TEASER : Towards Efficient Aspect-based SE ntiment Analysis and Recognition (2021)

Abstract: Sentiment analysis aims to detect the overall sentiment, i.e., the polarity of a sentence, paragraph, or text span, without considering the entities mentioned and their aspects. Aspect-based sentiment analysis aims to extract the aspects of the given target entities and their respective sentiments. Prior works formulate this as a sequence tagging problem or solve this task using a span-based extract-then-classify framework where first all the opinion targets are extracted from the sentence, and then with the help of span representations, the targets are classified as positive, negative, or neutral. The sequence tagging problem suffers from issues like sentiment inconsistency and colossal search space. Whereas, Span-based extract-then-classify framework suffers from issues such as half-word coverage and overlapping spans. To overcome this, we propose a similar span-based extract-then-classify framework with a novel and improved heuristic. Experiments on the three benchmark datasets (Restaurant14, Laptop14, Restaurant15) show our model consistently outperforms the current state-of-the-art. Moreover, we also present a novel supervised movie reviews dataset (Movie20) and a pseudo-labeled movie reviews dataset (moviesLarge) made explicitly for this task and report the results on the novel Movie20 dataset as well.

## introduction online reviews and tweets play an essential role in consumer decision making. hence, it becomes crucial to efficiently and effectively extract user opinions from an unstructured text (user review, tweet). aspect-based sentiment analysis is a fundamental task in mining opinions and sentiment analysis @xcite . this task requires detecting the aspects of the target entities mentioned (aspect extraction) and detecting the sentiment attached, i.e., the polarity of the target entity (sentiment classification). hence, it is more challenging than the traditional sentence-level sentiment analysis @xcite , where we predict the text's polarity as a whole. as shown in most of the previous works treat this aspect-based sentiment analysis (absa) task as a combination of two different subtasks, namely, aspect extraction (ae) and sentiment classification (sc). researchers often treat these two subtasks as independent and work on both of them individually, or some even tried to combine the two and propose a joint model that can extract the targets and predict the polarity. much work has been done in the field of ae. @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite formulate ae as a sequence tagging problem. in sequence tagging, the task is to mark each word with a set of tags (e.g. b, i, o). the second subtask, sc, i.e., marking each extracted target term as positive, negative, or neutral, has also been widely studied @xcite @xcite @xcite . the main issue with most of these sentiment classifiers is that, they assume the target is already given. @xcite and @xcite tried to combine the two subtasks and solve the task in a more integrated way by jointly extracting targets and predicting their sentiments. the main idea here is either jointly marking the words with a set of tags for the task of ae (e.g., b, i, o) and also marking them as positive, negative, neutral for sc or use a more collapsed version of marking (e.g., ## conclusion in this work, we proposed teaser, an extractthen-classify network for aspect-based sentiment analysis with pre-trained bert-large as the main network. we also presented an aspect extractor with a novel heuristic, which helps extract all the targets of a given sentence. experiments show that our method consistently outperforms the current state-of-the-art in the task of ae and also in absa. we also presented two datasets, movie20, a supervised dataset of 1162 sentences with a cohen kappa score of 0.8326, and movieslarge, a pseudo-labeled dataset of around 14373 sentences. lastly, using semi-supervised learning, we benchmarked teaser on the movie20 dataset. we analyzed the model to reason where the model failed to perform, and according to the findings, aspects preceded with rare adjectives and aspects with an absence of a clear adjective were the primary reasons for the failure.