Subjective Sentiment Research Articles

Explainable recommender system directed by reconstructed explanatory factors and multi‐modal matrix factorization

SummaryMatrix factorization (MF)‐based recommender systems (RSs) as black‐box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi‐modal matrix factorization (ERS‐REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi‐modal matrix, ERS‐REFMMF utilizes the Funk‐singular value decomposition method at the foundational layer to decompose the multi‐modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi‐objective optimization algorithms. ERS‐REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many‐objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods.

Deep learning-based sentiment analysis for social media: A focus on multimodal and aspect-based approaches

Commonly referred to as opinion mining, sentiment analysis harnesses the power of deep learning systems to discern human emotions and subjective sentiments towards a wide array of subjects. As such, it has become an integral tool in identifying and distinguishing sentences that harbor emotional biases or trends. By systematically examining sentiment-tinged data, researchers can unearth pivotal insights that not only reflect current perspectives but also predict future behaviors and trends. This process involves intricate computational models that analyze and interpret the emotional undertones embedded within a body of text. Whether these undertones are positive, negative, or neutral, sentiment analysis allows us to delve into the subtle nuances of human communication. This ability to "understand" and quantify sentiment is particularly vital in our modern digital age, where opinions and reviews shared through social media and online platforms can greatly influence public sentiment and consumer behavior. By extending beyond the literal meanings of words and phrases, sentiment analysis can provide a more comprehensive understanding of how people truly feel. It is instrumental in fields as diverse as marketing, politics, social science, and even artificial intelligence development, given its potential to gauge public opinion and predict societal trends. This paper aims to consolidate relevant research within the field of sentiment analysis conducted in recent years. Furthermore, it seeks to prognosticate the future trajectories and impacts of this rapidly evolving domain. Emphasis is placed on the role of deep learning and its transformational effects on the approach and capabilities of sentiment analysis, anticipating how its further advancement will continue to refine this intricate process of emotion recognition and interpretation.

The impact of contagion effects of media reports, investors’ sentiment and attention on the stock market based on HAR-RV model

In this paper, the Shanghai Securities Composite Index and 18 A-share listed companies are used to justify the impact of contagion effects of media reports, investors’ sentiment and attention on stock market. Five indicators are built: The news media optimistic tendency, investors’ attention, investors’ sentiment, investors’ sentiment disagreement and media sentiment disagreement. Furthermore, theoretical models are constructed based on HAR-RV model to analyze the contagion structure between media sentiment and investors’ sentiment and its impact on the performance of stock market. Additionally, the “reverse silence spiral” theory is proposed to analyze the regulatory role of sentiment disagreement in the contagion effects according to the information communication theory. The empirical results demonstrate the following conclusions. (1) The optimism degree of media reports positively affects investors’ subjective sentiment and increases their transaction volume. (2) Strengthening investors’ attention to corporate-related information is the main path by which media sentiment interferes with investors’ sentiment. (3) Media sentiment will indirectly affect the excess return and volatility of stocks through investors’ sentiment and their transactions. (4) Media sentiment disagreement has weakened the influence of media sentiment on investors’ attention and sentiment. Investors’ sentiment disagreement has alleviated its impact on the excess returns and volatility of stocks.

Does the built environment of settlements affect our sentiments? A multi-level and non-linear analysis of Xiamen, China, using social media data.

Humans spend most of their time in settlements, and the built environment of settlements may affect the residents' sentiments. Research in this field is interdisciplinary, integrating urban planning and public health. However, it has been limited by the difficulty of quantifying subjective sentiments and the small sample size. This study uses 147,613 Weibo text check-ins in Xiamen from 2017 to quantify residents' sentiments in 1,096 neighborhoods in the city. A multilevel regression model and gradient boosting decision tree (GBDT) model are used to investigate the multilevel and nonlinear effects of the built environment of neighborhoods and subdistricts on residents' sentiments. The results show the following: (1) The multilevel regression model indicates that at the neighborhood level, a high land value, low plot ratio, low population density, and neighborhoods close to water are more likely to improve the residents' sentiments. At the subdistrict level, more green space and commercial land, less industry, higher building density and road density, and a smaller migrant population are more likely to promote positive sentiments. Approximately 19% of the total variance in the sentiments occurred among subdistricts. (2) The proportion of green space and commercial land, and the density of buildings and roads are linearly correlated with residents' sentiments. The land value is a basic need and exhibits a nonlinear correlation with sentiments. The plot ratio, population density, and the proportions of industrial land and the migrant population are advanced needs and are nonlinearly correlated with sentiments. The quantitative analysis of sentiments enables setting a threshold of the influence of the built environment on residents' sentiments in neighborhoods and surrounding areas. Our results provide data support for urban planning and implementing targeted measures to improve the living environment of residents.

Coping Strategies To Promote Self Esteem In Alcoholism

The intake of alcoholic beverages is a huge social issue. Worldwide, an estimated 2.3 billion individuals consume alcohol, with an average daily consumption of 33 grammes of pure alcohol. Self-esteem is a measure of one’s self-awareness and sense of personal worth. The process of behaving and then thinking about acts, as well as how others interpret actions, shapes one’s self-perception. Self-esteem refers to how we feel about ourselves and how much we regard ourselves. Alcoholism has far-reaching consequences and might result in mental health issues. One of the most typical effects found in alcoholics is the development of poor self-esteem. This term refers to a person’s total subjective sentiments of personal worth and value. It has a wide range of effects. It has an impact on a variety aspect of life, including substance abuse and recovery. The majority of alcoholics have a low sense of self-esteem. They have struggled to cope with their lives and frequently have significant emotional, family, social, and occupational issues, as well as financial, legal, and sexual issues. Coping is frequently studied in terms of its potential to reduce negative consequences. Coping skills can also help you achieve more favourable results.

Aspect-level sentiment analysis: A survey of graph convolutional network methods

Aspect-level sentiment analysis (ALSA) is the process of collecting, processing, analyzing, inferring, and synthesizing subjective sentiments of entities contained in texts at the aspect level. The development of social networks has been driven by the on-going appearance of vast numbers of short documents, such as those in which opinions are expressed and comments are made. The text in these documents reflects users’ emotions related to entities. The ALSA of these short texts plays an important role in solving various problems in life. Particularly in e-commerce, manufacturers can use sentiment analysis to determine users’ orientations, adapt their products to perfection, identify potential users, and pinpoint users that influence other users. Therefore, improving the performance of ALSA methods has recently attracted the interest of researchers. Currently, four main types of ALSA methods are available: knowledge-based, machine learning-based, hybrid-based, and most recently, graph convolutional network (GCN)-based. This study is the first survey to focus on reviewing the proposed methods for ALSA using GCN methods. In this paper, we propose a novel taxonomy to divide GCN-based ALSA models into three categories based on the types of knowledge extraction. We present and compare GCN-based ALSA methods following our taxonomy comprehensively. Common benchmark datasets and text representations that are often used in GCN-based methods are also discussed. In addition, we discuss five challenges and suggest seven future research directions for GCN-based ALSA methods. The findings of our survey are expected to provide the necessary guidelines for beginners, practitioners, and new researchers to improve the performance of ALSA methods.

Sentiment analysis with covariate-assisted word embeddings

Sentiment analysis measures inclination of textual documents, aiming to extract and quantify their subjective sentiment polarity. In literature, most sentiment analysis methods first numericalize textual documents through certain word embeddings framework, and then formulate sentiment analysis as an ordinal regression or classification task. Yet it is often ignored that different people may have different preference of wording, and thus a uniform word embeddings often leads to suboptimal performance. In this article, to accommodate the heterogeneity among individual persons, we propose a covariate-assisted word embeddings in a margin-based ordinal regression framework, where covariates are incorporated through scaling factors to adjust the word embeddings. Moreover, we employ a block-wise coordinate descent scheme to tackle the resultant large-scale optimization task, and establish theoretical results to quantify the asymptotic behavior of the proposed method, guaranteeing its fast convergence rate in terms of prediction accuracy. Finally, we demonstrate the advantages of the proposed method over its competitors in both the Yelp Challenge dataset and synthetic datasets.

RETRACTED ARTICLE: Sentimental analysis of transliterated text in Malayalam using recurrent neural networks

Usage of mobile phones, access to internet in the fingertips and increasing number of mobile applications has accelerated the generation of online content. Freedom of expression has waived the barriers of online interaction. Curiosity in knowing others viewpoint through their reviews in each and everything starting from a purchase of product to watching a movie has become a common scenario. Decision on success and failure of one’s business is in the hands of public now. Humans are always comfortable, sticking to their native Language, when it comes to expressions whether it is interest, emotions, feeling or opinion. Usage of Natural Language and the trend to analyze the subjective sentiments is increasing day by day. Transliterated text of a language is the English version of spoken native language. For example, Malayalam in English we call as Manglish. Transliterated text has become the language of social media websites like WhatsApp, Facebook, Twitter. It’s a kind of boon to the young generation who know to speak their native language but not to read or write in its own nominal scripts. In this paper we consider the Sentimental Analysis of Transliterated text. RNN-LSTM technique is used to derive the sentiments of transliterated text.

The impact of political party/candidate on the election results from a sentiment analysis perspective using #AnambraDecides2017 tweets

This work investigates empirically the impact of political party control over its candidates or vice versa on winning an election using a natural language processing technique called sentiment analysis (SA). To do this, a set of 7430 tweets bearing or related to #AnambraDecides2017 was streamed during the November 18, 2017, Anambra State gubernatorial election. These are Twitter discussions on the top five political parties and their candidates termed political actors in this paper. We conduct polarity and subjectivity sentiment analyses on all the tweets considering time as a useful dimension of SA. Furthermore, we use the word frequency to find words most associated with the political actors in a given time. We find most talked about topics using a topic modeling algorithm and how the computed sentiments and most frequent words are related to the topics per political actor. Among other things, we deduced from the experimental results that even though a political party serves as a platform that sales the personality of a candidate, the acceptance of the candidate/party adds to the winning of an election. For example, we found the winner of the election Willie Obiano benefiting from the values his party share among the people of the State. Associating his name with his party, All Progressive Grand Alliance (APGA) displays more positive sentiments and the subjective sentiment analysis indicates that Twitter users mentioning APGA are less emotionally subjective in their tweets than the other parties.

Subjective Sentiment Analysis for Arabic Newswire Comments

This paper presents an approach based on supervised machine learning methods to discriminate between positive, negative and neutral Arabic reviews in online newswire. The corpus is labeled for subjectivity and sentiment analysis (SSA) at the sentence-level. The model uses both count and TF-IDF representations and apply six machine learning algorithms; Multinomial Naive Bayes, Support Vector Machines (SVM), Random Forest, Logistic Regression, Multi-layer perceptron and k-nearest neighbors using uni-grams, bi-grams features. With the goal of extracting users sentiment from written text. Experimental results showed that n-gram features could substantially improve performance; and showed that the Multinomial Naive Bayes approach is the most accurate in predicting topic polarity. Best results were achieved using count vectors trained by combination of word-based uni-grams and bi-grams with an overall accuracy of 85.57% over two classes and 65.64% over three classes.

An Enhanced Technique for Analyzing Sentiments of Public Reviews - II

Enhnaced Dual Sentiment Analysis (EDSA) is an improved system which enhances the performance of the existing Dual Sentiment Analysis (DSA) which is implemented in literature. It mainly focuses on improving the efficiency of the existing system by making some modifications to the existing DSA approach. EDSA improves the classification accuracy of the public reviews. Apart from the classification accuracy other parameters considered in EDSA are precision, recall and fmeasure. In the first phase, a data pre-processing is performed to clean the data where subjectivity analysis is performed to obtain the subjective reviews and sentiment analysis is performed on subjective reviews only. Second phase deals with negation detection and sentiment word sreversal to obtain the reversed reviews. Third phase performs polarity calculation on the original and reversed reviews to obtain positive and negative reviews based on sentiment score of the reviews. Fourth phase performs the enhanced dual training and prediction where the positive and negative reviews are provided to various classifiers which provides the final results as the output. Final phase is the graphical representation of the various parameter values obtained from the previous phase which helps in comparing the results of the various classifiers.

Paintings of Flower, Vegetable, and Fruit from the Late Joseon Period: Focusing on Sim Sajeong and Gang Sehwang

This essay explores new aspects of paintings of flower, vegetable, and fruit that Sim Sajeong (1707~1769), Gang Sehwang (1713~1791), and their artistic circle escalated into one of the major pictorial genres in the late Joseon dynasty. Falling into a conventional category Bird-and-Flower painting, this branch of painting has hitherto been studied only in the context of an individual artist’s development without receiving a proper recognition as a sign of a new pictorial trend of the period. The rise of the novel genre and its characteristics will shed a new light on the eighteenthcentury painting. The late Joseon period witnessed growing interests in pragmatic horticultural knowledge beyond a tradition of associating plants with idealistic virtues or subjective sentiments. Also a cultural phenomenon of appreciating and favoring flowers and trees has developed into extreme aestheticism and an extravagant pastime. Gardening books and related materials from the Ming and Qing dynasties were widely read, including painting manuals that inspired Kim Ingwan’s (act. fl. early 18th century) Landscape, Fish and Crab, Flower and Insects. The artistic currents, which Sim and Gang channeled into new heights, feature an increase of a single plant motif, the growth of paintings combining flowers and garden rocks, and the emergence of new subject-matters (e.g. begonias, rose mallows, lilies, hydrangeas, red plum blossoms, and narcissi). In addition, new pictorial modes settled, the kernels of which lie in the Idealistic inkmonochrome method or color-washed boneless manner. As such it made solid its status as a subject of privileged scholar-artist paintings. Although Sim and Gang display pictorial modes and subjects in common, their styles differ according to their social standings (Sim as a semi-professional literati painter, while Gang an amateur scholar painter). Sim sought rich and animated brushworks, a sophisticated composition inviting birds and worms against a garden background, and the picturesque. On the contrary Gang liked to employ somewhat monotonous ink tonalities, calligraphic strokes, concise structures, and blandness. Sim’s combined application of light-color washes and vivid paints contrasts with Gang’s nearly exclusive use of limpid color washes. Whereas Sim had an eclectic tendency to derive visual constituents from various painting manuals, Gang deliberately pursued the flatness of the prints. Those who shared subjects, modes, and styles with Sim and Gang are Choe Buk (1712~ca. 1786), Jeong Chungyeop (1725~after 1800), Ryu Sin (1748~1790), Jang Siheung (act. late 18th century), Kim Deokhyeong (act. late 18th~early 19th century), and Yi Bang-un (1761~after 1815). Their individual styles and brushworks based on sketching from life contributed not only to the proliferation of paintings of flower, vegetable, and fruit but also to the enrichment of late Joseon art world.

Products Ranking Through Aspect-Based Sentiment Analysis of Online Heterogeneous Reviews

With the rapid growth of online shopping platforms, more and more customers intend to share their shopping experience and product reviews on the Internet. Both large quantity and various forms of online reviews bring difficulties for potential consumers to summary all the heterogenous reviews for reference. This paper proposes a new ranking method through online reviews based on different aspects of the alternative products, which combines both objective and subjective sentiment values. Firstly, weights of these aspects are determined with LDA topic model to calculate the objective sentiment value of the product. During this process, the realistic meaning of each aspect is also summarized. Then, consumers’ personalized preferences are taken into consideration while calculating total scores of alternative products. Meanwhile, comparative superiority between every two products also contributes to their final scores. Therefore, a directed graph model is constructed and the final score of each product is computed by improved PageRank algorithm. Finally, a case study is given to illustrate the feasibility and effectiveness of the proposed method. The result demonstrates that while considering only objective sentiment values of the product, the ranking result obtained by our proposed method has a strong correlation with the actual sales orders. On the other hand, if consumers express subjective preferences towards a certain aspect, the final ranking is also consistent with the actual performance of alternative products. It provides a new research idea for online customer review mining and personalized recommendation.

Bias‐Sentiment‐Topic model for microblog sentiment analysis

SummaryUnified models of sentiment and topic have been widely employed in unsupervised sentiment analysis, where each word in text carries both sentiment and topic information. In fact, however, some words tend to express objective things while others prefer to express subjective sentiments. Based on this observation, the concept of word bias is put forward firstly, including objective bias and subjective bias. Considering the relations of bias, sentiment, and topic, a unified framework named Bias‐Sentiment‐Topic (BST) model is then presented to jointly model them for microblog sentiment analysis. After that, an improved Gibbs sampler is proposed for the inference of BST by introducing the general Pólya urn model, which incorporates word embedding as the general knowledge. Finally, experiments on standard test datasets illustrate major improvements of BST in sentiment classification and its effectiveness in separation of words with different biases.

An Annotated Huge Dataset for Standard and Colloquial Arabic Reviews for Subjective Sentiment Analysis

Sentiment analysis is getting increasingly popular as it facilitates gaining an indication of the wider public opinions or attitudes towards certain products, services, articles, etc. Many researchers have shown considerable interest in this field. Most of these studies have focused on English and other Indo-European languages. Very few studies have addressed the problem for the Arabic language. This is, mostly, due to the rare or nonexistent huge and free Arabic datasets that contains both Modern Standard Arabic (MSA) as well as Dialectal Arabic (DA). Generally, one of the main challenges for developing robust sentiment analysis systems is the availability of such large-scale datasets. Such datasets exist in abundance for English language, while it is not the case for a low-resource language such as the Arabic language. Recently, there have been some efforts for providing relatively large-scale Arabic datasets dedicated for sentiment analysis such as LABR and most recently BRAD 1.0, which is considered as the largest Arabic Book Reviews dataset for sentiment analysis and machine learning applications. In this work, we present BRAD 2.0, an extension to BRAD 1.0 with more than 200K extra records to account for several Arabic dialects. BRAD 2.0 has a total number of 692586 annotated reviews; each represents a single review along with the reviewer’s rating ranging from 1 to 5 of a certain book. The most interesting property of BRAD 2.0 is that it combines both MSA and DA. To verify and validate the proposed dataset, we implement several state-of-the-art supervised and unsupervised classifiers to categorize book reviews. For the unsupervised classifiers, we implemented several models of CNN and RNN classifiers utilizing GloVe-based word embeddings. Although all classifiers performed well, the highest accuracies attained are between 90% and 91%. Experimental results show that BRAD 2.0 is rich and robust. Our key contribution is to make this benchmark-dataset available and accessible to promote further research in the field of Arabic computational linguistic.

Are income poverty and perceptions of financial difficulties dynamically interrelated?

An individual’s economic ill fare can be assessed both objectively, looking at one’s income with reference to a poverty line, or subjectively, on the basis of the individual’s perceived experience of financial difficulties. Although these are distinct perspectives, income poverty and perceptions of financial difficulties are likely to be interrelated. Low income (especially if it persists) is likely to negatively affect perceptions of financial difficulties and, as recently suggested by the behavioural economics literature, (past) subjective sentiment may in return influence individual’s income generating ability and poverty status. The aim of this paper is to determine the extent of these dynamic cross-effects between both processes. Using Luxembourg survey data, our main result highlights the existence of a feedback effect from past perceived financial difficulties on current income poverty suggesting that subjective perceptions can have objective effects on an individual’s behaviour and outcomes.

The False Idol of Beauty: Bl. John Henry Newman's Critiques of Aesthetics and the Challenge of Evangelization

The False Idol of BeautyBl. John Henry Newman's Critiques of Aesthetics and the Challenge of Evangelization Matthew Muller (bio) Charles Taylor has shown how, in the romantic period, there arose a need to discover and express, on an individualistic basis, one's authentic human life. For the romantics, "nature as source" meant finding meaning and identity within the individual, and the most authentic experience of nature was the experience of sentiment. As Taylor notes, the romantics gave "a central and positive place to sentiments in the moral life," such that they took "the centrality of sentiment to unheard-of lengths."1 The need to construct an identity from the inner source of nature required attentiveness to one's sentiments or emotions. "Fulfilling my nature means espousing the inner élan, the voice or impulse," and this "expressive view of human life went along naturally with a new understanding of art."2 Throughout the late eighteenth and into the nineteenth century, continuing still today, argues Taylor, is a dissolution of "the distinction between the ethical and the aesthetic."3 The definition of aesthetics likewise changed in this time, so that, "along with a new understanding of natural and artistic beauty," aesthetics "focused less on the nature of the object and more on the quality of the experience evoked."4 If we accept Taylor's narrative and description of the modern person's [End Page 54] understanding of beauty, art, and aesthetics, then there are serious challenges to the possibility that beauty can serve as a useful source of evangelization. If the experience of beauty is merely about my experience of sentiment, and the creation of the beautiful is about the manifestation of individualistic and subjective sentiments, then it need not draw a person towards wonder, questioning, or search. In this article, I argue that Bl. John Henry Newman came to view aesthetics in the way that Taylor describes, but that he also recognized the pitfalls of art as sentiment in cultivating Christian faith, virtue, and devotion. I argue that Newman understood artistic production, most often spoken of in relation to poetry, as the expression of sentiment, or the release of sentiment. Since the goal of artistic production was the communication of sentiment, the experience of sentiment was likewise the primary purpose and effect of viewing art. This led Newman to be very cautious about the experience of beauty as a means of evangelization, or, to speak more in his terms, as a means for growth in virtue and religious duty and devotion. To show this, I would like to give a brief account of what we might call Newman's aesthetic theory. Of particular importance is Newman's assertion that beauty is self-sufficient, in that it does not point beyond itself, which is consistent with Taylor's account of expressivism. This then, as I will argue, has a formative impact on the imagination, so that depending on the perspective one brings to his or her experience of beauty, the imagination may be prejudiced against Christian faith. In this sense, taste or aesthetics becomes the standard for truth or goodness. In other words, something might be considered good or evil, true or false, based on the criteria of emotional experience caused by the perception of beauty in the object, action, or event. Newman's concern for the effects of beauty on an individual's religious commitments still hold today if art, in its production, but more importantly, in its reception (the way it is viewed) is about merely the experience of sentiments. The challenge is increased today, when a whole range of experiences of beauty are available through print and social media. [End Page 55] The first point to note is that Newman and his fellow Tractarians received from the romantic movement the conception of art as the expression of personal sentiment. One of the main sources of Newman's aesthetic theory was John Keble, whom Newman called "the true and primary author" of the Oxford Movement.5 Keble published a volume of poetry, The Christian Year, in 1827, which gave the Oxford Movement its spiritual ethos.6 Over a decade earlier, however, Keble had already articulated what...

A PLS Approach to Measuring Investor Sentiment in Chinese Stock Market

We select five objective sentiment indicators and one subjective sentiment indicator to build investor sentiment composite index in Chinese stock market by using the partial least squares. The reason why we do that is to improve the shortcomings of the principal component analysis, which was adopted to build investor sentiment composite index in the pioneering research. Moreover, due to the large proportion of individual investors in Chinese stock market and the rapid change of investor sentiment, we innovatively use the weekly data with smaller information granularity and higher frequency. Through empirical tests for its reasonability and market’s predictive capability, we find that this index appears to fit the data better and improves prediction.

Are Income Poverty and Perceptions of Financial Difficulties Dynamically Interrelated?

An individual's economic ill fare can be assessed both objectively, looking at one's income with reference to a poverty line, or subjectively on the basis of the individual's perceived experience of financial difficulties. Although these are distinct perspectives, income poverty and perceptions of financial difficulties are likely to be interrelated: low income (especially if it persists) is likely to negatively affect perceptions of financial difficulties and, as recently suggested by the behavioral economics literature, (past) subjective sentiment may in return influence individual's income generating ability and poverty status. The aim of this paper is to determine the extent of these dynamic cross-effects between both processes. Using Luxembourg survey data, our main result highlights the existence of a feedback effect from past perceived financial difficulties on current income poverty suggesting that subjective perceptions can have objective effects on an individual's behaviour and outcomes.

Option Pricing Anomalies in Times of Financial Distress

Classical finance theory asserts that option prices should simultaneously reflect equilibrium market expectations about the underlying asset and conform to the no-arbitrage requirements of put-call parity. We explore whether these requirements hold during times of financial distress by investigating S&P 500 index option prices during the stock market crash of 2008-2009. We show that during this period, homogeneous expectations for put and call option markets violate put-call parity. The illusion of market segmentation, however, is a spurious byproduct of put-call parity restoration. We also find that market homogeneity and put-call parity are mutually compatible in a theoretically frictionless market absent subjective investor sentiment.