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A :) Is Worth a Thousand Words: How People Attach Sentiment to Emoticons and Words in Tweets

Related publications (16)

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Understanding the sentiment associated with cultural ecosystem services using images and text from social media

Devis Tuia

Social media is increasingly being employed to develop Cultural Ecosystem Services (CES) indicators. The image-sharing platform Flickr has been one of the most popular sources of data. Most large-scale studies, however, tend to only use the number of image ...

2024

Comparative Study on Sentence Boundary Prediction for German and English Broadcast News

Philip Neil Garner, David Imseng, Yang Wang

We present a comparative study on sentence boundary prediction for German and English broadcast news that explores generalization across different languages. In the feature extraction stage, word pause duration is firstly extracted from word aligned speech ...

Idiap2017

Twitter Sentiment Analysis (Almost) from Scratch

Rémi Philippe Lebret, Ronan Collobert, Pedro Henrique Oliveira Pinheiro

A popular application in Natural Language Processing (NLP) is the Sentiment Analysis (SA), i.e., the task of extracting contextual polarity from a given text. The social network Twitter provides an immense amount of text (called tweets) generated by users ...

Idiap2016

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While social data is being widely used in various applications such as sentiment analysis and trend prediction, its sheer size also presents great challenges for storing, sharing and processing such data. These challenges can be addressed by data summariza ...

Assoc Computing Machinery2016

Learning Explainable User Sentiment and Preferences for Information Filtering

Nikolaos Pappas

In the last decade, online social networks have enabled people to interact in many ways with each other and with content. The digital traces of such actions reveal people's preferences towards online content such as news or products. These traces often res ...

EPFL2016

Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...

École Polytechnique Fédérale de Lausanne2016

Word Embeddings for Natural Language Processing

Rémi Philippe Lebret

EPFL2016

N-gram-Based Low-Dimensional Representation for Document Classification

Rémi Philippe Lebret, Ronan Collobert

The bag-of-words (BOW) model is the common approach for classifying documents, where words are used as feature for training a classifier. This generally involves a huge number of features. Some techniques, such as Latent Semantic Analysis (LSA) or Latent D ...

2015

EmotionWatch: Visualizing Fine-Grained Emotions in Event-Related Tweets

Pearl Pu Faltings, Claudiu-Cristian Musat, Valentina Sintsova, Renato Kempter

Spectators are increasingly using social platforms to express their opinions and share their emotions during big public events. Those reactions reveal the subjective perception of the event and extend its understanding. This has motivated us to develop a s ...

2014

EmotionWatch: Visualizing Fine-Grained Emotions in Event-Related Tweets

Pearl Pu Faltings, Claudiu-Cristian Musat, Valentina Sintsova, Renato Kempter

2014