JN - NLP at SIGHAN -2024 dim ABSA Task: Extraction of Sentiment Intensity Quadruples Based on Paraphrase Generation (2024)

Abstract: Aspect-based sentiment analysis(ABSA) is a fine-grained sentiment analysis task, which aims to extract multiple specific sentiment elements from text. The current aspect-based sentiment analysis task mainly involves four basic elements: aspect term, aspect category, opinion term, and sentiment polarity. With the development of ABSA, methods for predicting the four sentiment elements are gradually increasing. However, traditional ABSA usually only distinguishes between “positive”, “negative”, or “neutral”attitudes when judging sentiment polarity, and this simplified classification method makes it difficult to highlight the sentimentintensity of different reviews. SIGHAN 2024 provides a more challenging evaluation task, the Chinese dimensional ABSA shared task (dimABSA), which replaces the traditional sentiment polarity judgment task with a dataset in a multidimensional space with continuous sentiment intensity scores, including valence and arousal. Continuous sentiment intensity scores can obtain more detailed emotional information. In this task, we propose a new paraphrase generation paradigm that uses generative questioning in an end-to-end manner to predict sentiment intensity quadruples, which can fully utilize semantic information and reduce propagation errors in the pipeline approach.

## introduction traditional aspect-based sentiment analysis (absa) can extract four specific emotional elements from the text: 1) aspect term, which is a specific aspect in the sentence, generally a word or phrase expressed in the text, and may not exist; 2) aspect category, the category involved by the aspect term, usually a predefined set of categories; 3) opinion term, the expression of a specific emotional view on an aspect; 4) sentiment polarity, the emotional tendency towards a certain aspect. for example, for the review "the pizza at this restaurant is delicious, but the service is terrible.", the absa task can extract two emotional quadruples: (pizza, food type, delicious, positive) and (service, service attitude, terrible, negative). the chinese dimensional absa shared task (dimabsa) @xcite dataset is a collection of comments extracted by organizers from online catering industry social media platforms. after removing html tags and multimedia tags, the text was split into multiple sentences. a selection of these sentences was then manually annotated with aspect term, aspect category, opinion term, and sentiment intensity. for sentiment intensity, the organizers used the valence and arousal provided by the "chinese emobank" @xcite to represent emotional states as continuous numerical values in a multidimensional space. using valence-arousal as sentiment intensity, this method provides more detailed emotional information. the "chinese emobank" is a manually annotated chinese emotional dictionary. in it, valence describes the positivity or negativity of emotions, ranging continuously from negative (such as sadness, anger) to positive (such as happiness, excitement). valence is often seen as the "pleasantness" of an emotional experience and is a standard for assessing the quality of emotional experiences. arousal, on the other hand, refers to the level of excitement of an emotion. it reflects the intensity of an individual's physiological and psychological response to an emotional stimulus. arousal is also a continuous range, from very low (such as boredom, tiredness) to very high (such as surprise, panic). in the "chinese emobank", both valence and arousal are measured on a scale from 1 to 9. in the sentiment intensity of the dimabsa dataset, valence and arousal are separated by a '#'. the dimabsa task provides three subtasks: subtask 1: for a given sentence and its aspect term, predict the sentiment intensity of the aspect term in the comment; subtask 2: for a given sentence, extract the aspect term and opinion term from the comment and predict the sentiment intensity; subtask 3: for a given sentence, extract the aspect term, aspect category, and opinion term from the comment and predict the sentiment intensity. in this task, we have implemented subtask 3, where the system could extract all sentiment quadruples (aspect, category, opinion, intensity). for example, for the comment "this bowl of ramen is super invincibly thunderously bad.", the final extraction of the sentiment intensity quadruple would be: (ramen, food#quality, super invincibly thunderously bad, 2.00#7.88). this work processes the dataset provided by the dimabsa task and proposes a new paraphrase generation paradigm. it replaces the traditional sentiment polarity judgment task in absa with the judgment of sentiment intensity: valence#arousal, using the t5 pre-trained model @xcite ) that unifies natural language processing tasks into text-to-text tasks. through fine-tuning training, it accomplishes the task of extracting sentiment intensity quadruples. experiments demonstrate that our newly proposed paraphrase generation paradigm achieves good performance in predicting sentiment intensity quadruples. our contributions are summarized as follows: 1) we propose transforming the dimabsa task into a paraphrase generation problem and introduce a new paraphrase generation paradigm, allowing us to fully utilize semantic information while predicting sentiment intensity quadruple in one shot; 2) our model has been experimentally validated to perform excellently in extracting aspect term, aspect category, and opinion term. ## related work with the emergence of pre-trained language models like bert, research in absa has made significant progress. @xcite proposed a method based on pre-trained language models, utilizing models like bert to extract the subjects and aspects from online comments, and employing multi-task learning to determine their sentiment polarity. this was the first method for absa based on pre-trained language models and is representative of methods based on transfer learning. @xcite were the first to apply bert to end-to-end absa tasks, achieving the best results at the time with a simple linear classifier. subse-quently, composite absa tasks began to develop. researchers proposed various end-to-end models for extracting multiple sentiment elements, capable of handling multiple subtasks in sentiment analysis tasks, such as aspect term extraction (ate) and aspect sentiment classification (asc). this integrated approach reduced error propagation and improved overall performance. liu et al. (2021) adopted the seq2seq modeling paradigm to extract aspect category and sentiment polarity, based on pre-trained generative models, using natural language sentences to represent the desired output for aspect category sentiment analysis (acsa) tasks. peng et al. (2020) proposed a two-stage pipeline method for extracting aspect term, opinion term, and sentiment polarity to address the aspect sentiment triplet extraction (aste) task; wan et al. (2020) introduced the target-aspect-sentiment joint detection task for aspect-based sentiment analysis (tasd), aimed at simultaneously predicting aspect category, aspect term, and sentiment polarity. the aspect sentiment quad prediction (asqp) task aims to extract the four sentiment elements of a specific sentence at once, revealing a more comprehensive and complete aspect-level sentiment structure. zhang et al.(2021) proposed a paraphrase generation paradigm to solve the asqp task in english. this approach generates natural language sentences from sentiment quadruples using pre-established templates, making the generated natural language sequence the target sequence, which forms a mapping relationship with the original review sentence. zhang et al. transformed the original quad element prediction task into a text generation problem, which was then solved using a sequence-to-sequence (seq2seq) approach. compared to the pipeline method, the seq2seq approach can reduce the cumulative propagation error caused by accuracy errors at each step in the pipeline, and since the subtasks of asqp are usually expressed as token-level or sequence-level classification problems, the seq2seq approach can make full use of semantic information. ## conclusions the goal of dimabsa subtask 3 is to extract sentiment intensity quadruples from online review sentences in the catering industry, including aspect term, aspect category, opinion term, and the valence-arousal representing sentiment intensity. this work proposes a new paraphrase generation paradigm, utilizing the dataset provided by the dimabsa task, and ultimately achieves a model based on the t5 pre-trained model fine-tuned for training. this model uses the new paraphrase generation paradigm to facilitate seq2seq learning, transforming sentiment intensity quadruples into natural language target sentences, forming a mapping relationship with the review sentences. the model can generate an output sentence according to the paraphrasing rules for the input sentence, and the sentiment intensity quadruples can be obtained by processing the output sentence. by comparing the performance of this work's model with the test model in extracting triples of aspect term, aspect category, and opinion term, it is evident that this work's model performs better.