5,507 research outputs found
Gated Convolutional Neural Networks for Domain Adaptation
Domain Adaptation explores the idea of how to maximize performance on a
target domain, distinct from source domain, upon which the classifier was
trained. This idea has been explored for the task of sentiment analysis
extensively. The training of reviews pertaining to one domain and evaluation on
another domain is widely studied for modeling a domain independent algorithm.
This further helps in understanding correlation between domains. In this paper,
we show that Gated Convolutional Neural Networks (GCN) perform effectively at
learning sentiment analysis in a manner where domain dependant knowledge is
filtered out using its gates. We perform our experiments on multiple gate
architectures: Gated Tanh ReLU Unit (GTRU), Gated Tanh Unit (GTU) and Gated
Linear Unit (GLU). Extensive experimentation on two standard datasets relevant
to the task, reveal that training with Gated Convolutional Neural Networks give
significantly better performance on target domains than regular convolution and
recurrent based architectures. While complex architectures like attention,
filter domain specific knowledge as well, their complexity order is remarkably
high as compared to gated architectures. GCNs rely on convolution hence gaining
an upper hand through parallelization.Comment: Accepted Long Paper at 24th International Conference on Applications
of Natural Language to Information Systems, June 2019, MediaCityUK Campus,
United Kingdo
Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings
Our study employs sentiment analysis to evaluate the compatibility of
Amazon.com reviews with their corresponding ratings. Sentiment analysis is the
task of identifying and classifying the sentiment expressed in a piece of text
as being positive or negative. On e-commerce websites such as Amazon.com,
consumers can submit their reviews along with a specific polarity rating. In
some instances, there is a mismatch between the review and the rating. To
identify the reviews with mismatched ratings we performed sentiment analysis
using deep learning on Amazon.com product review data. Product reviews were
converted to vectors using paragraph vector, which then was used to train a
recurrent neural network with gated recurrent unit. Our model incorporated both
semantic relationship of review text and product information. We also developed
a web service application that predicts the rating score for a submitted review
using the trained model and if there is a mismatch between predicted rating
score and submitted rating score, it provides feedback to the reviewer.Comment: 15 pages, 10 figures, 3 tables, journal articl
Effectiveness of Self Normalizing Neural Networks for Text Classification
Self Normalizing Neural Networks(SNN) proposed on Feed Forward Neural
Networks(FNN) outperform regular FNN architectures in various machine learning
tasks. Particularly in the domain of Computer Vision, the activation function
Scaled Exponential Linear Units (SELU) proposed for SNNs, perform better than
other non linear activations such as ReLU. The goal of SNN is to produce a
normalized output for a normalized input. Established neural network
architectures like feed forward networks and Convolutional Neural Networks(CNN)
lack the intrinsic nature of normalizing outputs. Hence, requiring additional
layers such as Batch Normalization. Despite the success of SNNs, their
characteristic features on other network architectures like CNN haven't been
explored, especially in the domain of Natural Language Processing. In this
paper we aim to show the effectiveness of proposed, Self Normalizing
Convolutional Neural Networks(SCNN) on text classification. We analyze their
performance with the standard CNN architecture used on several text
classification datasets. Our experiments demonstrate that SCNN achieves
comparable results to standard CNN model with significantly fewer parameters.
Furthermore it also outperforms CNN with equal number of parameters.Comment: Accepted Long Paper at 20th International Conference on Computational
Linguistics and Intelligent Text Processing, April 2019, La Rochelle, Franc
Tensor Fusion Network for Multimodal Sentiment Analysis
Multimodal sentiment analysis is an increasingly popular research area, which
extends the conventional language-based definition of sentiment analysis to a
multimodal setup where other relevant modalities accompany language. In this
paper, we pose the problem of multimodal sentiment analysis as modeling
intra-modality and inter-modality dynamics. We introduce a novel model, termed
Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed
approach is tailored for the volatile nature of spoken language in online
videos as well as accompanying gestures and voice. In the experiments, our
model outperforms state-of-the-art approaches for both multimodal and unimodal
sentiment analysis.Comment: Accepted as full paper in EMNLP 201
An Attention-Gated Convolutional Neural Network for Sentence Classification
The classification of sentences is very challenging, since sentences contain
the limited contextual information. In this paper, we proposed an
Attention-Gated Convolutional Neural Network (AGCNN) for sentence
classification, which generates attention weights from the feature's context
windows of different sizes by using specialized convolution encoders. It makes
full use of limited contextual information to extract and enhance the influence
of important features in predicting the sentence's category. Experimental
results demonstrated that our model can achieve up to 3.1% higher accuracy than
standard CNN models, and gain competitive results over the baselines on four
out of the six tasks. Besides, we designed an activation function, namely,
Natural Logarithm rescaled Rectified Linear Unit (NLReLU). Experiments showed
that NLReLU can outperform ReLU and is comparable to other well-known
activation functions on AGCNN.Comment: Accepted for publication in the Intelligent Data Analysis journal, 19
pages, 4 figures and 5 table
Deep Learning for Sentiment Analysis : A Survey
Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.Comment: 34 pages, 9 figures, 2 table
Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models
With the popularity of social networks, and e-commerce websites, sentiment
analysis has become a more active area of research in the past few years. On a
high level, sentiment analysis tries to understand the public opinion about a
specific product or topic, or trends from reviews or tweets. Sentiment analysis
plays an important role in better understanding customer/user opinion, and also
extracting social/political trends. There has been a lot of previous works for
sentiment analysis, some based on hand-engineering relevant textual features,
and others based on different neural network architectures. In this work, we
present a model based on an ensemble of long-short-term-memory (LSTM), and
convolutional neural network (CNN), one to capture the temporal information of
the data, and the other one to extract the local structure thereof. Through
experimental results, we show that using this ensemble model we can outperform
both individual models. We are also able to achieve a very high accuracy rate
compared to the previous works
Supervised Sentiment Classification with CNNs for Diverse SE Datasets
Sentiment analysis, a popular technique for opinion mining, has been used by
the software engineering research community for tasks such as assessing app
reviews, developer emotions in issue trackers and developer opinions on APIs.
Past research indicates that state-of-the-art sentiment analysis techniques
have poor performance on SE data. This is because sentiment analysis tools are
often designed to work on non-technical documents such as movie reviews. In
this study, we attempt to solve the issues with existing sentiment analysis
techniques for SE texts by proposing a hierarchical model based on
convolutional neural networks (CNN) and long short-term memory (LSTM) trained
on top of pre-trained word vectors. We assessed our model's performance and
reliability by comparing it with a number of frequently used sentiment analysis
tools on five gold standard datasets. Our results show that our model pushes
the state of the art further on all datasets in terms of accuracy. We also show
that it is possible to get better accuracy after labelling a small sample of
the dataset and re-training our model rather than using an unsupervised
classifier
Feature Weight Tuning for Recursive Neural Networks
This paper addresses how a recursive neural network model can automatically
leave out useless information and emphasize important evidence, in other words,
to perform "weight tuning" for higher-level representation acquisition. We
propose two models, Weighted Neural Network (WNN) and Binary-Expectation Neural
Network (BENN), which automatically control how much one specific unit
contributes to the higher-level representation. The proposed model can be
viewed as incorporating a more powerful compositional function for embedding
acquisition in recursive neural networks. Experimental results demonstrate the
significant improvement over standard neural models
Review Helpfulness Assessment based on Convolutional Neural Network
In this paper we describe the implementation of a convolutional neural
network (CNN) used to assess online review helpfulness. To our knowledge, this
is the first use of this architecture to address this problem. We explore the
impact of two related factors impacting CNN performance: different word
embedding initializations and different input review lengths. We also propose
an approach to combining rating star information with review text to further
improve prediction accuracy. We demonstrate that this can improve the overall
accuracy by 2%. Finally, we evaluate the method on a benchmark dataset and show
an improvement in accuracy relative to published results for traditional
methods of 2.5% for a model trained using only review text and 4.24% for a
model trained on a combination of rating star information and review text
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