Related papers: KeyXtract Twitter Model - An Essential Keywords Ex…

Twitter is a well-known microblogging social site where users express their views and opinions in real-time. As a result, tweets tend to contain valuable information. With the advancements of deep learning in the domain of natural language…

Keyphrase extraction as a task to identify important words or phrases from a text, is a crucial process to identify main topics when analyzing texts from a social media platform. In our study, we focus on text written in Indonesia language…

While keyphrase extraction has received considerable attention in recent years, relatively few studies exist on extracting keyphrases from social media platforms such as Twitter, and even fewer for extracting disaster-related keyphrases…

We present TweeNLP, a one-stop portal that organizes Twitter's natural language processing (NLP) data and builds a visualization and exploration platform. It curates 19,395 tweets (as of April 2021) from various NLP conferences and general…

Twitter with over 500 million users globally, generates over 100,000 tweets per minute . The 140 character limit per tweet, perhaps unintentionally, encourages users to use shorthand notations and to strip spellings to their bare minimum…

In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic focus areas such as sentiment analysis and named entity…

Keyphrases efficiently summarize a document's content and are used in various document processing and retrieval tasks. Several unsupervised techniques and classifiers exist for extracting keyphrases from text documents. Most of these…

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 such as sentiment…

Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddings of sentences labelled as semantically similar by annotators. Since big labelled datasets are rare, in particular for non-English…

Social media platforms are vital resources for sharing self-reported health experiences, offering rich data on various health topics. Despite advancements in Natural Language Processing (NLP) enabling large-scale social media data analysis,…

The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. Through the analysis of collected Twitter data, models were developed for classifying…

Processing of raw text is the crucial first step in text classification and sentiment analysis. However, text processing steps are often performed using off-the-shelf routines and pre-built word dictionaries without optimizing for domain,…

With the growth of social medias, such as Twitter, plenty of user-generated data emerge daily. The short texts published on Twitter -- the tweets -- have earned significant attention as a rich source of information to guide many…

The performance of a Part-of-speech (POS) tagger is highly dependent on the domain ofthe processed text, and for many domains there is no or only very little training data available. This work addresses the problem of POS tagging noisy…

The development of deep neural networks and the emergence of pre-trained language models such as BERT allow to increase performance on many NLP tasks. However, these models do not meet the same popularity for tweet summarization, which can…

To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and…

Twitter is a microblogging service for sending short, public text messages (tweets) that has recently received more attention in scientific comunity. In the works of Sasaki et al. (2010) and Earle et al., (2011) the authors explored the…

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character…

Twitter messages (tweets) contain various types of information, which include health-related information. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily life. In this work,…

For those languages which use it, capitalization is an important signal for the fundamental NLP tasks of Named Entity Recognition (NER) and Part of Speech (POS) tagging. In fact, it is such a strong signal that model performance on these…