Данные Twitter

Sentiment analysis on Twitter data has attract- ed much attention recently. In this paper, we focus on target-dependent Twitter sentiment classification; namely, given a query, we clas- sify the sentiments …...

Different demographics, e.g., gender or age, can demonstrate substantial variation in their language use, particularly in informal contexts such as social media. In this paper we focus on learning gender …...

With 19%–28% of Internet users partici- pating in online health discussions, it be- came imperative to be able to detect and analyze posted personal health informa- tion (PHI). In this …...

Extensive experiments have validated the ef- fectiveness of the corpus-based method for classifying the word’s sentiment polarity. However, no work is done for comparing d- ifferent corpora in the polarity …...

This paper describes a dual-classifier ap- proach to contextual sentiment analysis at the SemEval-2013 Task 2. Contextual analysis of polarity focuses on a word or phrase, rather than the broader …...

We experiment with using different sources of distant supervision to guide unsupervised and semi-supervised adaptation of part-of-speech (POS) and named entity taggers (NER) to Twitter. We show that a particularly …...

Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predic- tive lexica (words and weights) for age and gender using regression and classification …...

Sentiment Analysis has become an in- creasingly important research topic. This paper describes our approach to building a system for the Sentiment Analysis in Twit- ter task of the SemEval-2014 …...

AbstractSeveral works in Natural Language Processing have recently looked into part-of-speech annotation of Twitter data and typically used their own data sets. Since conventions on Twitter change rapidly, models often …...

There is a growing interest in automatically predicting the gender and age of authors from texts. However, most research so far ignores that language use is related to the social …...

We present the INESC-ID system for the mes- sage polarity classification task of SemEval 2015. The proposed system does not make use of any hand-coded features or linguistic resources. It …...

We present a simple, yet effective approach to adapt part-of-speech (POS) taggers to new domains. Our approach only requires a dic- tionary and large amounts of unlabeled tar- get data. …...

In named entity recognition task especially for massive data like Twitter, having a large amount of high quality gazetteers can allevi- ate the problem of training data scarcity. One could …...

When considering a social media corpus, we often have access to structural information about how messages are flowing between peo- ple or organizations. This information is par- ticularly useful when …...

Public health surveillance is challenging due to difficulties accessing medical data in real-time. We present a novel, ef- fective and computationally inexpensive method for syndromic surveillance using Twitter data. The …...

AbstractWe propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation …...

AbstractVariation in language is ubiquitous, particularly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the author. …...

We address the problem of mining code-switched data from the web, where code-switching is defined as the tendency of bilinguals to switch between their multiple languages both across and within …...

AbstractBecause of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This situation worsened …...

AbstractIn this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person’s expressions by discovering both textual and contextual information. In particular, we construct …...

AbstractThis paper describes our participation in the SemEval 2019 Task 3 - Contextual Emotion Detection in Text. This task aims to identify emotions, viz. happiness, anger, sadness in the context …...

AbstractWe introduce an approach to multilingual Offensive Language Detection based on the mBERT transformer model. We download extra training data from Twitter in English, Danish, and Turkish, and use it …...

AbstractMuch as the social landscape in which languages are spoken shifts, language too evolves to suit the needs of its users. Lexical semantic change analysis is a burgeoning field of …...

AbstractEmotion detection from social media posts has attracted noticeable attention from natural language processing (NLP) community in recent years. The ways for obtaining gold labels for training and testing of …...

AbstractMultiple studies have demonstrated that behaviors expressed on online social media platforms can indicate the mental health state of an individual. The widespread availability of such data has spurred interest …...

AbstractWe present CoNTACT: a Dutch language model adapted to the domain of COVID-19 tweets. The model was developed by continuing the pre-training phase of RobBERT (Delobelle et al., 2020) by …...

AbstractAge-related stereotypes are pervasive in our society, and yet have been under-studied in the NLP community. Here, we present a method for extracting age-related stereotypes from Twitter data, generating a …...

AbstractTransformer-based language models such as Bidirectional Encoder Representations from Transformers (BERT) are now mainstream in the NLP field, but extensions to languages other than English, to new domains and/or to …...