The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis
Open accessAbstract
In this paper we present two deep-learning systems that competed at SemEval-2017 Task 4 "Sentiment Analysis in Twitter".We participated in all subtasks for English tweets, involving message-level and topic-based sentiment polarity classification and quantification.We use Long Short-Term Memory (LSTM) networks augmented with two kinds of attention mechanisms, on top of word embeddings pre-trained on a big collection of Twitter messages.Also, we present a text processing tool suitable for social network messages, which performs tokenization, word normalization, segmentation and spell correction.Moreover, our approach uses no hand-crafted features or sentiment lexicons.We ranked 1 st (tie) in Subtask A, and achieved very competitive results in the rest of the Subtasks.Both the word embeddings and our text processing tool 1 are available to the research community.
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Cited by
SemEval-2017 Task 4: Sentiment Analysis in Twitter
Rosenthal, S., Farra, N., & Nakov, P. (2017) · Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) · doi:10.18653/v1/s17-2088 · Cited by 870
Transformer models for text-based emotion detection: a review of BERT-based approaches
Acheampong, F. A., Nunoo-Mensah, H., & Chen, W. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09958-2 · Cited by 659
Sentiment Analysis of Comment Texts Based on BiLSTM
Xu, G., Meng, Y., Qiu, X., Yu, Z., & Wu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2909919 · Cited by 649
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition
Hu, Y., Wong, Y., Wei, W., Du, Y., Kankanhalli, M., & Geng, W. (2018) · PLOS ONE · doi:10.1371/journal.pone.0206049 · Cited by 398
Text‐based emotion detection: Advances, challenges, and opportunities
Acheampong, F. A., Wenyu, C., & Nunoo‐Mensah, H. (2020) · Engineering Reports · doi:10.1002/eng2.12189 · Cited by 367
Metadata
- Authors
- Baziotis, C., Pelekis, N., & Doulkeridis, C.
- Venue
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Published
- 2017
- Pages
- 747-754
- OpenAlex
- W2750747353
- Cited by
- 485
- References
- 35
- FWCI
- 31.12
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Baziotis, C., Pelekis, N., & Doulkeridis, C. (2017). DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). https://doi.org/10.18653/v1/s17-2126
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