3,724 research outputs found
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification
In this paper, we propose a variational approach to weakly supervised
document-level multi-aspect sentiment classification. Instead of using
user-generated ratings or annotations provided by domain experts, we use
target-opinion word pairs as "supervision." These word pairs can be extracted
by using dependency parsers and simple rules. Our objective is to predict an
opinion word given a target word while our ultimate goal is to learn a
sentiment polarity classifier to predict the sentiment polarity of each aspect
given a document. By introducing a latent variable, i.e., the sentiment
polarity, to the objective function, we can inject the sentiment polarity
classifier to the objective via the variational lower bound. We can learn a
sentiment polarity classifier by optimizing the lower bound. We show that our
method can outperform weakly supervised baselines on TripAdvisor and
BeerAdvocate datasets and can be comparable to the state-of-the-art supervised
method with hundreds of labels per aspect.Comment: Accepted by NAACL-HLT 201
Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision
Aspect Term Extraction (ATE) detects opinionated aspect terms in sentences or
text spans, with the end goal of performing aspect-based sentiment analysis.
The small amount of available datasets for supervised ATE and the fact that
they cover only a few domains raise the need for exploiting other data sources
in new and creative ways. Publicly available review corpora contain a plethora
of opinionated aspect terms and cover a larger domain spectrum. In this paper,
we first propose a method for using such review corpora for creating a new
dataset for ATE. Our method relies on an attention mechanism to select
sentences that have a high likelihood of containing actual opinionated aspects.
We thus improve the quality of the extracted aspects. We then use the
constructed dataset to train a model and perform ATE with distant supervision.
By evaluating on human annotated datasets, we prove that our method achieves a
significantly improved performance over various unsupervised and supervised
baselines. Finally, we prove that sentence selection matters when it comes to
creating new datasets for ATE. Specifically, we show that, using a set of
selected sentences leads to higher ATE performance compared to using the whole
sentence set
Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention
Previous approaches to training syntax-based sentiment classification models
required phrase-level annotated corpora, which are not readily available in
many languages other than English. Thus, we propose the use of tree-structured
Long Short-Term Memory with an attention mechanism that pays attention to each
subtree of the parse tree. Experimental results indicate that our model
achieves the state-of-the-art performance in a Japanese sentiment
classification task.Comment: 10 pages; PACLIC 201
Semi-Supervised Affective Meaning Lexicon Expansion Using Semantic and Distributed Word Representations
In this paper, we propose an extension to graph-based sentiment lexicon
induction methods by incorporating distributed and semantic word
representations in building the similarity graph to expand a three-dimensional
sentiment lexicon. We also implemented and evaluated the label propagation
using four different word representations and similarity metrics. Our
comprehensive evaluation of the four approaches was performed on a single data
set, demonstrating that all four methods can generate a significant number of
new sentiment assignments with high accuracy. The highest correlations
(tau=0.51) and the lowest error (mean absolute error < 1.1%), obtained by
combining both the semantic and the distributional features, outperformed the
distributional-based and semantic-based label-propagation models and approached
a supervised algorithm
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
Effective LSTMs for Target-Dependent Sentiment Classification
Target-dependent sentiment classification remains a challenge: modeling the
semantic relatedness of a target with its context words in a sentence.
Different context words have different influences on determining the sentiment
polarity of a sentence towards the target. Therefore, it is desirable to
integrate the connections between target word and context words when building a
learning system. In this paper, we develop two target dependent long short-term
memory (LSTM) models, where target information is automatically taken into
account. We evaluate our methods on a benchmark dataset from Twitter. Empirical
results show that modeling sentence representation with standard LSTM does not
perform well. Incorporating target information into LSTM can significantly
boost the classification accuracy. The target-dependent LSTM models achieve
state-of-the-art performances without using syntactic parser or external
sentiment lexicons.Comment: 7 pages, 3 figures published in COLING 201
Emotion Detection in Text: a Review
In recent years, emotion detection in text has become more popular due to its
vast potential applications in marketing, political science, psychology,
human-computer interaction, artificial intelligence, etc. Access to a huge
amount of textual data, especially opinionated and self-expression text also
played a special role to bring attention to this field. In this paper, we
review the work that has been done in identifying emotion expressions in text
and argue that although many techniques, methodologies, and models have been
created to detect emotion in text, there are various reasons that make these
methods insufficient. Although, there is an essential need to improve the
design and architecture of current systems, factors such as the complexity of
human emotions, and the use of implicit and metaphorical language in expressing
it, lead us to think that just re-purposing standard methodologies will not be
enough to capture these complexities, and it is important to pay attention to
the linguistic intricacies of emotion expression
Investigating the Working of Text Classifiers
Text classification is one of the most widely studied tasks in natural
language processing. Motivated by the principle of compositionality, large
multilayer neural network models have been employed for this task in an attempt
to effectively utilize the constituent expressions. Almost all of the reported
work train large networks using discriminative approaches, which come with a
caveat of no proper capacity control, as they tend to latch on to any signal
that may not generalize. Using various recent state-of-the-art approaches for
text classification, we explore whether these models actually learn to compose
the meaning of the sentences or still just focus on some keywords or lexicons
for classifying the document. To test our hypothesis, we carefully construct
datasets where the training and test splits have no direct overlap of such
lexicons, but overall language structure would be similar. We study various
text classifiers and observe that there is a big performance drop on these
datasets. Finally, we show that even simple models with our proposed
regularization techniques, which disincentivize focusing on key lexicons, can
substantially improve classification accuracy.Comment: Proceedings of COLING 2018, the 27th International Conference on
Computational Linguistics: Technical Papers (COLING 2018), NIPS 2017 Workshop
on Deep Learning: Bridging Theory and Practic
EvoMSA: A Multilingual Evolutionary Approach for Sentiment Analysis
Sentiment analysis (SA) is a task related to understanding people's feelings
in written text; the starting point would be to identify the polarity level
(positive, neutral or negative) of a given text, moving on to identify emotions
or whether a text is humorous or not. This task has been the subject of several
research competitions in a number of languages, e.g., English, Spanish, and
Arabic, among others. In this contribution, we propose an SA system, namely
EvoMSA, that unifies our participating systems in various SA competitions,
making it domain independent and multilingual by processing text using only
language-independent techniques. EvoMSA is a classifier, based on Genetic
Programming, that works by combining the output of different text classifiers
and text models to produce the final prediction. We analyze EvoMSA on different
SA competitions to provide a global overview of its performance, and as the
results show, EvoMSA is competitive obtaining top rankings in several SA
competitions. Furthermore, we performed an analysis of EvoMSA's components to
measure their contribution to the performance; the idea is to facilitate a
practitioner or newcomer to implement a competitive SA classifier. Finally, it
is worth to mention that EvoMSA is available as open-source software
Developing a concept-level knowledge base for sentiment analysis in Singlish
In this paper, we present Singlish sentiment lexicon, a concept-level
knowledge base for sentiment analysis that associates multiword expressions to
a set of emotion labels and a polarity value. Unlike many other sentiment
analysis resources, this lexicon is not built by manually labeling pieces of
knowledge coming from general NLP resources such as WordNet or DBPedia.
Instead, it is automatically constructed by applying graph-mining and
multi-dimensional scaling techniques on the affective common-sense knowledge
collected from three different sources. This knowledge is represented
redundantly at three levels: semantic network, matrix, and vector space.
Subsequently, the concepts are labeled by emotions and polarity through the
ensemble application of spreading activation, neural networks and an emotion
categorization model.Comment: CICLing 201
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