Duy Tin Vo

Also published as: Duy-Tin Vo


2016

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Don’t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text
Duy Tin Vo | Yue Zhang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Context-Sensitive Lexicon Features for Neural Sentiment Analysis
Zhiyang Teng | Duy-Tin Vo | Yue Zhang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


Neural Networks for Sentiment Analysis
Yue Zhang | Duy Tin Vo
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Sentiment analysis has been a major research topic in natural language processing (NLP). Traditionally, the problem has been attacked using discrete models and manually-defined sparse features. Over the past few years, neural network models have received increased research efforts in most sub areas of sentiment analysis, giving highly promising results. A main reason is the capability of neural models to automatically learn dense features that capture subtle semantic information over words, sentences and documents, which are difficult to model using traditional discrete features based on words and ngram patterns. This tutorial gives an introduction to neural network models for sentiment analysis, discussing the mathematics of word embeddings, sequence models and tree structured models and their use in sentiment analysis on the word, sentence and document levels, and fine-grained sentiment analysis. The tutorial covers a range of neural network models (e.g. CNN, RNN, RecNN, LSTM) and their extensions, which are employed in four main subtasks of sentiment analysis:Sentiment-oriented embeddings;Sentence-level sentiment;Document-level sentiment;Fine-grained sentiment.The content of the tutorial is divided into 3 sections of 1 hour each. We assume that the audience is familiar with linear algebra and basic neural network structures, introduce the mathematical details of the most typical models. First, we will introduce the sentiment analysis task, basic concepts related to neural network models for sentiment analysis, and show detail approaches to integrate sentiment information into embeddings. Sentence-level models will be described in the second section. Finally, we will discuss neural network models use for document-level and fine-grained sentiment.

2015

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Neural Networks for Open Domain Targeted Sentiment
Meishan Zhang | Yue Zhang | Duy-Tin Vo
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing