8,791 research outputs found
Beyond Sentiment: The Manifold of Human Emotions
Sentiment analysis predicts the presence of positive or negative emotions in
a text document. In this paper we consider higher dimensional extensions of the
sentiment concept, which represent a richer set of human emotions. Our approach
goes beyond previous work in that our model contains a continuous manifold
rather than a finite set of human emotions. We investigate the resulting model,
compare it to psychological observations, and explore its predictive
capabilities. Besides obtaining significant improvements over a baseline
without manifold, we are also able to visualize different notions of positive
sentiment in different domains.Comment: 15 pages, 7 figure
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Tagging news articles or blog posts with relevant tags from a collection of
predefined ones is coined as document tagging in this work. Accurate tagging of
articles can benefit several downstream applications such as recommendation and
search. In this work, we propose a novel yet simple approach called DocTag2Vec
to accomplish this task. We substantially extend Word2Vec and Doc2Vec---two
popular models for learning distributed representation of words and documents.
In DocTag2Vec, we simultaneously learn the representation of words, documents,
and tags in a joint vector space during training, and employ the simple
-nearest neighbor search to predict tags for unseen documents. In contrast
to previous multi-label learning methods, DocTag2Vec directly deals with raw
text instead of provided feature vector, and in addition, enjoys advantages
like the learning of tag representation, and the ability of handling newly
created tags. To demonstrate the effectiveness of our approach, we conduct
experiments on several datasets and show promising results against
state-of-the-art methods.Comment: 10 page
Language Without Words: A Pointillist Model for Natural Language Processing
This paper explores two separate questions: Can we perform natural language
processing tasks without a lexicon?; and, Should we? Existing natural language
processing techniques are either based on words as units or use units such as
grams only for basic classification tasks. How close can a machine come to
reasoning about the meanings of words and phrases in a corpus without using any
lexicon, based only on grams?
Our own motivation for posing this question is based on our efforts to find
popular trends in words and phrases from online Chinese social media. This form
of written Chinese uses so many neologisms, creative character placements, and
combinations of writing systems that it has been dubbed the "Martian Language."
Readers must often use visual queues, audible queues from reading out loud, and
their knowledge and understanding of current events to understand a post. For
analysis of popular trends, the specific problem is that it is difficult to
build a lexicon when the invention of new ways to refer to a word or concept is
easy and common. For natural language processing in general, we argue in this
paper that new uses of language in social media will challenge machines'
abilities to operate with words as the basic unit of understanding, not only in
Chinese but potentially in other languages.Comment: 5 pages, 2 figure
Crowdsourced real-world sensing: sentiment analysis and the real-time web
The advent of the real-time web is proving both challeng-
ing and at the same time disruptive for a number of areas of research,
notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis
and discusses the motivations and challenges behind such a direction
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