Fine-Grained Implicit Sentiment in Financial News: Uncovering Hidden Bulls and Bears

Abstract

The field of sentiment analysis is currently dominated by the detection of attitudes in lexically explicit texts such as user reviews and social media posts. In objective text genres such as economic news, indirect expressions of sentiment are common. Here, a positive or negative attitude toward an entity must be inferred from connotational or real-world knowledge. To capture all expressions of subjectivity, a need exists for fine-grained resources and approaches for implicit sentiment analysis. We present the SENTiVENT corpus of English business news that contains token-level annotations for target spans, polar spans, and implicit polarity (positive, negative, or neutral investor sentiment, respectively). We both directly annotate polar expressions and induce them from existing schema-based event annotations to obtain event-implied implicit sentiment tuples. This results in a large dataset of 12,400 sentiment–target tuples in 288 fully annotated articles. We validate the created resource with an inter-annotator agreement study and a series of coarse- to fine-grained supervised deep-representation-learning experiments. Agreement scores show that our annotations are of substantial quality. The coarse-grained experiments involve classifying the positive, negative, and neutral polarity of known polar expressions and, in clause-based experiments, the detection of positive, negative, neutral, and no-polarity clauses. The gold coarse-grained experiments obtain decent performance (76% accuracy and 63% macro-F1) and clause-based detection shows decreased performance (65% accuracy and 57% macro-F1) with the confusion of neutral and no-polarity. The coarse-grained results demonstrate the feasibility of implicit polarity classification as operationalized in our dataset. In the fine-grained experiments, we apply the grid tagging scheme unified model for <polar span, target span, polarity> triplet extraction, which obtains state-of-the-art performance on explicit sentiment in user reviews. We observe a drop in performance on our implicit sentiment corpus compared to the explicit benchmark (22% vs. 76% F1). We find that the current models for explicit sentiment are not directly portable to our implicit task: the larger lexical variety within implicit opinion expressions causes lexical data scarcity. We identify common errors and discuss several recommendations for implicit fine-grained sentiment analysis. Data and source code are available.

A dynamic graph structural framework for implicit sentiment identification based on complementary semantic and structural information

“…Sentiment analysis methods can be categorized into: corpus-based methods and deep learning-based methods. In terms of implicit corpus-based approaches, Jacobs et al 9 constructed an implicit SENTiVENT corpus of English business news and conducted coarse-grained implicit sentiment analysis experiments on this dataset, and found that the existing explicit sentiment models could not be directly applied in implicit sentiment analysis.…”

A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting

“…It is highlighted that the user's bearish tendencies implied a higher market volatility and reflect a possible higher market return. Moreover, there is a SENTiVENT corpus presented by [65] consisting of the token-level annotations for target spans, polar spans and polarity, and training the model based on corresponding annotations for stock movement prediction.…”

A Novel Cascade Model for End-to-End Aspect-Based Social Comment Sentiment Analysis

“…In order to make these separate works in practical use for the complete ABSA task, one typical way is to pipeline these two sub-tasks together to a relatively integrated method [6,7,9,[16][17][18][19]39].…”

“…As the dominant line of research in fine-grained opinion mining, aspect-based sentiment analysis (ABSA) aims to identify sentiment of target entities and their aspects. Specifically, given a target entity of interest, ABSA methods can extract its properties and identify the sentiment expressed about those properties [6]. From a technological point of view, the methodologies can be divided into two sub-tasks, namely opinion target extraction and target sentiment identification [4,5], which corresponds to the above-mentioned interested target entity properties extraction and expresses sentiment identification.…”