Implicit Sentiment Research Articles

Implicit Emotion Analysis Based on Improved Supervised Contrastive Learning

Implicit sentiment analysis (ISA) can be distinguished from traditional text sentiment analysis by the fact that it does not rely on emotional words as emotional clues, and the expression is usually vaguer. Identifying implicit emotions is more difficult, as it requires a deeper understanding of the context, even when emotional words are absent. Researchers have focused on context feature modeling and developing sophisticated feature extraction mechanisms instead of starting from the emotional perspective. Enhancing the difference in emotional features of text samples is an intuitive method to address this challenge. We proposed a supervised contrastive learning (SCL) method during training that enables the model to conduct contrastive learning based on emotion labels even while training on weak emotion features. SCL training can strengthen the average embedding distance between texts with different emotion labels and enhance implicit emotion discrimination. Moreover, research indicates that contextual information can improve implicit emotion classification ability. Therefore, we applied a straightforward context feature fusion method (bi-affine) over a more complicated context feature modeling approach. To evaluate the effectiveness of our proposed method, we conducted experiments on the SMP2019-ECISA (Chinese implicit sentiment analysis) dataset. The results show a 2.13% enhancement in the F1 value compared to the BERT baseline, proving the effectiveness of our methods.

Impact of COVID-19 on Market Sentiment and Abnormal Returns in India: A Comparative Analysis of Two Waves

This study examines the impact of COVID-19 on market sentiment and the stock market’s reaction at different investors’ time horizons in India. We applied wavelet coherence analysis and event study methodology during waves 1 and 2 of COVID-19 on NIFTY 50 firms. The results of this study report that market-related implicit sentiment proxies depicting the market’s bullish (bearish) sentiment negatively (positively) correlate with COVID-19 during the first wave of the pandemic in the short-term to medium-term (until 16 days). Using the event study method to compute abnormal stock returns during waves 1 and 2 of COVID-19, we found statistically significant negative abnormal returns during wave 1 only. Our findings extend the literature that examines the market reaction to COVID-19. The results generally hold for various robustness checks.

TS-GCN: Aspect-level sentiment classification model for consumer reviews

The goal of aspect-level sentiment classification (ASC) task is to obtain the sentiment polarity of aspect words in the text. Most existing methods ignore the implicit aspects, resulting in low classification accuracy. To improve the accuracy, this paper proposes a classification model for consumer reviews, abbreviated as TS-GCN (Truncated history attention and Selective transformation network-Graph Convolutional Networks). TS-GCN can classify sentiment from both explicit and implicit aspects. Firstly, we process the text by the BERT model and the BiLSTM model to obtain the text features. Secondly, the GCN model completes explicit sentiment classification by training text features. Due to the lack of implicit words, the GCN model cannot classify implicit sentiments. Finally, we predict implicit words based on the TS model, which makes up for the deficiency of the GCN model and completes the sentiment classification of implicit words. TS-GCN is proved on several datasets in the consumer reviews field. The results of experiments show that the TS-GCN can improve the accuracy and F1 of ASC.

Dynamic commonsense knowledge fused method for Chinese implicit sentiment analysis

Compared with explicit sentiment analysis that attracts considerable attention, implicit sentiment analysis is a more difficult task due to the lack of sentimental words. The abundant information in an external sentimental knowledge base can play a significant complementary and expansion role. In this paper, a sentimental commonsense knowledge graph embedded multi-polarity orthogonal attention model is proposed to learn the implication of the implicit sentiment. We analyzed the effectiveness of different knowledge relations in the ConceptNet knowledge base in detail, and proposed a matching and filtering method to distill useful knowledge tuples for implicit sentiment analysis automatically. By introducing the sentimental information in the knowledge base, the proposed model can extend the semantic of a sentence with an implicit sentiment. Then, a bi-directional long–short term memory model with multi-polarity orthogonal attention is adopted to fuse the distilled sentimental knowledge with the semantic embedding, effectively enriching the representation of sentences. Experiments on the SMP2019-ECISA implicit sentiment dataset show that our model fully utilizes the information of the knowledge base and improves the performance of Chinese implicit sentiment analysis.

Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification.

As a research hotspot in the field of natural language processing (NLP), sentiment analysis can be roughly divided into explicit sentiment analysis and implicit sentiment analysis. However, due to the lack of obvious emotion words in the implicit sentiment analysis task and because the sentiment polarity contained in implicit sentiment words is not easily accurately identified by existing text-processing methods, the implicit sentiment analysis task is one of the most difficult tasks in sentiment analysis. This paper proposes a new preprocessing method for implicit sentiment text classification; this method is named Text To Picture (TTP) in this paper. TTP highlights the sentiment differences between different sentiment polarities in Chinese implicit sentiment text with the help of deep learning by converting original text data into word frequency maps. The differences between sentiment polarities are used as sentiment clues to improve the performance of the Chinese implicit sentiment text classification task. It does this by transforming the original text data into a word frequency map in order to highlight the differences between the sentiment polarities expressed in the implicit sentiment text. We conducted experimental tests on two common datasets (SMP2019, EWECT), and the results show that the accuracy of our method is significantly improved compared with that of the competitor’s. On the SMP2019 dataset, the accuracy-improvement range was 4.55–7.06%. On the EWECT dataset, the accuracy was improved by 1.81–3.95%. In conclusion, the new preprocessing method for implicit sentiment text classification proposed in this paper can achieve better classification results.

Sememe knowledge and auxiliary information enhanced approach for sarcasm detection

Sarcasm expression is a pervasive literary technique in which people intentionally express the opposite of what is implied. Accurate detection of sarcasm in a text can facilitate the understanding of speakers’ true intentions and promote other natural language processing tasks, especially sentiment analysis tasks. Since sarcasm is a kind of implicit sentiment expression and speakers deliberately confuse the audience, it is challenging to detect sarcasm only by text. Existing approaches based on machine learning and deep learning achieved unsatisfactory performance when handling sarcasm text with complex expression or needing specific background knowledge to understand. Especially, due to the characteristics of the Chinese language itself, sarcasm detection in Chinese is more difficult. To alleviate this dilemma on Chinese sarcasm detection, we propose a sememe and auxiliary enhanced attention neural model, SAAG. At the word level, we introduce sememe knowledge to enhance the representation learning of Chinese words. Sememe is the minimum unit of meaning, which is a fine-grained portrayal of a word. At the sentence level, we leverage some auxiliary information, such as the news title, to learning the representation of the context and background of sarcasm expression. Then, we construct the representation of text expression progressively and dynamically. The evaluation on a sarcasm dateset, consisting of comments on news text, reveals that our proposed approach is effective and outperforms the state-of-the-art models.

Implicit sentiment analysis based on multi-feature neural network model

Implicit sentiment analysis based on multi-feature neural network model

Context aware semantic adaptation network for cross domain implicit sentiment classification

Cross-domain sentiment classification could be attributed to two steps. The first step is used to extract the text representation, and the other is to reduce domain discrepancy. Existing methods mostly focus on learning the domain-invariant information, rarely consider using the domain-specific semantic information, which could help cross-domain sentiment classification; traditional adversarial-based models merely focus on aligning the global distribution ignore maximizing the class-specific decision boundaries. To solve these problems, we propose a context-aware semantic adaptation (CASA) network for cross-domain implicit sentiment classification (ISC). CASA can provide more semantic relationships and an accurate understanding of the emotion-changing process for ISC tasks lacking explicit emotion words. (1) To obtain inter- and intrasentence semantic associations, our model builds a context-aware heterogeneous graph (CAHG), which can aggregate the intrasentence dependency information and the intersentence node interaction information, followed by an attention mechanism that remains high-level domain-specific features. (2) Moreover, we conduct a new multigrain discriminator (MGD) to effectively reduce the interdomain distribution discrepancy and improve intradomain class discrimination. Experimental results demonstrate the effectiveness of different modules compared with existing models on the Chinese implicit emotion dataset and four public explicit datasets.

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

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.

Multiplex network embedding for implicit sentiment analysis

As one of the hot research directions in natural language processing, sentiment analysis has received continuous and extensive attention. Different from explicit sentiment words indicating sentiment polarity, implicit sentiment analysis is a more challenging problem due to the lack of sentiment words, which makes it inadequate to use traditional sentiment analysis method to judge the polarity of implicit sentiment. This paper takes sentiment analysis as a special sign link prediction problem, which is different from traditional text-based method. In particular, by performing the word graph-based text level information embedding and heterogeneous social network information embedding (i.e. user social relationship network embedding, and user-entity sentiment network embedding), the proposed scheme learns the highly nonlinear representations of network nodes, explores early fusion method to combine the strength of these two types of embedding modeling, optimizes all parameters simultaneously and creates enhanced context representations, leading to better capture of implicit sentiment polarity. The proposed method has been examined on real-world dataset, for implicit sentiment link prediction task. The experimental results demonstrate that the proposed method outperforms state-of-the-art schemes, including LINE, node2vec, and SDNE, by 20.2%, 19.8%, and 14.0%, respectively, on accuracy, and achieves at least 14% gains on AUROC. For sentiment analysis accuracy, the proposed method achieves AUROC of 80.6% and accuracy of 78.3%, which is at least 31% better than other models. This work can provide useful guidance on the implicit sentiment analysis.

Implicit sentiment analysis based on graph attention neural network

AbstractSentiment analysis is one of the crucial tasks in the field of natural language processing. Implicit sentiment suffers a significant challenge because the sentence does not include explicit emotional words and emotional expression is vague. This paper proposed a novel implicit sentiment analysis model based on graph attention convolutional neural network. A graph convolutional neural network is used to propagate semantic information. The attention mechanism is employed to compute the contribution to the emotional expression of words. In order to solve the problem of multiple attention preserving repeated information, orthogonal attention constraint was used to make different attention store different emotional information; given the uneven distribution of emotional information, score attention constraint was proposed to make the model focus on a limited number of essential words. The performance of the proposed model was verified on implicit sentiment datasets. The F value reached 88.16%, which is higher than the benchmark model in the literature. The attention mechanism is analyzed to verify the effectiveness of orthogonal constraint and score constraint.

Exploiting textual and relationship information for fine-grained financial sentiment analysis

Sentiment analysis aims to identify the way in which sentiments are expressed in texts. State-of-the-art approaches base their analyses solely on the given text, which complicates the detection of implicit sentiments and ignores the role of sentiment contagion. In the financial domain, sentiment could be spread across multiple platforms, such as in company and analyst reports, news articles, and microblogs. Thus, to capture implicit sentiments and the contagion process, we introduce a novel approach that leverages the text and contextual information of a record for fine-grained sentiment analysis. Based on this information, we generate a record representation, which is used in an adapted feed-forward neural network. Our proposed solution improves the performance by as much as 15% and 234% relative to multiple baselines. Our work demonstrates the impact of implicit sentiment as well as the importance of different relationships for sentiment prediction on company and analyst reports, news articles, and microblogs. For example, we identified timestamp information as being non-essential for the fine-grained sentiment analysis of company and analyst reports. Although we are able to showcase improvements in financial sentiment analysis, sentiment contagion and limited context are two common problems that continue to prevail. Therefore, by re-defining sentiment analysis as a multi-text problem, our proposed solution can be applied across multiple domains and text types, such as product reviews.

Does COVID 19 Impact Market Sentiment and Stock Returns?- Evidence from India

This article examines the impact of COVID-19 on market sentiment and stock market returns for firms listed in India using daily data from January 2020 to May 2021, a period that includes the first and second wave of the COVID-19 pandemic. We applied wavelet coherence to explore the co-movement of COVID-19 and sentiment. Market-related implicit sentiment proxies depicting the market's bullish sentiment negatively correlate with COVID-19, whereas sentiment proxies representing the market's bearish sentiment positively correlate with COVID-19 during the first wave of the pandemic. No co-movement was found between sentiment proxies and COVID-19 during the second wave of the pandemic. We also applied event study methodology to measure abnormal returns and regression analysis to explain the causes of abnormal returns during both the pandemic waves. We found statistically significant negative abnormal returns during wave 1 of COVID-19 due to increased negative sentiment in the market. During wave 2, we did not find abnormal returns to be statistically significant. We also found that during wave 1, return on assets (ROA) is statistically associated with abnormal returns, while during wave 2, no firm-specific characteristic is statistically associated with abnormal returns. These findings are among the first empirical evidence of COVID-19, market sentiment, and stock market returns during wave 1 and wave 2 of the pandemic.

Dual channel sentiment classification model based on grammar rules and multi attention

As convolutional neural networks (CNN) and recurrent neural networks (RNN) have achieved excellent results in the field of Chinese text sentiment analysis. More and more researchers are extracting features of text based on the advantages of CNN and RNN in extracting features. However, the current scholars fail to make full use of sentiment language resources such as sentiment words, negatives and degree adverbs when they adopt deep learning methods. For implicit texts without explicit sentiment words, they cannot fully identify the differences between words and sentiment tendency. At the same time, it fails to consider the grammatical structure of the text, which leads to poor classification effect for some turning sentences or summary sentences. In addition, most of the models are mainly input in the form of word vector. For English, it is very convenient to segment words through spaces between words, but for Chinese, there may be inaccurate word segmentation, which will reduce the accuracy of classification. To solve these problems, a dual channel sentiment classification model based on grammar rules and multi attention (DCGA) is proposed. Firstly, the text with clear sentiment tendency is obtained according to the grammar rules, and the local features of the text are extracted by CNN channel. Considering that the grammar rules may ignore the context information, bi-directional long short term memory network (Bi-LSTM) channel is used to extract the global features containing the context information, and attention pooling is used to improve the sentiment information extracted by CNN channel. Then, the part of speech attention mechanism is used to mine the implicit sentiment features in the sentiment text to solve the poor classification effect caused by the existence of implicit text. Finally, the local features obtained by CNN channel and the global features obtained by RNN channel are fused for classification. Experimental results on four Chinese comment text datasets show that the proposed model outperforms most existing methods in accuracy.

A message-passing multi-task architecture for the implicit event and polarity detection.

Implicit sentiment analysis is a challenging task because the sentiment of a text is expressed in a connotative manner. To tackle this problem, we propose to use textual events as a knowledge source to enrich network representations. To consider task interactions, we present a novel lightweight joint learning paradigm that can pass task-related messages between tasks during training iterations. This is distinct from previous methods that involve multi-task learning by simple parameter sharing. Besides, a human-annotated corpus with implicit sentiment labels and event labels is scarce, which hinders practical applications of deep neural models. Therefore, we further investigate a back-translation approach to expand training instances. Experiment results on a public benchmark demonstrate the effectiveness of both the proposed multi-task architecture and data augmentation strategy.

Chinese Implicit Sentiment Analysis based on Hybrid Neural Networks

Implicit sentiment analysis is an important part of sentiment computing, especially sentiment analysis based on deep learning has become a research hotspot in recent years. In this paper, convolutional neural networks are used to extract text features, combined with long-term and short-term memory network (LSTM) structure to extract context information, and add attention mechanism to the network to construct a new hybrid neural network model to achieve implicit emotion for text analysis. The hybrid neural network model extracts more meaningful sentence semantic and structural hidden features from the word-level and sentence-level hierarchies, respectively, and pays attention to the features with a large emotional contribution rate through the attention mechanism. The classification accuracy rate of the model on the public implicit sentiment dataset has reached 77%. The research of implicit sentiment analysis can improve the effect of text sentiment analysis more comprehensively, and further promote the application of text sentiment analysis in the fields of knowledge embedding, text representation learning, user modeling and natural language.

Context Aware Sentiment Link Prediction in Heterogeneous Social Network

People often express opinions towards others in a social network, causing sentiment links to form among users. To develop effective methods for discovering implicit sentiment links among users, the extraction and exploitation of structural semantic information from heterogeneous social networks are of great importance. We propose a novel heterogeneous social network embedding-based approach for sentiment link prediction that takes both global structural information with multi-dimensional relations and heterogeneous context information into consideration to leverage rich and intrinsic association information. Specifically, the attributed heterogeneous social network and Sentic LSTM-based sentiment link network are employed to incorporate various explicit context knowledge and implicit multi-dimensional user interaction association knowledge into representation learning and sentiment link prediction. The experimental results on a real-world dataset show that the proposed approach has advantages over the state-of-the-art baselines. The results show the effectiveness of incorporating social relations and profile context information into sentiment link prediction, especially in cold-start scenarios. The learned embedding representation that incorporates both structural information with multi-dimensional relations and context information from heterogeneous social networks can improve sentiment link prediction performance. The proposed approach is effective and feasible for detecting unobserved sentiment links from online social networks and outperforms the state-of-the-art baselines in sentiment link prediction tasks.

Implicit emotional tendency recognition based on disconnected recurrent neural networks

Implicit emotional tendency recognition is a challenging task, for the patterns of implicit emotional sentences have no obvious sentiment words or privative words. Currently, deep learning approaches have shown a huge potential for this problem. This paper proposes a hierarchical disconnected recurrent neural network to overcome the problem of the lack of emotional information in implicit sentiment sentence recognition. The proposed network first encodes both words and characters in implicit sentiment sentence by using the disconnected recurrent neural network and then fuses the context information of this sentence through the hierarchical structure. The capsule network is adopted to construct different fine-grained context information for extracting high-level feature information and provide additional semantic information for emotion recognition by using the context information. This approach improves the accuracy in implicit emotion recognition. Experimental results prove that the proposed model outperform various current mainstream models. The F1 of ours reaches 81.5%, which is 2%-3% higher than those of the current mainstream models.

Implicit emotional tendency recognition based on disconnected recurrent neural networks

Implicit emotional tendency recognition is a challenging task, for the patterns of implicit emotional sentences have no obvious sentiment words or privative words. Currently, deep learning approaches have shown a huge potential for this problem. This paper proposes a hierarchical disconnected recurrent neural network to overcome the problem of the lack of emotional information in implicit sentiment sentence recognition. The proposed network first encodes both words and characters in implicit sentiment sentence by using the disconnected recurrent neural network and then fuses the context information of this sentence through the hierarchical structure. The capsule network is adopted to construct different fine-grained context information for extracting high-level feature information and provide additional semantic information for emotion recognition by using the context information. This approach improves the accuracy in implicit emotion recognition. Experimental results prove that the proposed model outperform various current mainstream models. The F1 of ours reaches 81.5%, which is 2%-3% higher than those of the current mainstream models.

Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic Text

Abstract Sentiment analysis aims to predict sentiment polarities (positive, negative or neutral) of a given piece of text. It lies at the intersection of many fields such as Natural Language Processing (NLP), Computational Linguistics, and Data Mining. Sentiments can be expressed explicitly or implicitly. Arabic Sentiment Analysis presents a challenge undertaking due to its complexity, ambiguity, various dialects, the scarcity of resources, the morphological richness of the language, the absence of contextual information, and the absence of explicit sentiment words in an implicit piece of text. Recently, deep learning has obviously shown a great success in the field of sentiment analysis and is considered as the state-of-the-art model in Arabic Sentiment Analysis. However, the state-of-the-art accuracy for Arabic sentiment analysis still needs improvements regarding contextual information and implicit sentiment expressed in different real cases. In this paper, an efficient Bidirectional LSTM Network (BiLSTM) is investigated to enhance Arabic Sentiment Analysis, by applying Forward-Backward encapsulate contextual information from Arabic feature sequences. The experimental results on six benchmark sentiment analysis datasets demonstrate that our model achieves significant improvements over the state-of-art deep learning models and the baseline traditional machine learning methods.