Sentiment Analysis Using Deep Learning: A Comparison Between Chinese And English

With the increasing popularity of opinion-rich resources, opinion mining and sentiment analysis has received increasing attention. Sentiment analysis is one of the most effective ways to find the opinion of authors. By mining what people think, sentiment analysis can provide the basis for decision making. Most of the objects of analysis are text data, such as Facebook status and movie reviews. Despite many sentiment classification models having good performance on English corpora, they are not good at Chinese or other languages. Traditional sentiment approaches impose many restrictions on the raw data, and they don't have enough capacity to deal with long-distance sequential dependencies. So, we propose a model based on recurrent neural network model using a context vector space model. Chinese information entropy is typically higher than English, we therefore hypothesise that context vector space model can be used to improve the accuracy of sentiment analysis. Our algorithm represents each complex input by a dense vector trained to translate sequence data to another sequence, like the translation of English and French. Then we build a recurrent neural network with the Long-Short-Term Memory model to deal the long-distance dependencies in input data, such as movie review. The results show that our approach has promise but still has a lot of room for improvement

Evaluation in Discourse: a Corpus-Based Study

This paper describes the CASOAR corpus, the first manually annotated corpus that explores the impact of discourse structure on sentiment analysis with a study of movie reviews in French and in English as well as letters to the editor in French. While annotating opinions at the expression, the sentence or the document level is a well-established task and relatively straightforward, discourse annotation remains difficult, especially for non-experts. Therefore, combining both annotations poses several methodological problems that we address here. We propose a multi-layered annotation scheme that includes: the complete discourse structure according to the Segmented Discourse Representation Theory, the opinion orientation of elementary discourse units and opinion expressions, and their associated features. We detail each layer, explore the interactions between them and discuss our results. In particular, we examine the correlation between discourse and semantic category of opinion expressions, the impact of discourse relations on both subjectivity and polarity analysis and the impact of discourse on the determination of the overall opinion of a document. Our results demonstrate that discourse is an important cue for sentiment analysis, at least for the corpus genres we have studied

General Purpose Textual Sentiment Analysis and Emotion Detection Tools

Textual sentiment analysis and emotion detection consists in retrieving the sentiment or emotion carried by a text or document. This task can be useful in many domains: opinion mining, prediction, feedbacks, etc. However, building a general purpose tool for doing sentiment analysis and emotion detection raises a number of issues, theoretical issues like the dependence to the domain or to the language but also pratical issues like the emotion representation for interoperability. In this paper we present our sentiment/emotion analysis tools, the way we propose to circumvent the di culties and the applications they are used for.Comment: Workshop on Emotion and Computing (2013