Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification (2020)

Abstract: Neural Document-level Multi-aspect Sentiment Classification (DMSC) usually requires a lot of manual aspect-level sentiment annotations, which is time-consuming and laborious. As document-level sentiment labeled data are widely available from online service, it is valuable to perform DMSC with such free document-level annotations. To this end, we propose a novel Diversified Multiple Instance Learning Network (D-MILN), which is able to achieve aspect-level sentiment classification with only document-level weak supervision. Specifically, we connect aspect-level and document-level sentiment by formulating this problem as multiple instance learning, providing a way to learn aspect-level classifier from the back propagation of document-level supervision. Two diversified regularizations are further introduced in order to avoid the overfitting on document-level signals during training. Diversified textual regularization encourages the classifier to select aspect-relevant snippets, and diversified sentimental regularization prevents the aspect-level sentiments from being overly consistent with document-level sentiment. Experimental results on TripAdvisor and BeerAdvocate datasets show that D-MILN remarkably outperforms recent weakly-supervised baselines, and is also comparable to the supervised method.

## introduction document-level multi-aspect sentiment classification (dmsc) is a fine-grained sentiment analysis task, aiming to predict the sentiments of aspects in a document consisting of several sentences. in previous studies, neural models have shown to be effective for improving dmsc with the help of large amounts of aspect-level annotations @xcite @xcite a great location to stay at. the room was ordinary the bathroom looked like an after thought, the shower was extremely small. also the front desk clerks provided minimum service. besides, the price is very expensive. ## methodology we first briefly introduce the problem we work on. given a review, our task is to predict the sentiments of aspects in the review. formally, we denote the review document as d which contains i words {w 1 , w 2 , • • • , w i }, the sentiment label for the document as l d , and the set of j aspects mentioned in the document as {a 1 , a 2 , • • • , a j }. same as @xcite , each aspect a j is represented by k aspect-related keywords, {a j 1 , a j 2 , • • • , a j k }, in order to cover most of the semantic meanings of the aspect 1 . ## conclusion in this paper, we propose a diversified multiple instance learning network to achieve dmsc with only document-level supervision. we formulate this problem as multiple instance learning, so as to model the relation between aspect-level sentiment and document-level sentiment. in order to guarantee the proper transfer from document-level supervision to aspect-level prediction, we further propose diversified textual regularization and diversified sentimental regularization. through experiments on two benchmark datasets, we verify that our d-miln can properly capture the interaction between aspect-level and document-level, and achieve new sota on weakly supervised dmsc. detailed comparisons also show the necessity and effectiveness of our diversified regularizations. in the future, we plan to further improve d-miln with aspect-level annotations and find appropriate way to combine d-miln with pre-training methods @xcite . tripadvisor keywords value value, price, quality, worth, cost, expensive, $, reasonable, pricey, cheaper room room, suite, view, bed, suite, bathroom, shower, desk, well-equipped, balcony location location, traffic, minute, restaurant, locations, mclintock, chandler, located, convenient, mall cleanliness clean, dirty, maintain, smell, spotless, tidy, roomy, neat, comfortable, decorated check in/front desk stuff, check, help, reservation, check-in, check-outs, flights, appointment, doctor, tech service service, food, breakfast, buffet, staff, customer, exceptional, ambiance, friendly, experience business business, center, computer, internet, businesses, biz, collier, printer, desktop, wifi beeradvocate keywords feel feel, dryness, softness, sharpness, touch look look, appearance, color, dark, transparency smell smell, aroma, nose, smelly, snif taste taste, flavor, sugary, earthy, bitter