3,924 research outputs found
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification
Deep learning approaches for sentiment classification do not fully exploit
sentiment linguistic knowledge. In this paper, we propose a
Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the
problem by integrating three kinds of sentiment linguistic knowledge (e.g.,
sentiment lexicon, negation words, intensity words) into the deep neural
network via attention mechanisms. By using various types of sentiment
resources, MEAN utilizes sentiment-relevant information from different
representation subspaces, which makes it more effective to capture the overall
semantics of the sentiment, negation and intensity words for sentiment
prediction. The experimental results demonstrate that MEAN has robust
superiority over strong competitors
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
Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddings
There exist two main approaches to automatically extract affective
orientation: lexicon-based and corpus-based. In this work, we argue that these
two methods are compatible and show that combining them can improve the
accuracy of emotion classifiers. In particular, we introduce a novel variant of
the Label Propagation algorithm that is tailored to distributed word
representations, we apply batch gradient descent to accelerate the optimization
of label propagation and to make the optimization feasible for large graphs,
and we propose a reproducible method for emotion lexicon expansion. We conclude
that label propagation can expand an emotion lexicon in a meaningful way and
that the expanded emotion lexicon can be leveraged to improve the accuracy of
an emotion classifier
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
Distance Metric Learning for Aspect Phrase Grouping
Aspect phrase grouping is an important task in aspect-level sentiment
analysis. It is a challenging problem due to polysemy and context dependency.
We propose an Attention-based Deep Distance Metric Learning (ADDML) method, by
considering aspect phrase representation as well as context representation.
First, leveraging the characteristics of the review text, we automatically
generate aspect phrase sample pairs for distant supervision. Second, we feed
word embeddings of aspect phrases and their contexts into an attention-based
neural network to learn feature representation of contexts. Both aspect phrase
embedding and context embedding are used to learn a deep feature subspace for
measure the distances between aspect phrases for K-means clustering.
Experiments on four review datasets show that the proposed method outperforms
state-of-the-art strong baseline methods
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
Modeling Rich Contexts for Sentiment Classification with LSTM
Sentiment analysis on social media data such as tweets and weibo has become a
very important and challenging task. Due to the intrinsic properties of such
data, tweets are short, noisy, and of divergent topics, and sentiment
classification on these data requires to modeling various contexts such as the
retweet/reply history of a tweet, and the social context about authors and
relationships. While few prior study has approached the issue of modeling
contexts in tweet, this paper proposes to use a hierarchical LSTM to model rich
contexts in tweet, particularly long-range context. Experimental results show
that contexts can help us to perform sentiment classification remarkably
better
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
Sentiment analysis as a field has come a long way since it was first
introduced as a task nearly 20 years ago. It has widespread commercial
applications in various domains like marketing, risk management, market
research, and politics, to name a few. Given its saturation in specific
subtasks -- such as sentiment polarity classification -- and datasets, there is
an underlying perception that this field has reached its maturity. In this
article, we discuss this perception by pointing out the shortcomings and
under-explored, yet key aspects of this field that are necessary to attain true
sentiment understanding. We analyze the significant leaps responsible for its
current relevance. Further, we attempt to chart a possible course for this
field that covers many overlooked and unanswered questions.Comment: Published in the IEEE Transactions on Affective Computing (TAFFC
Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey
This work investigates the role of factors like training method, training
corpus size and thematic relevance of texts in the performance of word
embedding features on sentiment analysis of tweets, song lyrics, movie reviews
and item reviews. We also explore specific training or post-processing methods
that can be used to enhance the performance of word embeddings in certain tasks
or domains. Our empirical observations indicate that models trained with
multithematic texts that are large and rich in vocabulary are the best in
answering syntactic and semantic word analogy questions. We further observe
that influence of thematic relevance is stronger on movie and phone reviews,
but weaker on tweets and lyrics. These two later domains are more sensitive to
corpus size and training method, with Glove outperforming Word2vec. "Injecting"
extra intelligence from lexicons or generating sentiment specific word
embeddings are two prominent alternatives for increasing performance of word
embedding features.Comment: 20 pages, 16 figures, 15 table
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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