Sentiment analysis on user reviews helps to keep track of user reactions towards products, and make advices to users about what to buy. State-of-the-art review-level sentiment classification techniques could give pretty good precisions of above 90 . However, current phrase-level sentiment analysis approaches might only give sentiment polarity labelling precisions of around 70 80 , which is far from satisfaction and restricts its application in many practical tasks. In this paper, we focus on the problem of phrase-level sentiment polarity labelling and attempt to bridge the gap between phrase-level and review-level sentiment analysis. We investigate the inconsistency between the numerical star ratings and the sentiment orientation of textual user reviews. Although they have long been treated as identical, which serves as a basic assumption in previous work, we find that this assumption is not necessarily true. We further propose to leverage the results of review-level sentiment classification to boost the performance of phrase-level polarity labelling using a novel constrained convex optimization framework. Besides, the framework is capable of integrating various kinds of information sources and heuristics, while giving the global optimal solution due to its convexity. Experimental results on both English and Chinese reviews show that our framework achieves high labelling precisions of up to 89 , which is a significant improvement from current approaches.
Review- and sentence-level sentiment analysis attempt to label a review or sentence as one of some predefined sentiment polarities, which are, typically, positive, negative and sometimes neutral @cite_36 . This task is referred to as Sentiment Classification, which has drawn much attention from the research community, and both supervised @cite_6 @cite_8 @cite_43 @cite_21 @cite_39 @cite_15 , unsupervised @cite_30 @cite_32 @cite_0 @cite_3 @cite_16 @cite_33 or semi-supervised @cite_44 @cite_41 @cite_37 @cite_35 methods have been investigated.
@cite_30 @cite_35 @cite_37 @cite_33 @cite_8 @cite_36 @cite_41 @cite_21 @cite_32 @cite_6 @cite_39 @cite_0 @cite_43 @cite_3 @cite_44 @cite_15 @cite_16
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Current approaches for contextual sentiment lexicon construction in phrase-level sentiment analysis assume that the numerical star rating of a review represents the overall sentiment orientation of the review text. Although widely adopted, we find through user rating analysis that this is not necessarily true. In this paper, we attempt to bridge the gap between phrase-level and review document-level sentiment analysis by leveraging the results given by review-level sentiment classification to boost phrase-level sentiment polarity labeling in contextual sentiment lexicon construction tasks, using a novel constrained convex optimization framework. Experimental results on both English and Chinese reviews show that our framework improves the precision of sentiment polarity labeling by up to 5.6 , which is a significant improvement from current approaches. This paper presents a parse-and-paraphrase paradigm to assess the degrees of sentiment for product reviews. Sentiment identification has been well studied; however, most previous work provides binary polarities only (positive and negative), and the polarity of sentiment is simply reversed when a negation is detected. The extraction of lexical features such as unigram bigram also complicates the sentiment classification task, as linguistic structure such as implicit long-distance dependency is often disregarded. In this paper, we propose an approach to extracting adverb-adjective-noun phrases based on clause structure obtained by parsing sentences into a hierarchical representation. We also propose a robust general solution for modeling the contribution of adverbials and negation to the score for degree of sentiment. In an application involving extracting aspect-based pros and cons from restaurant reviews, we obtained a 45 relative improvement in recall through the use of parsing methods, while also improving precision. Sentiment analysis is the task of determining the attitude (positive or negative) of documents. While the polarity of words in the documents is informative for this task, polarity of some words cannot be determined without domain knowledge. Detecting word polarity thus poses a challenge for multiple-domain sentiment analysis. Previous approaches tackle this problem with transfer learning techniques, but they cannot handle multiple source domains and multiple target domains. This paper proposes a novel Bayesian probabilistic model to handle multiple source and multiple target domains. In this model, each word is associated with three factors: Domain label, domain dependence independence and word polarity. We derive an efficient algorithm using Gibbs sampling for inferring the parameters of the model, from both labeled and unlabeled texts. Using real data, we demonstrate the effectiveness of our model in a document polarity classification task compared with a method not considering the differences between domains. Moreover our method can also tell whether each word's polarity is domain-dependent or domain-independent. This feature allows us to construct a word polarity dictionary for each domain. Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classification models that can be configured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83 accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 and 68 respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated.