DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis.
Christos Baziotis,Nikos Pelekis,Christos Doulkeridis +2 more
- pp 747-754
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TLDR
Two deep-learning systems that competed at SemEval-2017 Task 4 “Sentiment Analysis in Twitter” are presented, which use Long Short-Term Memory networks augmented with two kinds of attention mechanisms, on top of word embeddings pre-trained on a big collection of Twitter messages.Abstract:
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 1st (tie) in Subtask A, and achieved very competitive results in the rest of the Subtasks. Both the word embeddings and our text processing tool are available to the research community.read more
Citations
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Proceedings ArticleDOI
SemEval-2017 Task 4: Sentiment Analysis in Twitter
TL;DR: Crowdourcing on Amazon Mechanical Turk was used to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks, which included two subtasks: A, an expression-level subtask, and B, a message level subtask.
Proceedings ArticleDOI
SemEval-2016 Task 4: Sentiment Analysis in Twitter
TL;DR: The SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. as mentioned in this paper discusses the fourth year of the Sentiment Analysis in Twitter Task and discusses the three new subtasks focus on two variants of the basic sentiment classification in Twitter task.
Proceedings ArticleDOI
BERTweet: A pre-trained language model for English Tweets
TL;DR: BERweet as discussed by the authors is the first large-scale pre-trained language model for English Tweets, having the same architecture as BERT-base and is trained using the RoBERTa pre-training procedure.
Journal ArticleDOI
Sentiment Analysis of Comment Texts Based on BiLSTM
TL;DR: An improved word representation method is proposed, which integrates the contribution of sentiment information into the traditional TF-IDF algorithm and generates weighted word vectors, which is proved to be effective with high accuracy on comments.
Overview of the GermEval 2018 Shared Task on the Identification of Offensive Language
TL;DR: This pilot edition of the GermEval Shared Task on the Identification of Offensive Language deals with the classification of German tweets from Twitter and describes the process of extracting the raw-data for the data collection and the annotation schema.
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