Sentiment Polarity Research Articles

Financial Report Sentiment Analysis Using Loughran-mcdonald Dictionary and BERT

In the ever-changing world of financial markets, understanding investor behavior and making informed decisions relies heavily on sentiment analysis. This study delves into the integration of traditional techniques, such as the Loughran- McDonald dictionary, with advanced natural language processing (NLP) methods utilizing BERT (Bidirectional Encoder Representations from Transformers). The goal is to enhance the accuracy and depth of sentiment analysis in financial reports.To begin, we employ the specialized Loughran-McDonald dictionary designed for financial sentiment analysis. This lexicon includes domainspecific word lists for positive and negative sentiments, forming a solid foundation for sentiment scoring. Expanding on this foundation, we incorporate BERT, an advanced transformerbased NLP model. BERT’s contextual understanding of language and ability to capture intricate semantic relationships within financial texts aim to overcome the limitations of rule-based sentiment analysis. The methodology involves preprocessing financial reports, integrating Loughran-McDonald sentiment scores, and fine-tuning BERT for financial sentiment classification. This hybrid approach leverages both the domain expertise encoded in the dictionary and BERT’s contextual comprehension of financial jargon and nuances. We validate and evaluate our implementation using a diverse dataset comprising quarterly earnings releases, annual reports, and other relevant disclosures. Performance metrics such as precision, recall, and F1 score are analyzed to assess the effectiveness of our hybrid approach compared to individual methods. The findings have significant implications for financial analysts, investors, and policymakers by providing a more nuanced understanding of sentiment in financial reports. Our hybrid approach aims to offer improved accuracy in capturing sentiment polarity while facilitating more informed decision-making in today’s complex and dynamic realm of financial markets.

Aspect-specific Parsimonious Segmentation via Attention-based Graph Convolutional Network for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) involves determining sentiment polarity in sentences concerning specific aspects. Recent approaches in ABSA have incorporated dependency parsing to gain a deeper understanding of word associations within aspect-related terms. Nevertheless, these models typically concentrate solely on dependency connections while neglecting their specific characteristics or types. This can lead to sentiment inconsistencies, particularly when analyzing sentences with multiple aspects and diverse polarities. To address this challenge, we propose an innovative solution known as Aspect-specific Parsimonious Segmentation via Attention-based Graph Convolutional Network (APSA-GCN) for ABSA. Our model consists of two key modules that harness a variety of features, including syntax, semantics, and lexicon-based representations. The aspect-specific parsimonious segmentation framework segments the data and directs it to parsimonious dual filters, retaining only aspect-specific sentences called explicit data for lexicon-based representation. Subsequently, the Attention-based Graph Convolutional Network (GCN) is employed to extract syntactic and semantic features more explicitly, thereby enriching the representation of aspect-related features. Extensive experiments were conducted on various benchmark datasets, and the results confirm the model’s efficacy as our method achieves state-of-the-art performance. Moreover, our introduced APS framework can be effortlessly incorporated into other benchmark models to enhance the outcomes of ABSA.

Sentiment Analysis on Textual Data - A comparison of accuracy using different algorithms

Sentiment Analysis and Opinion Mining is an adverse area of research that deals with the judgement and evaluation criteria in an Emotional communication. Sentiment analysis is generally classified into three different level namely they are document level, sentence level and entity-aspect level. Several research papers are published based on machine learning techniques such as support vector machine, maximum entropy(maxent) and naïve bayes classifiers and random forest classifiers as the most widely used algorithm in sentiment analysis. Our approach is to take dataset and implement the same using three machine learning models and compare their accuracy. Index Terms— Sentiment analysis, machine learning, Support Vector machine, Naïve bayes classifier, Sentiment polarity.

Label-semantics enhanced multi-layer heterogeneous graph convolutional network for Aspect Sentiment Quadruplet Extraction

Aspect-based sentiment analysis (ABSA) is a pivotal area within natural language processing (NLP) that focuses on extracting fine-grained sentiment information from text. A particularly demanding task within ABSA is Aspect Sentiment Quadruplet Extraction (ASQE), which aims to identify quadruplets comprising aspect terms, their corresponding opinion terms, sentiment polarity, and categories. This level of analysis is crucial for downstream applications such as sentiment monitoring and user experience research, offering nuanced insights into textual data. However, current methodologies fall short of fully leveraging the rich linguistic features of sentences and the semantic information of labels embedded within sentences. To address this gap, this paper introduces a novel framework, the Label-Semantics Enhanced Multi-layer Heterogeneous Graph Convolutional Network (LSEMH-GCN), specifically designed for ASQE. Our model integrates sentence linguistic features and label semantics to construct a graph neural network tailored for this task. It employs a multi-layer graph convolutional network that synergizes various linguistic features, and utilizes Biaffine attention to enrich the label probability distribution for token pairs with semantic label information. Furthermore, our approach introduces a token pair vector concatenation strategy alongside an advanced asymmetric label tagging scheme to enhance quadruplet extraction. Comprehensive evaluations on benchmark datasets reveal that LSEMH-GCN significantly surpasses existing state-of-the-art models, establishing a new benchmark for ASQE. Our model achieves an average F1 score improvement of 15.52% and 12.30% on the Restaurant-ACOS and Laptop-ACOS datasets, respectively.

MFSC: A Multimodal Aspect-Level Sentiment Classification Framework with Multi-Image Gate and Fusion Networks

Currently, there is a great deal of interest in multimodal aspect-level sentiment classification using both textual and visual information, which changes the traditional use of only single-modal to identify sentiment polarity. Considering that existing methods could be strengthened in terms of classification accuracy, we conducted a study on aspect-level multimodal sentiment classification with the aim of exploring the interaction between textual and visual features. Specifically, we construct a multimodal aspect-level sentiment classification framework with multi-image gate and fusion networks called MFSC. MFSC consists of four parts, i.e., text feature extraction, visual feature extraction, text feature enhancement, and multi-feature fusion. Firstly, a bidirectional long short-term memory network is adopted to extract the initial text feature. Based on this, a text feature enhancement strategy is designed, which uses text memory network and adaptive weights to extract the final text features. Meanwhile, a multi-image gate method is proposed for fusing features from multiple images and filtering out irrelevant noise. Finally, a text-visual feature fusion method based on an attention mechanism is proposed to better improve the classification performance by capturing the association between text and images. Experimental results show that MFSC has advantages in classification accuracy and macro-F1.

Russia Conflict on Twitter: Social factors and polarity on users’ interactions

In the aftermath of armed conflicts, societal expressions unfold through diverse communication channels, with Twitter. Individuals share these expressions, aiming for broader societal consumption, fostering interaction across impacted entities—individuals, businesses, organizations, and governments. This analytical endeavor aims to analyze interaction patterns responding to sociocultural factors and sentimentally charged content on Twitter in the context of the Russia-Ukraine conflict. This research employed a sequential mixed approach to examine social factors in user publications on Twitter and assess their impact on interactions, considering sentimental polarity. The qualitative phase involved netnographic exploration of a total of 2578 tweets, collected from users World Trade Organization since February 24, 2022, until March 31, 2022. The subsequent quantitative phase analyzed the relationship between social factors, sentimental polarity, and user interactions through decision tree analysis. The results show that notably, the categories MET-Mention (35.82%) and MSG-Message (35.82%) emerged as the most frequent Two interactions were the most common (52.5%). The primary theme discussed in the messages was Information with 52.99% of the twits. Negative polarity emerged as the factor triggering more engagement, resulting in higher interaction levels. The majority of interactions (52.5%) were characterized by two interactions. In conclusion, the dominance of the information category underscores the pivotal role of social media in disseminating information during global events. Furthermore, negative sentiment, is associated with conflict-related concerns, correlated with higher interaction levels.

Aspect based sentiment analysis using a novel ensemble deep network

Aspect-based sentiment analysis (ABSA) is a fine-grained task in natural language processing, which aims to predict the sentiment polarity of several parts of a sentence or document. The essential aspect of sentiment polarity and global context have deep relationships that have not received enough attention. This research work design and develops a novel ensemble deep network (EDN) which comprises the various network and integrated to enhance the model performance. In the proposed work the words of the input sentence are converted into word vectors using the optimised bidirectional encoder representations from transformers (BERT) model and an optimised BERT-graph neural networks (GNN) model with convolutions is built that analyses the ABSA of the input sentence. The optimised GNN model with convolutions for context-based word representations is developed for the word-vector embedding. We propose a novel EDN for an ABSA model for optimised BERT over GNN with convolutions. The proposed ensemble deep network proposed system (EDN-PS) is evaluated with various existing techniques and results are plotted in terms of metrics for accuracy and F1- score, concluding that the proposed EDN-PS ensures better performance in comparison with the existing model.

Progressive Multigranularity Information Propagation for Coupled Aspect-Opinion Extraction.

Coupled aspect-opinion extraction aims to identify aspect-opinion pairs in the form of (aspect term, opinion term) or triplets in the form of (aspect term, opinion term, sentiment polarity) from user-generated texts. Compared to the traditional aspect-based sentiment prediction or extraction tasks, coupled aspect-opinion extraction needs to associate aspects with their corresponding opinions and organize opinion-related information into structured outputs. The existing works either divide this task into subproblems (i.e., term extraction and relation prediction) or utilize a unified tagging scheme. However, these methods only focus on atomic word-level interactions and ignore the intensive information propagation among different granularities (e.g., words and word pairs). To address this limitation, we propose a progressive multigranularity information propagation network that progressively explores three types of correlations with different granularities. Specifically, our model starts with the most basic word-level correlations by composing all possible word pairs. In the second stage, the pairwise relation information is used to update the word features. The last stage propagates information among word pairs to produce the relation scores. We treat the task as a unified relation prediction problem and construct an end-to-end framework that iteratively conducts the three-stage information propagation to refine the textual representations. Comprehensive experiments on different aspect-based sentiment analysis benchmarks clearly demonstrate the effectiveness of the proposed approach.

DualGCN: Exploring Syntactic and Semantic Information for Aspect-Based Sentiment Analysis.

The task of aspect-based sentiment analysis aims to identify sentiment polarities of given aspects in a sentence. Recent advances have demonstrated the advantage of incorporating the syntactic dependency structure with graph convolutional networks (GCNs). However, their performance of these GCN-based methods largely depends on the dependency parsers, which would produce diverse parsing results for a sentence. In this article, we propose a dual GCN (DualGCN) that jointly considers the syntax structures and semantic correlations. Our DualGCN model mainly comprises four modules: 1) SynGCN: instead of explicitly encoding syntactic structure, the SynGCN module uses the dependency probability matrix as a graph structure to implicitly integrate the syntactic information; 2) SemGCN: we design the SemGCN module with multihead attention to enhance the performance of the syntactic structure with the semantic information; 3) Regularizers: we propose orthogonal and differential regularizers to precisely capture semantic correlations between words by constraining attention scores in the SemGCN module; and 4) Mutual BiAffine: we use the BiAffine module to bridge relevant information between the SynGCN and SemGCN modules. Extensive experiments are conducted compared with up-to-date pretrained language encoders on two groups of datasets, one including Restaurant14, Laptop14, and Twitter and the other including Restaurant15 and Restaurant16. The experimental results demonstrate that the parsing results of various dependency parsers affect their performance of the GCN-based models. Our DualGCN model achieves superior performance compared with the state-of-the-art approaches. The source code and preprocessed datasets are provided and publicly available on GitHub (see https://github.com/CCChenhao997/DualGCN-ABSA).

A deep learning and large group consensus based cruise satisfaction evaluation model with online reviews

Cruise satisfaction assessment is pivotal for both passenger experiences and the enterprise's industry competitiveness. To do that, this research proposes a deep learning based cruise satisfaction evaluation model by integrating cruise online reviews and a large group consensus model. In order to acquire the dimensions influencing cruise passenger satisfaction, attribute extraction is employed. Unlike traditional methods that rely on word-based attribute representation, this method employs sentence-level information to comprehensively capture attributes that are of genuine concern to users, including implicit ones. Additionally, it utilizes the pre-trained language model, BART, to accurately predict sentiment polarity by treating sentiment analysis as a generation task based on online reviews from the CruiseCritic website. Subsequently, the proposed consensus model is implemented to determine the final satisfaction level, taking into consideration subgroup weights and an ideal solution-based feedback mechanism. This mechanism facilitates the provision of reasonable adjustment recommendations tailored to the context of online evaluations. Ultimately, the research findings contribute valuable insights to enhance service quality within the Royal Caribbean International cruise line.

Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models

The rapid rise of e-commerce platforms has changed people's shopping habits, driving the popularity of online shopping. Users express their opinions on products and services by purchasing products on platforms and posting comments. These comment data contain rich user experiences, which are crucial for enterprises to understand user needs and improve product quality. Sentiment analysis of comment text is an important research direction in text mining, focusing on how to extract user evaluations of products from comment data to provide comprehensive, authentic, and accurate product feedback. This paper mainly investigates aspect-based fine-grained sentiment analysis. Because users express multiple sentiments towards different aspects in comments, coarse-grained sentiment analysis cannot accurately capture users' sentiment tendencies. This study utilizes artificial intelligence technology based on deep learning, firstly, it constructs auxiliary training samples to transform aspect sentiment tasks into machine reading comprehension or language inference tasks, using BERT model to extract text features and sentence features from comment data. To guide the model to focus on the features most relevant to the given aspect, a cross-attention mechanism is utilized to cross-focus the features of text with aspect category features. Finally, the sentiment polarity of given aspect category text can be predicted through a forward network. Experimental results on multiple datasets demonstrate that this method outperforms other deep learning models.

We care: A User-Focused Sentiment Analysis Framework for Social Media Interactions

This paper addresses the sentiment analysis of WhatsApp chats, focusing on the challenges and opportunities presented by conversational data. The introduction outlines the prevalence of sentiment analysis in social media platforms and the significance of accurate sentiment categorization. The literature review highlights previous work in sentiment analysis, emphasizing methods for categorizing sentiment polarity and feature selection. The proposed system introduces a comprehensive approach to sentiment analysis tailored to WhatsApp chats, considering the unique characteristics of conversational data. Key features include user-centric inclusion, nuanced extraction of sentiment, context-aware analysis, and effective handling of conversational nuances. The method section details the proposed system's emphasis on real-time sentiment tracking, user-specific sentiment scores, and integration with WhatsApp's API for broader applications. The results section summarizes the positive outcomes of the proposed method, including user-centric inclusion, nuanced extraction, context-aware illustration, and effective content handling. The future scope section discusses potential enhancements such as real-time sentiment alerts, multilingual support, detailed sentiment analysis, improved user feedback systems, personalized sentiment metrics, and collaboration capabilities. The conclusion highlights the significance of the "WeCare" sentiment analysis project in improving our understanding of digital conversations, particularly within the context of WhatsApp chats. The project's commitment to user-centric design, adaptability, and ethical considerations positions it as a dynamic tool for enhancing digital interactions and well-being. Finally, acknowledgments recognize the contributions of the development team and academic mentor.

Discovering Untapped Potential in Finance Markets Using NLP-Driven Sentiment Analysis

Objectives: The main objective in Sentiment Analysis, and financial data is to decode sentiments through the use of Natural Language Processing (NLP) and Machine Learning (ML). This provides insights into market trends that are essential for risk management and well-informed financial decisions. By removing emotion from financial writing, gives stakeholders practical advice for wise investments in dynamic markets. The main objective is to increase the accuracy of Multinomial Naïve Bayes (Multinomial NB). In this work, we used purely ML models to get the accuracy more than the previous works, which got 74%, and in this work, we can see the accuracy increased to approximately 82%. Methods: This specific Research Work employs Machine Learning approaches to extract sentiment polarity (positive, negative, and neutral) from financial text data. The used ML models are- Multinomial Naïve Bayes (Multinomial NB), Logistic Regression (LR), KNeighbors, Decision Tree, and Random Forest algorithms are used for Sentiment Analysis. Findings: Studies conducted in the financial sector have shown that sentiment and informational substance in stock news have a big impact on market factors such as trading volume, volatility, stock prices, and corporate earnings. This study involves approximately 6000 newspaper articles to extract the polarity from financial text data. The incorporation of Multinomial NB of Natural Language Processing techniques results in a marginal improvement in the sentiment classification model performance among all the models used, where the previous works got the lowest accuracy of 74%. Novelty: This research introduces advanced ML models for extracting financial sentiment from text, including Random Forest, LR, KNeighbors, Decision Tree, and Multinomial NB previously unexplored in similar studies. Notably, the Multinomial NB model accuracy rose significantly from 65% to 82%. The study also presents sentiment segregation (positive, negative, neutral) visualized in a donut chart. Keywords: Sentiment Analysis, Machine Learning, Natural Language Processing, Financial data, Stock market prediction

Hybrid deep learning approach for sentiment analysis using text and emojis

ABSTRACT Sentiment Analysis (SA) is a technique for categorizing texts based on the sentimental polarity of people’s opinions. This paper introduces a sentiment analysis (SA) model with text and emojis. The two preprocessed data’s are data with text and emojis and text without emojis. Feature extraction consists text features and text with emojis features. The text features are features like N-grams, modified Term Frequency-Inverse Document Frequency (TF-IDF), and Bag-of-Words (BoW) features extracted from the text. In classification, CNN (Conventional Neural Network) and MLP (Multi-Layer Perception) use emojis and text-based SA. The CNN weight is optimized by a new Electric fish Customized Shark Smell Optimization (ECSSO) Algorithm. Similarly, the text-based SA is carried out by hybrid Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) classifiers. The bagged data are given as input to the classification process via RNN and LSTM. Here, the weight of LSTM is optimized by the suggested ECSSO algorithm. Then, the mean of LSTM and RNN determines the final output. The specificity of the developed scheme is 29.01%, 42.75%, 23.88%,22.07%, 25.31%, 18.42%, 5.68%, 10.34%, 6.20%, 6.64%, and 6.84% better for 70% than other models. The efficiency of the proposed scheme is computed and evaluated.

Studying the Impact Of D.P.SL Model on Online Identity Management

This paper investigates online identities through social media language use, with a focus on classifying online identity within textual conversations. It sheds light on how Demographic (D) groups and Personality (P) impact the use of Social-Media Language (SL) for identity representation online. This study was conducted in 2023 when there were 4.76 billion social media users worldwide, making it essential to study how social media language is used in textual conversations to convey online identity. The study defines social media language as consisting of emoticons/emojis, abbreviations, and mixed language within textual conversations, which have become essential for expressing feelings and emotions during conversations. The D.P.SL based survey conducted for this work aimed to understand how demographic groups and personality are related to social media language. Based on the total number of social media user worldwide, 400 responses (required based on Cochran and Yamane’s formulae sample-size calculation) and the survey was distributed across various verified online survey exchange platforms. However, 406 responses were recorded with young people in age groups of 18-24 and 25-34 using social media language more as it has become a part of their social media habits. The study also found that emoticons/emojis and slang abbreviations with letter reduplication were quite common, making conversations lively and funny. Additionally, individuals whose primary language is not English use their native language but type in English for quick communication. Subsequent study is to be conducted using online mock group conversations between participating respondents to further understand correlations, causation, and concurrency on how ‘online identity’ is managed during online communications via social media language, its context of use, and polarity sentiment.

Research on Aspect-Level Sentiment Analysis Based on Adversarial Training and Dependency Parsing

Aspect-level sentiment analysis is used to predict the sentiment polarity of a specific aspect in a sentence. However, most current research cannot fully utilize semantic information, and the models lack robustness. Therefore, this article proposes a model for aspect-level sentiment analysis based on a combination of adversarial training and dependency syntax analysis. First, BERT is used to transform word vectors and construct adjacency matrices with dependency syntactic relationships to better extract semantic dependency relationships and features between sentence components. A multi-head attention mechanism is used to fuse the features of the two parts, simultaneously perform adversarial training on the BERT embedding layer to enhance model robustness, and, finally, to predict emotional polarity. The model was tested on the SemEval 2014 Task 4 dataset. The experimental results showed that, compared with the baseline model, the model achieved significant performance improvement after incorporating adversarial training and dependency syntax relationships.

Hierarchical denoising representation disentanglement and dual-channel cross-modal-context interaction for multimodal sentiment analysis

Multimodal sentiment analysis aims to extract sentiment cues from various modalities, such as textual, acoustic, and visual data, and manipulate them to determine the inherent sentiment polarity in the data. Despite significant achievements in multimodal sentiment analysis, challenges persist in addressing noise features in modal representations, eliminating substantial gaps in sentiment information among modal representations, and exploring contextual information that expresses different sentiments between modalities. To tackle these challenges, our paper proposes a new Multimodal Sentiment Analysis (MSA) framework. Firstly, we introduce the Hierarchical Denoising Representation Disentanglement module (HDRD), which employs hierarchical disentanglement techniques. This ensures the extraction of both common and private sentiment information while eliminating interference noise from modal representations. Furthermore, to address the uneven distribution of sentiment information among modalities, our Inter-Modal Representation Enhancement module (IMRE) enhances non-textual representations by extracting sentiment information related to non-textual representations from textual representations. Next, we introduce a new interaction mechanism, the Dual-Channel Cross-Modal Context Interaction module (DCCMCI). This module not only mines correlated contextual sentiment information within modalities but also explores positive and negative correlation contextual sentiment information between modalities. We conducted extensive experiments on two benchmark datasets, MOSI and MOSEI, and the results indicate that our proposed method offers state-of-the-art approaches.

Improving span-based Aspect Sentiment Triplet Extraction with part-of-speech filtering and contrastive learning

Aspect Sentiment Triple Extraction (ASTE), a subtask of fine-grained sentiment analysis, aims to extract aspect terms, opinion terms, and their corresponding sentiment polarities from sentences. Previous methods often enumerated all possible spans of aspects and opinions as candidate spans that contain many invalid and irrelevant spans. This made the model training and prediction more difficult due to noised spans, leading to poor performance. To address this issue, we first propose a novel span-level approach that explicitly considers prior grammatical knowledge to generate possible candidate spans by part-of-speech filtering. In this way, our approach can make the model easier to be trained and achieve higher performance at the test stage. Besides, the quality of span-level representation of aspects and opinions is crucial for predicting their sentiment relation. To build a high-quality span-level representation of aspects and opinions, we first incorporate the contextual embedding of the entire sequence into span-level representations. Then, we introduce an auxiliary loss based on contrastive learning to make a more compact representation of the same polarities. Experimental evaluations on the 14Lap, 14Res, 15Res, and 16Res datasets demonstrate the effectiveness of our model, achieving state-of-the-art performance in span-based triplet extraction.

The Politics of Blame-Seeking: Strategic Antagonism, Effective Alignment and Benefitting From Backlash

Why would a politician ever want to be blamed? Under what contextual conditions might blame-seeking behaviour emerge as a rational strategy? What tactics, tools and strategies might they deploy? Where is the empirical evidence of blame-seeking in action and why does it matter? These are the questions this article engages with as it challenges the long-standing ‘self-evident truth’ within political science that blame-is-bad. We argue that a new ‘blame game’ has emerged in which blame generation is deployed not solely to taint opponents but also to demonstrate the blame-seeker’s willingness to challenge convention, break the rules, or side with the marginalized. In a broader context characterized by democratic dissatisfaction, anti-political sentiment and affective polarization, we suggest that blame-seeking assumes a powerful symbolic and performative dimension. Antagonizing certain sections of society and then harnessing the backlash provides a powerful political strategy which challenges traditional scholarly assumptions about credit and blame existing in a zero-sum relationship. We illustrate these arguments using the case of Boris Johnson’s rise to the British premiership. A focus on blame-seeking, we suggest, expands our understanding of what politicians say and do to achieve their goals in polarized political contexts.

Unraveling the relationship between coastal landscapes and sentiments: An integrated approach based on social media data and interpretable machine learning methods

AbstractCoastal landscapes exert a significant impact on the human sentimental perceptions and physical and mental well‐being of people. However, little is known about explicitly linking between the landscape characteristics and people's sentimental preferences expressed in social media data. The main objective of this study was to explore the nonlinear and interaction effects of key factors that influenced sentiments in the coastal areas of Hong Kong, considering both subjective landscape preferences and objective landscape patterns. We quantified users' sentiment polarity based on the crowdsourcing textual data of Flickr. To study users' subjective landscape preferences, we computed various visual landscape objects' proportion in images. Meanwhile, eight user clusters and nine image clusters were detected by the identified visual object labels. We quantified objective landscape patterns considering the land use pattens and the availability of public service facilities. Finally, we utilized an interpretable classification model to analyze the factors that may affect sentiments and their interplay interactions. We found that ecotourism‐related clusters exhibited the most positive sentiment. The proportion of floor and sky pixels in images exhibits the highest global relative importance when predicting sentiments. This study extends a new insight on the relationship between landscape characteristics and sentiments from both subjective and objective perspectives based on social media data and interpretable machine learning methods. This research may help decision‐makers in designing landscapes that aptly satisfy to the needs of the public and promote sustainable management of the coastal environment.