Journal ArticleDOI
Opinion mining using ensemble text hidden Markov models for text classification
Mangi Kang,Jaelim Ahn,Kichun Lee +2 more
TLDR
A new sentiment analysis method, based on text-based hidden Markov models (TextHMMs), for text classification that uses a sequence of words in training texts instead of a predefined sentiment lexicon and has potential to classify implicit opinions.Citations
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Journal ArticleDOI
Text Classification Algorithms: A Survey
Kamran Kowsari,Kiana Jafari Meimandi,Mojtaba Heidarysafa,Sanjana Mendu,Laura E. Barnes,Donald E. Brown +5 more
TL;DR: A brief overview of text classification algorithms is discussed in this article, where different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods are discussed, and the limitations of each technique and their application in real-world problems are discussed.
Journal ArticleDOI
Text Classification Algorithms: A Survey
Kamran Kowsari,Kiana Jafari Meimandi,Mojtaba Heidarysafa,Sanjana Mendu,Laura E. Barnes,Donald E. Brown +5 more
TL;DR: An overview of text classification algorithms is discussed, which covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods.
Journal ArticleDOI
A survey of sentiment analysis in social media
TL;DR: A large quantity of techniques and methods are categorized and compared in the area of sentiment analysis, and different types of data and advanced tools for research are introduced, as well as their limitations.
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.
Journal ArticleDOI
Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism
TL;DR: An attention-based Bi-LSTM+CNN hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism is proposed that produces more accurate classification results, as well as higher recall and F1 scores, than individual multi-layer perceptron (MLP), CNN or L STM models as the hybrid models.
References
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Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Proceedings ArticleDOI
Convolutional Neural Networks for Sentence Classification
TL;DR: The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.
Proceedings ArticleDOI
Mining and summarizing customer reviews
Minqing Hu,Bing Liu +1 more
TL;DR: This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
Thumbs up? Sentiment Classiflcation using Machine Learning Techniques
TL;DR: In this paper, the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, was considered and three machine learning methods (Naive Bayes, maximum entropy classiflcation, and support vector machines) were employed.
Proceedings Article
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Richard Socher,Alex Perelygin,Jean Y. Wu,Jason Chuang,Christopher D. Manning,Andrew Y. Ng,Christopher Potts +6 more
TL;DR: A Sentiment Treebank that includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality, and introduces the Recursive Neural Tensor Network.