A survey of transfer learning <s> Relational-based transfer learning <s> Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques. <s> BIB001 </s> A survey of transfer learning <s> Relational-based transfer learning <s> Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model. <s> BIB002 </s> A survey of transfer learning <s> Relational-based transfer learning <s> In most sentiment analysis applications, the sentiment lexicon plays a key role. However, it is hard, if not impossible, to collect and maintain a universal sentiment lexicon for all application domains because different words may be used in different domains. The main existing technique extracts such sentiment words from a large domain corpus based on different conjunctions and the idea of sentiment coherency in a sentence. In this paper, we propose a novel propagation approach that exploits the relations between sentiment words and topics or product features that the sentiment words modify, and also sentiment words and product features themselves to extract new sentiment words. As the method propagates information through both sentiment words and features, we call it double propagation. The extraction rules are designed based on relations described in dependency trees. A new method is also proposed to assign polarities to newly discovered sentiment words in a domain. Experimental results show that our approach is able to extract a large number of new sentiment words. The polarity assignment method is also effective. <s> BIB003 </s> A survey of transfer learning <s> Relational-based transfer learning <s> In this paper, we focus on the opinion target extraction as part of the opinion mining task. We model the problem as an information extraction task, which we address based on Conditional Random Fields (CRF). As a baseline we employ the supervised algorithm by Zhuang et al. (2006), which represents the state-of-the-art on the employed data. We evaluate the algorithms comprehensively on datasets from four different domains annotated with individual opinion target instances on a sentence level. Furthermore, we investigate the performance of our CRF-based approach and the baseline in a single- and cross-domain opinion target extraction setting. Our CRF-based approach improves the performance by 0.077, 0.126, 0.071 and 0.178 regarding F-Measure in the single-domain extraction in the four domains. In the cross-domain setting our approach improves the performance by 0.409, 0.242, 0.294 and 0.343 regarding F-Measure over the baseline. <s> BIB004 </s> A survey of transfer learning <s> Relational-based transfer learning <s> Extracting sentiment and topic lexicons is important for opinion mining. Previous works have showed that supervised learning methods are superior for this task. However, the performance of supervised methods highly relies on manually labeled training data. In this paper, we propose a domain adaptation framework for sentiment- and topic- lexicon co-extraction in a domain of interest where we do not require any labeled data, but have lots of labeled data in another related domain. The framework is twofold. In the first step, we generate a few high-confidence sentiment and topic seeds in the target domain. In the second step, we propose a novel Relational Adaptive bootstraPping (RAP) algorithm to expand the seeds in the target domain by exploiting the labeled source domain data and the relationships between topic and sentiment words. Experimental results show that our domain adaptation framework can extract precise lexicons in the target domain without any annotation. <s> BIB005
The specific application addressed in the paper by Li BIB005 is to classify words from a text document into one of three classes (e.g. sentiments, topics, or neither). In this scenario, there exists a labeled text source domain on one particular subject matter and an unlabeled text target domain on a different subject matter. The main idea is that sentiment words remain constant between the source and target domains. By learning the grammatical and sentence structure patterns of the source, a relational pattern is found between the source and target domains, which is used to predict the topic words in the target. The sentiment words act as a common linkage or bridge between the source and target domains. A bipartite word graph is used to represent and score the sentence structure patterns. A bootstrapping algorithm is used to iteratively build a target classifier from the two domains. The bootstrapping process starts with defining seeds which are instances from the source that match frequent patterns in the target. A cross domain classifier is then trained with the seed information and extracted target information (there is no target information in the first iteration). The classifier is used to predict the target labels and the top confidence rated target instances are selected to reconstruct the bipartite word graph. The bipartite word graph is now used to select new target instances that are added to the seed list. This bootstrapping process continues over a selected number of iterations, and the cross domain classifier learned in the bootstrapping process is now available to predict target samples. This method is referred to as the Relational Adaptive bootstraPping (RAP) approach. The experiments tested the Li BIB005 approach against an upper bound method where a standard classifier is trained with a large amount of target data. Other transfer learning methods tested include an approach by Hu BIB001 , Qiu BIB003 , Jakob BIB004 , and Dai BIB002 . The application tested is word classification as described above where the F1 score is measured as the performance metric. The two domains tested are related to movie reviews and product reviews. The Li BIB005 method performed better than the other transfer learning methods, but fell short of the upper bound method as expected. In its current form, this algorithm is tightly coupled with its underlying text application, which makes it difficult to use for other non-text applications.