9, 6 (2017), 843--851.Google ScholarCross Ref
- A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts. 2011. Learning Word Vectors for Sentiment Analysis. In Proc. of the Annual Meeting of the Assoc. for Computational Linguistics: Human Language Technologies - Vol. 1. 142--150. Google ScholarDigital Library
- T. Mikolov, K. Chen, G. Corrado, and J. Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).Google Scholar
- J. Pennington, R. Socher, and C. Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 1532--1543.Google Scholar
- C. Raffel and D. P. Ellis. 2015. Feed-forward networks with attention can solve some long-term memory problems. arXiv:1512.08756 (2015).Google Scholar
- D. Reforgiato, V. Presutti, S. Consoli, A. Gangemi, and A. Nuzzolese. 2015. Sentilo: Frame-Based Sentiment Analysis. Cogn. Computation 7, 2 (2015), 211--225.Google Scholar
- D. Reforgiato Recupero, E. Cambria, and E. Di Rosa. 2017. Semantic Sentiment Analysis Challenge ESWC 2017. In Semantic Web Challenges. Springer, 109--123.Google Scholar
- E. Rudkowsky, M. Haselmayer, M. Wastian, M. Jenny, S. Emrich, and M. Sedlmair. 2018. More than Bags of Words: Sentiment Analysis with Word Embeddings. Communication Methods and Measures 12, 2-3 (2018), 140--157.Google ScholarCross Ref
- B. Shi, Z. Fu, L. Bing, and W. Lam. 2018. Learning Domain-Sensitive and Sentiment-Aware Word Embeddings. arXiv:1805.03801 (2018).Google Scholar
- D. Tang, F. Wei, B. Qin, N. Yang, T. Liu, and M. Zhou. 2016. Sentiment Embeddings with Applications to Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering 28, 2 (2016), 496--509. Google ScholarDigital Library
- D. Tang, F. Wei, N. Yang, M. Zhou, T. Liu, and B. Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In Proc. of the Annual Meeting of the Association for Computational Linguistics. 1555--1565.Google Scholar
- Z. Zhang and M. Lan. 2015. Learning sentiment-inherent word embedding for word-level and sentence-level sentiment analysis. In 2015 International Conference on Asian Language Processing (IALP). 94--97.Google Scholar
Recommendations
Joint sentiment/topic model for sentiment analysis
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementSentiment 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 based on Latent Dirichlet ...
Transportation sentiment analysis using word embedding and ontology-based topic modeling
AbstractSocial networks play a key role in providing a new approach to collecting information regarding mobility and transportation services. To study this information, sentiment analysis can make decent observations to support intelligent ...
Highlights- Social networks provide a new approach to collect data regarding transportation.
A Sentiment Analysis Algorithm of Danmaku Based on Building a Mixed Fine-grained Sentiment Lexicon
ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern RecognitionThe Danmaku is a form of instant video text commentary that reflects the viewer's sentiment orientation. Currently, most of sentiment analysis algorithms based on the sentiment lexicon are using manual construction of the lexicon. However, this kind of ...
Comments