15,780 research outputs found
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks
Sentiment analysis of online user generated content is important for many
social media analytics tasks. Researchers have largely relied on textual
sentiment analysis to develop systems to predict political elections, measure
economic indicators, and so on. Recently, social media users are increasingly
using images and videos to express their opinions and share their experiences.
Sentiment analysis of such large scale visual content can help better extract
user sentiments toward events or topics, such as those in image tweets, so that
prediction of sentiment from visual content is complementary to textual
sentiment analysis. Motivated by the needs in leveraging large scale yet noisy
training data to solve the extremely challenging problem of image sentiment
analysis, we employ Convolutional Neural Networks (CNN). We first design a
suitable CNN architecture for image sentiment analysis. We obtain half a
million training samples by using a baseline sentiment algorithm to label
Flickr images. To make use of such noisy machine labeled data, we employ a
progressive strategy to fine-tune the deep network. Furthermore, we improve the
performance on Twitter images by inducing domain transfer with a small number
of manually labeled Twitter images. We have conducted extensive experiments on
manually labeled Twitter images. The results show that the proposed CNN can
achieve better performance in image sentiment analysis than competing
algorithms.Comment: 9 pages, 5 figures, AAAI 201
Learning semantic sentence representations from visually grounded language without lexical knowledge
Current approaches to learning semantic representations of sentences often
use prior word-level knowledge. The current study aims to leverage visual
information in order to capture sentence level semantics without the need for
word embeddings. We use a multimodal sentence encoder trained on a corpus of
images with matching text captions to produce visually grounded sentence
embeddings. Deep Neural Networks are trained to map the two modalities to a
common embedding space such that for an image the corresponding caption can be
retrieved and vice versa. We show that our model achieves results comparable to
the current state-of-the-art on two popular image-caption retrieval benchmark
data sets: MSCOCO and Flickr8k. We evaluate the semantic content of the
resulting sentence embeddings using the data from the Semantic Textual
Similarity benchmark task and show that the multimodal embeddings correlate
well with human semantic similarity judgements. The system achieves
state-of-the-art results on several of these benchmarks, which shows that a
system trained solely on multimodal data, without assuming any word
representations, is able to capture sentence level semantics. Importantly, this
result shows that we do not need prior knowledge of lexical level semantics in
order to model sentence level semantics. These findings demonstrate the
importance of visual information in semantics
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
A lot of the recent success in natural language processing (NLP) has been
driven by distributed vector representations of words trained on large amounts
of text in an unsupervised manner. These representations are typically used as
general purpose features for words across a range of NLP problems. However,
extending this success to learning representations of sequences of words, such
as sentences, remains an open problem. Recent work has explored unsupervised as
well as supervised learning techniques with different training objectives to
learn general purpose fixed-length sentence representations. In this work, we
present a simple, effective multi-task learning framework for sentence
representations that combines the inductive biases of diverse training
objectives in a single model. We train this model on several data sources with
multiple training objectives on over 100 million sentences. Extensive
experiments demonstrate that sharing a single recurrent sentence encoder across
weakly related tasks leads to consistent improvements over previous methods. We
present substantial improvements in the context of transfer learning and
low-resource settings using our learned general-purpose representations.Comment: Accepted at ICLR 201
Econometrics meets sentiment : an overview of methodology and applications
The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software
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