Implicit Sentiment Analysis with Event-centered Text Representation (2021)

Abstract: Implicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive research topic in recent years. In this paper, we focus on event-centric implicit sentiment analysis that utilizes the sentiment-aware event contained in a sentence to infer its sentiment polarity. Most existing methods in implicit sentiment analysis simply view noun phrases or entities in text as events or indirectly model events with sophisticated models. Since events often trigger sentiments in sentences, we argue that this task would benefit from explicit modeling of events and event representation learning. To this end, we represent an event as the combination of its event type and the event triplet <subject, predicate, object>. Based on such event representation, we further propose a novel model with hierarchical tensor-based composition mechanism to detect sentiment in text. In addition, we present a dataset for event-centric implicit sentiment analysis where each sentence is labeled with the event representation described above. Experimental results on our constructed dataset and an existing benchmark dataset show the effectiveness of the proposed approach.

## introduction sentiment analysis aims at automatically detecting the sentiment of given text. explicit sentiment analysis methods detect the sentiment mainly based on the occurrence of sentiment-related words and have been extensively explored @xcite @xcite . however, sentiment could also be implicitly expressed. for example, the sentence 'i won the first place in the speech contest' does not contain any sentiment words, but the event of 'winning the first place' reflects the positive sentiment. implicit sentiment analysis was thus proposed to detect sentiment in the absence of sentiment words @xcite . compared with explicit sentiment analysis, implicit sentiment analysis is more challenging as there is a lack of explicit cues for inferring the sentiment polarity. research on implicit sentiment analysis can be broadly classified into two categories: metaphorbased and event-centric. metaphor/rhetoric-based implicit sentiment analysis methods typically detect sentiment based on a metaphoric sentiment dictionary and some manually designed rules @xcite , while event-centric approaches assume sentiment is triggered by events described in sentences. examples include an early approach which regarded noun phrases or entities in sentences as events @xcite , and more recent approaches which indirectly model events by capturing contextual information using graph convolutional networks @xcite or attention mechanism @xcite . we argue that in the former, the event representation is oversimplified and consequently event-related knowledge would be largely lost; while in the latter, events are not directly modeled which makes it less effective in detecting sentiments triggered by events. to overcome the limitations of existing eventcentric approaches, we propose to construct a corpus in which event triplets in the form @xmath0, predicate, @xmath1. in addition, each sentence is also assigned with a sentiment class label. an example sentence and its annotation are shown below: 'you abandon me for a week to go off on holiday with daddy, come back and barely 2 days later you go off out with him again.' event triplet: <you, abandon, @xmath2: abandonment sentiment: negative the event triplet and the event type are combined as the final representation of an event. based on such event-centered text representation, we further propose a method for implicit sentiment analysis built on hierarchical tensor-based compositions, which effectively employs tensor composition @xcite to encode the interaction between the subject, the predicate, the object and the sentence. moreover, we adopt a multi-task learning framework to perform event type classification and sentiment classification simultaneously. our experimental results show that event type classification benefits sentiment classification. in summary, our contributions are as follows: ## related work implicit sentiment analysis @xcite first classifies sentiment analysis into explicit and implicit sentiment analysis. generally speaking, implicit sentiment analysis can be further classified into metaphor-based and event-centric approaches. in metaphor-based approaches, sentences containing keywords found in a metaphor dictionary are considered implicitly expressing positive or negative emotional tendencies @xcite . in event-centric approaches, events mentioned in text may imply positive or negative sentiments. @xcite presented an approach for detecting event-triggered sentiment based on commonsense knowledge, emotinet, a knowledge base of concepts with associated affective value. @xcite used grammatical structures to mine language features related to implicit sentiments, and used similarity calculations to improve the performance of sentiment classification. zuo et al. (2020) proposed a context-specific heterogeneous graph convolutional network to address the problem of the absence of sentiment words. event-related sentiment analysis in recent years, researchers also pay attention to the importance of event information in sentiment analysis. deng and wiebe (2015) encoded a set of sentiment inference rules in a probabilistic soft logic framework for entity/event-level sentiment analysis tasks. hofmann et al. (2020) encoded properties of events as latent variables following theories of cognitive appraisal of events to improve emotion classification performance. gaonkar et al. (2020) tracked label-label correlations through label embeddings in sentiment classification tasks to maintain the consistency of emotion caused by the same type of event. ding and riloff (2016) first defined affective events as @xmath3, verb, objective>, and created a dataset containing affective events with sentiment polarity labels. ding and riloff (2018b) expanded affective events @xmath4, predicate, objective, prepositional phrase>, and introduced a weakly supervised method for affective events classification. ding and riloff (2018a) categorized affective events into physiological, health, leisure, social, financial, cognition, and freedom, based upon human need related to people's motivations and desires. yang et al. (2019) regarded the topic distribution of a sentence as an implicit event, and proposed an event-driven attention with topics of sentences for emotion ranking. zhuang et al. (2020) presented a discourse-enhanced selftraining method that iteratively improved the classifier with unlabeled affective events. ## dataset: evesa since the existing sentiment analysis datasets only contain text and sentiment polarity labels, but no event-related annotations, we construct an eventcentered dataset for implicit sentiment analysis. we first identify event types from framenet @xcite and then crawl tweets which contain the triggering words of the corresponding event types. ## conclusion in this paper, we propose a novel approach for factbased implicit sentiment analysis, mainly for the situation where a sentence contains no sentiment words but only event descriptions. our model employs event information, including the event triplets and event types as features, and detects sentiment based on event representations learned with hierarchical tensor-based compositions. moreover, we present a dataset with event annotations for implicit sentiment analysis. experimental analysis demonstrates that both event triplets and event type benefit implicit sentiment classification. our current approach assumes that events have already been extracted from text. future research will explore automated event extraction in order to perform endto-end implicit sentiment analysis.