Making the Best Use of Review Summary for Sentiment Analysis (2020)

Abstract: Sentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may give additional benefit for review sentiment analysis. In this paper, we conduct a study to exploit methods for better use of summary information. We start by finding out that the sentimental signal distribution of a review and that of its corresponding summary are in fact complementary to each other. We thus explore various architectures to better guide the interactions between the two and propose a hierarchically-refined review-centric attention model. Empirical results show that our review-centric model can make better use of user-written summaries for review sentiment analysis, and is also more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.

## introduction sentiment analysis @xcite @xcite is a fundamental task in natural language processing, which predicts the subjectivity and polarity of a given text. in practice, automatically extracting sentiment from user reviews has wide applications such as e-commerce and movie reviews @xcite @xcite . in many review websites such as amazon and imdb, the user is allowed to give a summary in addition to the review, where summaries can contain more general information about the review. figure 1 gives a few such examples. it is thus an interesting research question on how to make use of both review and summary information for better sentiment classification under such a scenario. as shown in figure 1 , user-written summaries can be a brief version of reviews that is highly indicative of the user sentiment. thus summaries can be used as additional training signals for sentiment classification. to this end, recent work @xcite exploits multi-task learning. the model structure can be illustrated by figure 2a . in particular, given a review input, a model is trained to simultaneously predict the sentiment and the summary. as a result, both summary and review features are integrated into the review encoder through back-propagation training. while the above methods are highly effective, we find that the correlation between reviews and summaries can be subtle. as shown in we conduct our investigation by treating both the review and the summary as inputs. in particular, we first compare the performance of sentiment classification using review only and using summary only, finding that the two sources of information are in fact complementary to each other. second, as shown in figure 2b , we investigate a simple method to integrate review and summary information by concatenating separately-learned representations. this method turns out to outperform models using review or summary inputs only. one limitation of this method, however, is that it does not capture the interaction between the review and summary information as thoroughly as the method shown in figure 2a , in which the representation of a review contains summary knowledge also. to address this issue, we further investigate a joint encoder structure between the review and the summary, which is demonstrated in figure 2c . to this end, an intuitive method is co-attention @xcite , which iteratively updates the representation of review and summary by consulting each other, as shown in figure 3a . however, we find empirically that the review itself is relatively more indicative of the user sentiment compared to the summary. given this observation, we further build a review-centric joint encoder. different from the co-attention encoder, the review-centric model iteratively updates a review representation given a summary representation, but not vice versa. we evaluate our proposed models on the snap (stanford network analysis project) amazon review datasets @xcite , which contain reviews and ratings together with user-written summaries if they exist. in scenarios where there is no user-written summary for a review, we use a pointer-generator network summarization model @xcite to generate abstractive summaries. empirical results show that our review-centric model outperforms a range of baselines, including multitask, separate encoder and joint encoder methods. in addition, our review-centric model achieves new state-of-the-art results, giving 2.1% (with system-generated summary) and 4.8% (with gold summary) absolute improvements compared to the previous best method on the snap benchmark. to our knowl-rating: 5 stars review: i can color right along with my grandchildren, without feeling intellectually compromised at the project. this book is so amazing that i have used some of the designs for stained glass windows. i highly recommend this for anyone who does not want to grow out of a favorite past time. ## related work our work is partly related to previous work building well-designed matching models to capture the relationship between two texts. in reading comprehension, a matching model is required to capture the similarity among a given passage, a question and a candidate answer. @xcite adopted two grus to encode the passage and question, respectively, and a bilinear function to compute the similarity on each passage token. @xcite make use of co-attention, which shares one single attention matrix between the passage and the question, calculating both passage-to-question and question-to-passage attention scores. for retrieval-based dialogue systems, models are required to calculate the matching score between a candidate response and a conversation context. in particular, sequential matching network @xcite captures matching information by constructing word-to-word and a sequence-tosequence similarity matrices. deep attention matching network @xcite adopts self-attention and cross-attention modules to harvest intra-sentence relationship and inter-sentence relationship, respectively. to capture potential long-term label dependency in sequence labeling, @xcite use attention over label embeddings to refine the marginal label probabilities by calculating the similarity between a word sequence and a set of label embeddings. compared with these methods, which model matching between two pieces of text, our work is different in that we consider how to effectively make use of the complementary property between a review and a corresponding summary for better review sentiment analysis. our work is related to previous work on sentiment analysis @xcite @xcite , taking a whole review as input @xcite @xcite rather than specific aspects @xcite . different from previous work, we additionally consider user-generated or automatically-generated summaries as input. our work is related to existing work on joint summarization and sentiment classification. @xcite propose a multi-view attention model for joint summarization and sentiment classification. @xcite improve the model of @xcite by using additional attention on the generated text. different from their work, we are not directly concerned about making better summaries. instead, we make a broader discussion on how to make the best use of both review and summary for sentiment classification. our work is also related to rationalizing sentiment predictions. @xcite regard gold-standard rationales as additional input and used rationale-level attention for text classification. @xcite propose an unsupervised latent model that selects a rationale and subsequently uses it for sentiment analysis. our work is similar in that we can visualize the most salient words in sentiment classification. different from the existing methods, our rationalization is based on the interaction between a review and a summary, with the latter guiding the visualization. 3 task ## method all the methods that we investigate are based on a bilstm @xcite structure. we first discuss the basic bilstm to encode text (sec 4.1), and then discuss two types of structures that use bilstm for separate encoding(sec 4.2.1) and symmetric joint encoding (sec 4.2.2), respectively. finally, we discuss review-centric joint encoding (sec 4.3) of the review and the summary. ## experiments the snap amazon review dataset foot_0 (mcauley and leskovec, 2013) consists of around 34 million amazon reviews in different domains, such as books, games, sports and movies. each review mainly consists of a product id, a piece of user information, a plain text review, a user-written summary and an overall sentiment rating which ranges from 1 to 5. for fair comparison with previous work, we adopt the same domains and partition used by @xcite and @xcite , which includes three datasets (toys & games, sports & outdoors and movies & tv). the statistics of our adopted dataset are shown in ## conclusion we empirically analyzed the correlation between reviews and summaries for customer review sentiment analysis, found that they are complementary to each other for carrying user sentiment. we investigated a range of joint encoder models for better modeling the interactions between reviews and summaries and proposed a novel review-centric method, which hold different inductive bias to capture the complementary correlation. empirical results verified the effectiveness of joint encoding for review and summary among strong baselines and existing work, showing that a review-centric model outperforms a symmetric co-attention model.