Sentiment Lexicon Research Articles

Automatically generate sentiment lexicon for the Persian stock market

Automatically generate sentiment lexicon for the Persian stock market

Identifying causal effects of the clinical sentiment of patients' nursing notes on anticipated fall risk stratification

Stratifying patients with high risks of falling based on assessment with nursing notes is essential for tailoring anticipated fall prevention strategies. However, the average exposure effects of the clinical sentiment polarity (CSP) of nursing notes on the fall risks of patients are still not well understood. Thus, this study proposes a causal inference framework to identify the exposure effect on patients' anticipated fall risks using electronic nursing notes. The augmented inverse probability weighting model is leveraged to estimate the average exposure effect in a quasi-experiment. The experimental data contains 334,000 words of 2,434 nursing notes from the MIMIC dataset. The results show that the fall risk of exposure to the positive CSP is 0.0054 lower than the control group. The contribution of this paper is three-fold. First, the exposure effect of the CSP in the clinical nursing notes is identified with the causal inference method, which augments the Morse fall scale by analyzing the nursing notes. Second, the Pearson correlations between the exposure and the MFS are sensitive to the cut-off of the clinical sentiment score. By contrast, our results show that their impact on the fall risks in the weighting model can be identified (negative effect) when reducing the bias of covariate imbalance. Compared to the risk factors in the Morse fall scale, the CSP takes a scale of 10 for predicting anticipated fall risks. Third, the CSP factor can be extracted from the sentiment lexicons of nursing notes on the patient's fall risks with information processing methods, which contributes to the clinical decision support of anticipated fall prevention.

Depression in South Korean Adolescents Captured by Text and Opinion Mining of Social Big Data.

Depression in adolescence is recognized as an important social and public health issue that interferes with continued physical growth and increases the likelihood of other mental disorders. The goal of this study was to examine online documents posted by South Korean adolescents for 3 years through the text and opinion mining of collectable documents in order to capture their depression. The sample for this study was online text-based individual documents that contained depression-related words among adolescents, and these were collected from 215 social media websites in South Korea from 1 January 2012 to 31 December 2014. A sentiment lexicon was developed for adolescent depressive symptoms, and such sentiments were analyzed through opinion mining. The depressive symptoms in the present study were classified into nine categories as suggested by the Diagnostic and Statistical Manual for Mental Disorders, 5th Edition (DSM-5). The association analysis and decision tree analysis of data mining were used to build an efficient prediction model of adolescent depression. Opinion mining indicated that 15.5% were emotionally stable, 58.6% moderately stressed, and 25.9% highly distressed. Data mining revealed that the presence of depressed mood most of the day or nearly every day had the greatest effect on adolescents' depression. Social big data analysis may serve as a viable option for developing a timely response system for emotionally susceptible adolescents. The present study represents one of the first attempts to investigate depression in South Korean adolescents using text and opinion mining from three years of online documents that originally amounted to approximately 3.1 billion documents.

Evolution of online public opinions on major accidents: Implications for post-accident response based on social media network

Based on the incredible breadth and speed of information distribution within social media network, and continuous development of natural language processing techniques for sentiment and topic analyses, it is potential to access online public opinions on major accidents. An integrated framework including four-stage evolution model, lexicon-based sentiment analysis, and latent Dirichlet allocation (LDA)-based topic extraction was developed based upon social media data analysis. The explosion of Xiangshui eco-chemical industrial zone (EXEIZ) was taken as a case for investigating the evolution of online public opinions. The large accident domain (LAD)-based sentiment dictionary was established for the lexicon-based approach for sentiment determination. Effective methods (i.e., combination of short microblogs and comments into a new text, and the hybrid approach integrating perplexity with principal component analysis) were deployed for overcoming two typical shortcomings (i.e., inappropriate for short text dataset, and sensitive to the number of topics within a corpus) of the LDA-based approach for topic analysis and extraction. According to the four-stage evolution model, customized strategies and suggestions were provided for guiding and controlling online public opinions on major accidents, in order to decrease their negative impacts on the society. This enabled conducting targeted communication efforts among different stakeholders for the reduction of negative sentiments such as indignation, sadness, and threat, and the avoidance of social media crises. Although a major accident poses tangible threats to the public, it may improve their awareness for preventing from these threats. In case of appropriate measures in time, the focus tends to steer toward effective and prosocial solutions. It is helpful for sustaining and re-establishing the image of authorities, enterprises or individuals that are closely associated with the major accident.

Machine Learning Based Twitter Sentiment Analysis and User Influence

The use of social media platforms, such as Twitter, has grown exponentially over the years, and it has become a valuable source of information for various fields, including marketing, politics, and finance. Sentiment analysis is particularly relevant in social media analysis. Sentiment analysis involves the use of natural language processing (NLP) techniques to automatically determine the sentiment expressed in a given text, such as positive, negative, or neutral.
 In this research paper, we focus on Twitter sentiment analysis and identify the most influential users in a given topic. We propose a methodology based on machine learning techniques to perform sentiment analysis and identify the most influential users on Twitter based on popularity. Specifically, we utilize a combination of NLP techniques, sentiment lexicons, and machine learning algorithms to classify tweets as positive, negative, or neutral. We then employ popularity calculations for each user to identify the top 10 most influential users on a given topic.
 The proposed methodology was tested on a large dataset of US airlines tweets which is related to a specific topic i.e. airlines, and the results show that the approach can effectively classify tweets according to sentiment and identify the most influential users. We evaluated the performance of several machine learning algorithms, including Multinomial Naive Bayes, Support Vector Machines (SVM), Decision Trees, Gradient Boosting, logistic regression, AdaBoost, KNN and Random Forest, and found that the logistic regression algorithm has achieved the highest accuracy.
 The proposed methodology has several implications for various fields, such as marketing, where sentiment analysis can help companies understand consumer behavior and tailor their marketing strategies accordingly. Moreover, identifying the most influential users can provide insights into opinion leaders in a given topic and help companies and policymakers target their messages more effectively.

Examining the representation of landscape and its emotional value in German-Swiss fiction between 1840 and 1940

This paper presents a quantitative analysis of the representation and affective encoding of fictional space in a corpus of 125 Swiss literary prose texts of the 19th and early 20th Century written in German, offering a contribution to both spatial and affective literary studies. Motivated by questions about the iconic dichotomy between ‘urban’ and ‘rural/natural’ space in literary works (Sengle; Fournier; Nell and Weiland) – and in Swiss literature around 1900 in particular (Rehm) – we use computational methods to detect and examine how different types of space are distributed and affectively encoded in German-Swiss literature. Taking into account the complexity of cultural perceptions and representations of space across history, we examine the presence of ‘urban’ and ‘rural/natural’ fictional spaces and their potential role in constructing a ‘Swiss’ national literature (Böhler; Zimmer), and their affective encoding. In order to do this, we first compiled a comprehensive dictionary of named and non-named spatial entities in the broad spatial categories RURAL and URBAN, and examined the presence of sentiment and emotions (valence and discrete emotions) and their ‘strength’ (arousal) in relation to these. We used current state-of-the-art sentiment lexicons for German available to the digital humanities community. Similarly to Heuser et al., we mapped the spatial entities and the sentiment lexicons onto our corpus, and focused on spans of +/-50 words around the detected entities, in order to examine the specific sentiment and emotions related to space. In an exploratory analysis, we offer here a first-time data-driven perspective on rural and urban fictional space, incorporating the dimension of affective encoding of space systematically.

Centrifugal Navigation-Based Emotion Computation Framework of Bilingual Short Texts with Emoji Symbols

Heterogeneous corpora including Chinese, English, and emoji symbols are increasing on platforms. Previous sentiment analysis models are unable to calculate emotional scores of heterogeneous corpora. They also struggle to effectively fuse emotional tendencies of these corpora with the emotional fluctuation, generating low accuracy of tendency prediction and score calculation. For these problems, this paper proposes a Centrifugal Navigation-Based Emotional Computation framework (CNEC). CNEC adopts Emotional Orientation of Related Words (EORW) to calculate scores of unknown Chinese/English words and emoji symbols. In EORW, t neighbor words of the predicted sample from one element in the short text are selected from a sentiment dictionary according to spatial distance, and related words are extracted using the emotional dominance principle from the t neighbor words. Emotional scores of related words are fused to calculate scores of the predicted sample. Furthermore, CNEC utilizes Centrifugal Navigation-Based Emotional Fusion (CNEF) to achieve the emotional fusion of heterogeneous corpora. In CNEF, how the emotional fluctuation occurs is illustrated by the trigger angle of centrifugal motion in physical theory. In light of the corresponding relationship between the trigger angle and conditions of the emotional fluctuation, the fluctuation position is determined. Lastly, emotional fusion with emotional fluctuation is carried out by a CNEF function, which considers the fluctuation position as a significant position. Experiments demonstrate that the proposed CNEC effectively computes emotional scores for bilingual short texts with emojis on the Weibo dataset collected.

Leveraging Domain Knowledge to Improve Depression Detection on Chinese Social Media

Depression is a prevalent and severe mental disorder that often goes undetected and untreated, particularly in its early stages. However, social media has emerged as a valuable resource for identifying symptoms of depression and other mental disorders as people are increasingly willing to share their experiences and emotions online. As such, social media-based depression detection has become an important area of research. Unfortunately, despite the growing number of cases in China, there are few Chinese social media-based resources for depression research. To address this gap, this article presents a dataset collected from Sina Weibo and approaches depression detection as a binary classification problem. A depression lexicon is developed based on domain knowledge of depression and the Dalian University of Technology Sentiment Lexicon (DUT-SL), which facilitates better extraction of lexical features related to depression. Then the lexical features are fused using a correlation-based metric. The effectiveness of this approach is verified using five classical machine learning methods and two boosting-based models, both on a public dataset and our dataset. Experimental results indicate that the depression domain lexicon features improve classification performance and fusing these features based on their correlations can further enhance prediction effectiveness. This study provides a method for future research in social media-based depression detection and contributes to the development of Chinese depression detection resources.

Policy Suggestions through Social Perception Analysis of Defense YouTube Channels

YouTube is a platform that delivers video content on diverse topics and enables communication, making it the most utilized service for conveying knowledge and exchanging information. The Korean Army is also proactively operating official Defense YouTube channels related to the military and producing videos to promote a positive perception and instill faith in the military among the general public and soldiers. However, there is a lack of research on the current status of the content and societal perceptions of defense-related channels. To understand societal awareness, we qualitatively analyzed video content and comments. In addition, we used big data analysis methods such as keyword derivation, term frequency analysis, word cloud visualization, word association analysis, and sentiment analysis of comments based on a Korean sentiment dictionary. These analyses identify current defense channel strategies and society's awareness of these strategies and provide a foundation for improving and perfecting future video creation and operation processes.

Evaluating the Applicability of Existing Lexicon-Based Sentiment Analysis Techniques on Family Medicine Resident Feedback Field Notes: Retrospective Cohort Study.

Field notes, a form for resident-preceptor clinical encounter feedback, are widely adopted across Canadian medical residency training programs for documenting residents' performance. This process generates a sizeable cumulative collection of feedback text, which is difficult for medical education faculty to navigate. As sentiment analysis is a subfield of text mining that can efficiently synthesize the polarity of a text collection, sentiment analysis may serve as an innovative solution. This study aimed to examine the feasibility and utility of sentiment analysis using 3 popular sentiment lexicons on medical resident field notes. We used a retrospective cohort design, curating text data from University of Toronto medical resident field notes gathered over 2 years (from July 2019 to June 2021). Lexicon-based sentiment analysis was applied using 3 standardized dictionaries, modified by removing ambiguous words as determined by a medical subject matter expert. Our modified lexicons assigned words from the text data a sentiment score, and we aggregated the word-level scores to a document-level polarity score. Agreement between dictionaries was assessed, and the document-level polarity was correlated with the overall preceptor rating of the clinical encounter under assessment. Across the 3 original dictionaries, approximately a third of labeled words in our field note corpus were deemed ambiguous and were removed to create modified dictionaries. Across the 3 modified dictionaries, the mean sentiment for the "Strengths" section of the field notes was mildly positive, while it was slightly less positive in the "Areas of Improvement" section. We observed reasonable agreement between dictionaries for sentiment scores in both field note sections. Overall, the proportion of positively labeled documents increased with the overall preceptor rating, and the proportion of negatively labeled documents decreased with the overall preceptor rating. Applying sentiment analysis to systematically analyze field notes is feasible. However, the applicability of existing lexicons is limited in the medical setting, even after the removal of ambiguous words. Limited applicability warrants the need to generate new dictionaries specific to the medical education context. Additionally, aspect-based sentiment analysis may be applied to navigate the more nuanced structure of texts when identifying sentiments. Ultimately, this will allow for more robust inferences to discover opportunities for improving resident teaching curriculums.

EMD-BiLSTM Stock Price Trend Forecasting Model based on Investor Sentiment

The movement of stock prices is the focus of investors' attention in the stock market, so stock price trend prediction has always been a hot topic in quantitative investment research. Traditional machine learning forecasting models are difficult to process nonlinear, high-frequency, high-noise stock price time series, which makes the prediction accuracy of stock price trends low. In order to improve the prediction accuracy, according to the temporal characteristics of stock price data, it is proposed to use a combination of empirical mode decomposition (EMD), investor sentiment and two-way long short-term memory neural network to predict the rise and fall of stock prices. Firstly, the empirical mode decomposition algorithm is used to extract the characteristics of the stock price time series on different time scales, and the investor sentiment indicators of the text from the close of the previous stock trading day to the opening of the next trading day are extracted by constructing a financial sentiment dictionary, and finally the EMD-BiLSTM model is used to predict the rise and fall of the next index trading day. Experiments on the dataset of stock price series show that the optimized BiLSTM model has strong predictive ability for the trend of consumer sector indexes.

A Review of Text Sentiment Analysis Methods and Applications

This paper reviews the application of natural language processing in sentiment analysis. Sentiment analysis is an important task aimed at automatically identifying and inferring sentiment tendencies and sentiment intensity in texts. This paper first introduces the application areas of sentiment analysis, including practical applications of text sentiment analysis. Then, text pre-processing techniques such as word separation, deactivation removal and punctuation processing are discussed. Then, feature extraction and representation methods are explored, including bag-of-words model, TF-IDF, word embedding and Word2Vec, attention mechanism and Transformer. In addition, methods for sentiment analysis, such as sentiment dictionaries and rule-based methods, traditional machine learning methods, and deep learning-based methods, are presented. Finally, the application areas of sentiment analysis are discussed and conclusions are given. The review in this paper will help readers understand the current status and development trend of natural language processing applications in sentiment analysis, as well as the advantages and disadvantages of different methods in sentiment analysis.

Analysis of Customer Satisfaction in Tourism Services Based on the Kano Model

Understanding customer needs is of great significance to enhance service quality and competitive advantage. However, for the tourism industry, it is still unclear how to mine service improvement strategies from tourist-generated online reviews. This paper aims to develop a data-driven approach to conduct a fine-grained dimension analysis of customer satisfaction with tourism services. First, this paper uses Latent Dirichlet Allocation to explore the key dimensions of tourist satisfaction from online reviews. Next, based on the Chinese sentiment dictionary, tourists’ emotional attitudes towards each service dimension can be identified. Then, the backpropagation neural network is used to measure the complex relationship between tourists’ sentiment orientations towards different dimensions and their satisfaction. Finally, according to the improved Kano model, multi-dimensional attribute classification is realized to support the strategic analysis of tourism service quality improvement. The proposed method is empirically verified through a real tourism review dataset. The results exhibit the theoretical and practical implications of our method.

Prediction of Alleged Stress Symptoms based on Indonesian Sentiment Lexicon using Multilayer Perceptron

Stres adalah fenomena mental atau fisik yang terbentuk melalui penilaian kognitif seseorang terhadap rangsangan dan hasil interaksi seseorang dengan lingkungan. Layaknya penyakit lainnya, stres harus segera ditangani, agar tidak mengganggu keseharian hidup seseorang. Namun, diagnosis mandiri perlu dihindari untuk mencegah terjadinya penanganan yang keliru. Metode terbaru mendeteksi stres melalui media sosial, dimana psikiater dapat mengenali gejala stres seseorang berdasarkan postingan yang terus-menerus di media sosial. Penelitian ini menggunakan dataset dari Twitter, dengan kuesioner DASS-42, model algoritma Multilayer Perceptron dan Indonesian Sentiment Lexicon. Analisis terhadap tweet dapat membentuk model prediktif yang diterapkan untuk mendeteksi sentimen serupa di tweet lain. Percobaan dua kasus uji, yaitu dengan parameter Adam solver menghasilkan akurasi 86%, sedangkan dengan parameter SGD solver menghasilkan akurasi 72%. Hal itu dikarenakan Adam solver bekerja lebih baik dari segi waktu pelatihan dan skor validasi pada kumpulan data yang relatif besar

Count Me Too: Sentiment Analysis of Roman Sindhi Script

Social media has given voice to people around the globe. However, all voices are not counted due to the scarcity of lexical computational resources. Such resources could harness the torrent of social media text data. Computational resources for rich languages such as English are available. More are being developed, meanwhile strengthening and enhancing the current ones. However, Roman Sindhi, a resource-poor writing style, is a phonetically rich language lacking computational resources, creating a working space for researchers. This work attempts to develop lexical sentiment resources that will help calculate the public opinion expressed in Roman Sindhi and bring their point of view into the limelight. This work is one of the initial efforts to develop lexical Roman Sindhi sentiment dictionary resources to help detect sentiment orientation in a text. Furthermore, it also developed two interfaces to leverage the lexical resources—a Roman Sindhi to English translator (RoSET) that translates a Roman Sindhi feature into an equivalent English word and a Roman Sindhi rule-based sentiment scorer ( RBRS3) that assigns sentiment score to a Roman Sindhi script features. The results obtained from the developed system accommodated the bilingual dataset (Roman Sindhi + English) more adequately. An increase of 20.8% was recorded for positive sentence detection, and a 16% increase was obtained for negative sentences, whereas neutral sentences were marginalized to a lower number (59.31% decrease). The resultant system makes those public voices expressed in the Roman Sindhi script get counted, which otherwise are in vain.

글로벌 금융시대 한국 주식시장의 애널리스트 허딩 행태에 따른 텍스트 정보력 연구

Recently, the investment information is spreading in near real-time due to the global integration of the international financial market. At this point, it is essential to resolve the information asymmetry in the capital market to vitalize domestic and foreign investment. Analysts improve market efficiency, by performing information intermediary role between the capital market and investors. However, the herding behavior among analysts in the market can weaken the information quality and lead to inefficiency in the financial market. In this study, the relationship between analyst herding behavior and the text of the report is analyzed. In addition, the effect of the text of the report body on the information power according to the analyst's herding behavior was verified. To this end, the positive and negative emotions shown in the body of the analyst report were extracted as words of up to 2-gram units and merged with the list of positive and negative words of Loughran and McDonald(2011), a sentiment dictionary widely used in the financial field. As a result of the analysis, when an analyst presents a 'bold' opinion that deviates from the consensus, the sentiment of the report body is more extreme than that of an ‘herd’ opinion. In addition, it was confirmed that positive emotion weakens the report information power according to the analyst's herding behavior. This study is expected to alleviate information asymmetry between investors and the financial market. Furthermore, the text mining methodology used in this study can be readily applied to any other kind of text, including News, Social Network Services, or IR reports.

Fusing sentiment knowledge and inter-aspect dependency based on gated mechanism for aspect-level sentiment classification

Aspect level sentiment classification is a fine-grained sentiment analysis task that aims to identify the sentiment polarity of one or more given aspects in a sentence. In natural language, words frequently carry certain sentimental tendencies, which can be beneficial in obtaining the features between aspects and contexts. On the other hand, the dependencies between different aspects in a sentence can provide sufficient information for the sentiment polarity discrimination of a target aspect. However, existing models tend to focus on sentiment knowledge or aspect interactions individually without leveraging their converged information. Therefore, we propose a model based on Gated Mechanism Fusing Sentiment Knowledge and Inter-Aspect dependency (GMF-SKIA) for Aspect-level Sentiment Classification in this paper, aiming to dynamically fuse sentiment knowledge information of words and inter-aspect dependency. Specifically, the model uses the SenticNet sentiment dictionary to add sentiment knowledge information to words during dependency tree construction, and then we introduce a graph convolutional network to obtain sentiment information of dependency tree. We utilize an aspect-related multiheaded self-attention mechanism to model the inter-aspect interactions. Moreover, we design an information gate based on gated mechanism to fuse sentiment knowledge and inter-aspect features. We performed experiments on four publicly available datasets, our model outperforms the best benchmark model by an average of 2.1% and achieves the highest accuracy of 91.56% on the Rest16 dataset.

The relative importance of overnight sentiment versus trading-hour sentiment in volatility forecasting

We examine the relative importance of overnight sentiment versus trading-hour sentiment in forecasting volatility. Previous studies on investor sentiment either ignore overnight sentiment or aggregate overnight sentiment with trading-hour sentiment. With the help of Chinese sentiment dictionary, we extract investor sentiment from Chinese internet social forums. Our empirical analyses suggest conclusively that investor sentiment significantly affects volatility. In particular, overnight sentiment is more informative than trading-hour sentiment in forecasting volatility, and has higher predictive power than overnight returns, which are widely used to capture overnight information. Our results hold in a series of robustness tests, including in highly volatile subsample, alternative rolling window size, and alternative sentiment proxy.

Using Twitter to Gauge Customer Satisfaction Response to a Major Transit Service Change in Calgary, Canada

Measuring public opinion about the quality of transit services is a key factor in understanding and addressing customer dissatisfaction and increasing customer loyalty and ridership. Sentiment analysis using social media—in particular Twitter—is a relatively cheap and potentially powerful complement to traditional survey methods, which are expensive and limited in sample size. This study aims to evaluate customer response to the introduction of Calgary Transit’s MAX routes. We compared customer satisfaction expressed on Twitter with measured service reliability in the form of on-time performance. We also employed a qualitative research approach using content analysis from Twitter to gauge rider satisfaction over several service attributes before and after the service change. A transit-specific sentiment lexicon was developed to support this study using a hybrid approach. This lexicon outperformed generic sentiment lexicons traditionally used in transit studies with regard to both accuracy (18.4%) and F1-score (7.1%). We found that the overall perception of on-time performance from riders using Twitter was similar to the actual performance in the field. This was also observed for one individual route on which stops with poor schedule adherence were linked with negative feedback. This study concludes that combining customer-oriented measures from Twitter with operational-oriented ones would enable transit agencies to make better-informed decisions for planning and operational purposes.

Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method.

Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. In this paper, we propose a new model for the topic discovery analysis of literature. Firstly, the FastText model is applied to calculate the word vector of literature keywords, based on which cosine similarity is applied to calculate keyword similarity, to carry out the merging of synonymous keywords. Secondly, the hierarchical clustering method based on the Jaccard coefficient is used to cluster the domain literature and count the literature volume of each topic. Thirdly, the information gain method is applied to extract the high information gain characteristic words of various topics, based on which the connotation of each topic is condensed. Finally, by conducting a time series analysis of the literature, a four-quadrant matrix of topic distribution is constructed to compare the research trends of each topic within different stages. The 1186 articles in the field of text sentiment analysis from 2012 to 2022 can be divided into 12 categories. By comparing and analyzing the topic distribution matrices of the two phases of 2012 to 2016 and 2017 to 2022, it is found that the various categories of topics have obvious research development changes in different phases. The results show that: ① Among the 12 categories, online opinion analysis of social media comments represented by microblogs is one of the current hot topics. ② The integration and application of methods such as sentiment lexicon, traditional machine learning and deep learning should be enhanced. ③ Semantic disambiguation of aspect-level sentiment analysis is one of the current difficult problems this field faces. ④ Research on multimodal sentiment analysis and cross-modal sentiment analysis should be promoted.