What 20 domains are available for selection of source domain?
Amazon Instant Video,  Automotive, Baby,  Beauty, Books,  Clothing Accessories,  Electronics,  Health, Home, Kitchen, Movies, Music,  Office Products, Patio, Pet Supplies, Shoes,  Software,  Sports Outdoors,  Tools Home Improvement, Toys Games, Video Games.
Cross-domain sentiment analysis (CDSA) helps to address the problem of data scarcity in scenarios where labelled data for a domain (known as the target domain) is unavailable or insufficient. However, the decision to choose a domain (known as the source domain) to leverage from is, at best, intuitive. In this paper, we investigate text similarity metrics to facilitate source domain selection for CDSA. We report results on 20 domains (all possible pairs) using 11 similarity metrics. Specifically, we compare CDSA performance with these metrics for different domain-pairs to enable the selection of a suitable source domain, given a target domain. These metrics include two novel metrics for evaluating domain adaptability to help source domain selection of labelled data and utilize word and sentence-based embeddings as metrics for unlabelled data. The goal of our experiments is a recommendation chart that gives the K best source domains for CDSA for a given target domain. We show that the best K source domains returned by our similarity metrics have a precision of over 50%, for varying values of K.
Sentiment analysis (SA) deals with automatic detection of opinion orientation in text BIBREF0. Domain-specificity of sentiment words, and, as a result, sentiment analysis is also a well-known challenge. A popular example being `unpredictable' that is positive for a book review (as in `The plot of the book is unpredictable') but negative for an automobile review (as in `The steering of the car is unpredictable'). Therefore, a classifier that has been trained on book reviews may not perform as well for automobile reviews BIBREF1. However, sufficient datasets may not be available for a domain in which an SA system is to be trained. This has resulted in research in cross-domain sentiment analysis (CDSA). CDSA refers to approaches where the training data is from a different domain (referred to as the `source domain') as compared to that of the test data (referred to as the `target domain'). ben2007analysis show that similarity between the source and target domains can be used as indicators for domain adaptation, in general. In this paper, we validate the idea for CDSA. We use similarity metrics as a basis for source domain selection. We implement an LSTM-based sentiment classifier and evaluate its performance for CDSA for a dataset of reviews from twenty domains. We then compare it with similarity metrics to understand which metrics are useful. The resultant deliverable is a recommendation chart of source domains for cross-domain sentiment analysis. The key contributions of this work are: We compare eleven similarity metrics (four that use labelled data for the target domain, seven that do not use labelled data for the target domain) with the CDSA performance of 20 domains. Out of these eleven metrics, we introduce two new metrics. Based on CDSA results, we create a recommendation chart that prescribes domains that are the best as the source or target domain, for each of the domains. In general, we show which similarity metrics are crucial indicators of the benefit to a target domain, in terms of source domain selection for CDSA. With rising business applications of sentiment analysis, the convenience of cross-domain adaptation of sentiment classifiers is an attractive proposition. We hope that our recommendation chart will be a useful resource for the rapid development of sentiment classifiers for a domain of which a dataset may not be available. Our approach is based on the hypothesis that if source and target domains are similar, their CDSA accuracy should also be higher given all other conditions (such as data size) are the same. The rest of the paper is organized as follows. We describe related work in Section SECREF2 We then introduce our sentiment classifier in Section SECREF3 and the similarity metrics in Section SECREF4 The results are presented in Section SECREF5 followed by a discussion in Section SECREF6 Finally, we conclude the paper in Section SECREF7