A novel domain and event adaptive tweet augmentation approach for enhancing the classification of crisis related tweets

  title={A novel domain and event adaptive tweet augmentation approach for enhancing the classification of crisis related tweets},
  author={Dharini Ramachandran and Parvathi Ramasubramanian},
  journal={Data Knowl. Eng.},
1 Citations

A Space-Time Framework for Sentiment Scope Analysis in Social Media

A multi-dimensional view of scope, the introduction of the concept of sentiment scope, and the definition of a general framework capable of analyzing the sentiment scope related to any subject on any social network are proposed.



Classification for Crisis-Related Tweets Leveraging Word Embeddings and Data Augmentation

University College Dublin’s (UCD) work at TREC 2019-B Incident Streams (IS) track, to find actionable messages and estimate their priority among a stream of crisis-related tweets, is presented.

Twitter Mining for Disaster Response: A Domain Adaptation Approach

Experimental results suggest that, for some tasks, source data itself can be useful for classifying target data, but for tasks specific to a particular disaster, domain adaptation approaches that use target unlabeled data in addition to source labeled data are superior.

The OL-DAWE Model: Tweet Polarity Sentiment Analysis With Data Augmentation

The combination of Conjunction Analysis (CA) technology and Punctuation Mark Identification (PMI) technology is used to detect negation cue and its scope and the OL-DAWE model, which uses Data Augmentation (DA) technology to generate opposed tweets according to the original tweet is proposed.

Aggression Identification Using Deep Learning and Data Augmentation

This system description paper presents a proposal to augment the provided dataset to increase the number of labeled comments from 15,000 to 60,000, and introduces linguistic variety into the dataset, to train a special deep neural net, which generalizes especially well to unseen data.

Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs

The approach improves sexist tweet classification significantly in the entire machine learning models and can be readily applied to any other small dataset size like hate speech or abusive language and text classification problem using any machine learning model.

Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection

  • Sooji HanJie GaoF. Ciravegna
  • Computer Science
    2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
  • 2019
Preliminary experiments with a state-of-the-art deep learning-based rumor detection model show that augmented data can alleviate over-fitting and class imbalance caused by limited train data and can help to train complex neural networks (NNs).

TREC Incident Streams: Finding Actionable Information on Social Media

The current state-of-the-art emergency response technology is insufficient for emergency responders’ requirements, particularly for rare actionable information for which there is little prior training data available.

Atalaya at TASS 2018: Sentiment Analysis with Tweet Embeddings and Data Augmentation

This work presents the participation as team Atalaya in the task of polarity classification of tweets, which followed standard techniques in preprocessing, representation and classification, and also explored some novel ideas.

CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises

Using a crisis lexicon leads to substantial improvements in terms of recall when added to a set of crisis-specific keywords manually chosen by experts; it also helps to preserve the original distribution of message types.