Character-based Neural Embeddings for Tweet Clustering

2017-03-15 · WS 2017 4 · Svitlana Vakulenko, Lyndon Nixon, Mihai Lupu

In this paper we show how the performance of tweet clustering can be improved by leveraging character-based neural networks. The proposed approach overcomes the limitations related to the vocabulary explosion in the word-based models and allows for the seamless processing of the multilingual content. Our evaluation results and code are available on-line at https://github.com/vendi12/tweet2vec_clustering

📄 PDF Abstract BibTeX arXiv:1703.05123

Code (1)

vendi12/tweet2vec_clustering 공식 구현

Tasks

Clustering

Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

2016-07-26 · Soroush Vosoughi, Prashanth Vijayaraghavan, Deb Roy

We present Tweet2Vec, a novel method for generating general-purpose vector representation of tweets. The model learns tweet embeddings using character-level CNN-LSTM encoder-decoder. We trained our model on 3 million, ra…

Decoder Semantic Similarity Semantic Textual Similarity

Clustering tweets usingWikipedia concepts

2014-05-01 · LREC 2014 5 · Guoyu Tang, Yunqing Xia, Weizhi Wang, Raymond Lau 외

Two challenging issues are notable in tweet clustering. Firstly, the sparse data problem is serious since no tweet can be longer than 140 characters. Secondly, synonymy and polysemy are rather common because users intend…

Clustering Text Clustering

Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis

2019-09-25 · Franco M. Luque

In this article we describe our participation in TASS 2019, a shared task aimed at the detection of sentiment polarity of Spanish tweets. We combined different representations such as bag-of-words, bag-of-characters, and…

Data Augmentation Sentiment Analysis Translation Word Embeddings

Deep Learning for Identification of Adverse Effect Mentions In Twitter Data

2019-08-01 · WS 2019 8 · Paul Barry, Ozlem Uzuner

Social Media Mining for Health Applications (SMM4H) Adverse Effect Mentions Shared Task challenges participants to accurately identify spans of text within a tweet that correspond to Adverse Effects (AEs) resulting from …

Impact of Feature Selection on Micro-Text Classification

2017-08-27 · Ankit Vadehra, Maura R. Grossman, Gordon V. Cormack

Social media datasets, especially Twitter tweets, are popular in the field of text classification. Tweets are a valuable source of micro-text (sometimes referred to as "micro-blogs"), and have been studied in domains suc…

Classification Clustering feature selection General Classification+8