Cross-domain sentiment classification using a sentiment sensitive thesaurus

Automatic classification of sentiment is important for numerous applications such as opinion mining, opinion summarization, contextual advertising, and market analysis. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is costly. Applying a sentiment classifier trained using labeled data for a particular domain to classify sentiment of user reviews on a different domain often results in poor performance. We propose a method to overcome this problem in cross-domain sentiment classification. First, we create a sentiment sensitive distributional thesaurus using labeled data for the source domains and unlabeled data for both source and target domains. Sentiment sensitivity is achieved in the thesaurus by incorporating document level sentiment labels in the context vectors used as the basis for measuring the distributional similarity between words. Next, we use the created thesaurus to expand feature vectors during train and test times in a binary classifier. The proposed method significantly outperforms numerous baselines and returns results that are comparable with previously proposed cross-domain sentiment classification methods. We conduct an extensive empirical analysis of the proposed method on single and multi-source domain adaptation, unsupervised and supervised domain adaptation, and numerous similarity measures for creating the sentiment sensitive thesaurus

A comparative study of Bayesian models for unsupervised sentiment detection

This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentimenttopic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

Automatically extracting polarity-bearing topics for cross-domain sentiment classification

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning

On Deep Learning in Cross-Domain Sentiment Classification

Cross-domain sentiment classification consists in distinguishing positive and negative reviews of a target domain by using knowledge extracted and transferred from a heterogeneous source domain. Cross-domain solutions aim at overcoming the costly pre-classification of each new training set by human experts. Despite the potential business relevance of this research thread, the existing ad hoc solutions are still not scalable with real large text sets. Scalable Deep Learning techniques have been effectively applied to in-domain text classification, by training and categorising documents belonging to the same domain. This work analyses the cross-domain efficacy of a well-known unsupervised Deep Learning approach for text mining, called Paragraph Vector, comparing its performance with a method based on Markov Chain developed ad hoc for cross-domain sentiment classification. The experiments show that, once enough data is available for training, Paragraph Vector achieves accuracy equiva lent to Markov Chain both in-domain and cross-domain, despite no explicit transfer learning capability. The outcome suggests that combining Deep Learning with transfer learning techniques could be a breakthrough of ad hoc cross-domain sentiment solutions in big data scenarios. This opinion is confirmed by a really simple multi-source experiment we tried to improve transfer learning, which increases the accuracy of cross-domain sentiment classification

Approaching Sentiment Analysis by Using Semi-supervised Learning of Multidimensional Classifiers

Sentiment Analysis is defined as the computational study of opinions, sentiments and emotions expressed in text. Within this broad field, most of the work has been focused on either Sentiment Polarity classification, where a text is classified as having positive or negative sentiment, or Subjectivity classification, in which a text is classified as being subjective or objective. However, in this paper, we consider instead a real-world problem in which the attitude of the author is characterised by three different (but related) target variables: Subjectivity, Sentiment Polarity, Will to Influence, unlike the two previously stated problems, where there is only a single variable to be predicted. For that reason, the (uni-dimensional) common approaches used in this area yield suboptimal solutions to this problem. In order to bridge this gap, we propose, for the first time, the use of the novel multi-dimensional classification paradigm in the Sentiment Analysis domain. This methodology is able to join the different target variables in the same classification task so as to take advantage of the potential statistical relations between them. In addition, and in order to take advantage of the huge amount of unlabelled information available nowadays in this context, we propose the extension of the multi-dimensional classification framework to the semi-supervised domain. Experimental results for this problem show that our semi-supervised multi-dimensional approach outperforms the most common Sentiment Analysis approaches, concluding that our approach is beneficial to improve the recognition rates for this problem, and in extension, could be considered to solve future Sentiment Analysis problems