3,625 research outputs found
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Traditional sentiment analysis approaches tackle problems like ternary
(3-category) and fine-grained (5-category) classification by learning the tasks
separately. We argue that such classification tasks are correlated and we
propose a multitask approach based on a recurrent neural network that benefits
by jointly learning them. Our study demonstrates the potential of multitask
models on this type of problems and improves the state-of-the-art results in
the fine-grained sentiment classification problem.Comment: International ACM SIGIR Conference on Research and Development in
Information Retrieval 201
TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics
Online social networking services allow their users to post content in the
form of text, images or videos. The main mechanism driving content diffusion is
the possibility for users to re-share the content posted by their social
connections, which may then cascade across the system. A fundamental problem
when studying information cascades is the possibility to develop sound
mathematical models, whose parameters can be calibrated on empirical data, in
order to predict the future course of a cascade after a window of observation.
In this paper, we focus on Twitter and, in particular, on the temporal patterns
of retweet activity for an original tweet. We model the system by
Time-Dependent Hawkes process (TiDeH), which properly takes into account the
circadian nature of the users and the aging of information. The input of the
prediction model are observed retweet times and structural information about
the underlying social network. We develop a procedure for parameter
optimization and for predicting the future profiles of retweet activity at
different time resolutions. We validate our methodology on a large corpus of
Twitter data and demonstrate its systematic improvement over existing
approaches in all the time regimes.Comment: The manuscript has been accepted in the 10th International AAAI
Conference on Web and Social Media (ICWSM 2016
Task-specific Word Identification from Short Texts Using a Convolutional Neural Network
Task-specific word identification aims to choose the task-related words that
best describe a short text. Existing approaches require well-defined seed words
or lexical dictionaries (e.g., WordNet), which are often unavailable for many
applications such as social discrimination detection and fake review detection.
However, we often have a set of labeled short texts where each short text has a
task-related class label, e.g., discriminatory or non-discriminatory, specified
by users or learned by classification algorithms. In this paper, we focus on
identifying task-specific words and phrases from short texts by exploiting
their class labels rather than using seed words or lexical dictionaries. We
consider the task-specific word and phrase identification as feature learning.
We train a convolutional neural network over a set of labeled texts and use
score vectors to localize the task-specific words and phrases. Experimental
results on sentiment word identification show that our approach significantly
outperforms existing methods. We further conduct two case studies to show the
effectiveness of our approach. One case study on a crawled tweets dataset
demonstrates that our approach can successfully capture the
discrimination-related words/phrases. The other case study on fake review
detection shows that our approach can identify the fake-review words/phrases.Comment: accepted by Intelligent Data Analysis, an International Journa
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