Journal ArticleDOI
A review of natural language processing techniques for opinion mining systems
Shiliang Sun,Chen Luo,Junyu Chen +2 more
TLDR
This paper introduces general NLP techniques which are required for text preprocessing, and investigates the approaches of opinion mining for different levels and situations, and introduces comparative opinion mining and deep learning approaches for opinion mining.About:
This article is published in Information Fusion.The article was published on 2017-07-01. It has received 381 citations till now. The article focuses on the topics: Biomedical text mining & Automatic summarization.read more
Citations
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Journal ArticleDOI
A review of affective computing
TL;DR: This first of its kind, comprehensive literature review of the diverse field of affective computing focuses mainly on the use of audio, visual and text information for multimodal affect analysis, and outlines existing methods for fusing information from different modalities.
Journal ArticleDOI
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey
TL;DR: In this article, the authors investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling.
Posted Content
Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey
TL;DR: In this article, the authors investigated the research development, current trends and intellectual structure of topic modeling based on Latent Dirichlet Allocation (LDA), and summarized challenges and introduced famous tools and datasets in topic modelling based on LDA.
Journal ArticleDOI
A survey on classification techniques for opinion mining and sentiment analysis
TL;DR: This paper represents a complete, multilateral and systematic review of opinion mining and sentiment analysis to classify available methods and compare their advantages and drawbacks, in order to have better understanding of available challenges and solutions to clarify the future direction.
Journal ArticleDOI
A recent overview of the state-of-the-art elements of text classification
TL;DR: Six baseline elements of text classification including data collection, data analysis for labelling, feature construction and weighing, feature selection and projection, training of a classification model, and solution evaluation are described.
References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.