A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis.

If the words of natural human language possess a universal positivity bias, as assumed by Boucher and Osgood’s (1969) famous Pollyanna hypothesis and computationally confirmed for large text corpora in several languages (Dodds et al., 2015), then children and youth literature (CYL) should also show a Pollyanna effect. Here we tested this prediction applying a vector space model- based sentiment analysis tool called SentiArt (Jacobs, 2019) to two CYL corpora, one in English (372 books) and one in German (...) (500 books). Pitching our analysis at the sentence level, and assessing semantic as well as lexico-grammatical information, bBoth corpora show the Pollyanna effect and thus add further evidence to the universality hypothesis. The results of our multivariate sentiment analyses provide interesting testable predictions for future scientific studies of literature. (shrink)

Sentiment analysis is the field of natural language processing to analyze opinionated data, for the purpose of decision making. An opinion is a statement about a subject which expresses the sentiments as well as the emotions of the opinion makers on the topic. In this paper, we develop a sentiment analysis tool namely SENTI-METER. This tool estimates the success rate of social campaigns based on the algorithms we developed that analyze the sentiment of word as (...) well as blog. Social campaigns have a huge impact on the mindset of people. One such campaign was launched in India on October 2, 2014, named Swachh Bharat Abhiyan. Our tool computes an elaborated analysis of Swachh Bharat Abhiyan, which examines the success rate of this social campaign. Here, we performed the location-wise analysis of the campaign and predict the degree of polarity of tweets along with the monthly and weekly analysis of the tweets. The experiments were conducted in five phases namely extraction and preprocessing of tweets, tokenization, sentiment evaluation of a line, sentiment evaluation of a blog and analysis. Our tool is also capable of handling transliterated words. Unbiased tweets were extracted from Twitter related to this specific campaign, and on comparing with manual tagging we were able to achieve 84.47 % accuracy using unigram machine learning approach. This approach helps the government to implement the social campaigns effectively for the betterment of the society. (shrink)

This article asks what the emerging computational field of sentiment analysis can teach us about the sentimental novel, and vice versa. It argues that, despite humanistic skepticism about quantitative methods and sentiment analysis’s well-known limitations (in recognizing irony, for example), sentiment analysis can help us better to understand the novel form and the sentimental novel in particular. The literary approach to computational analysis taken in this article demonstrates the ability of sentiment (...) class='Hi'>analysis to link large-scale observations about text data to small-scale features of individual texts and reveals that the sentimental novel itself already constitutes an analytical tradition. (shrink)

We investigated the pairing of a financial news article prediction system, AZFinText, with sentiment analysis techniques. From our comparisons we found that news articles of a subjective nature were easier to predict in both price direction (59.0% vs 50.4% without sentiment) and through a simple trading engine (3.30% return vs 2.41% without sentiment). Looking into sentiment further, we found that news articles of a negative sentiment were easiest to predict in both price direction (50.9% (...) vs 50.4% without sentiment) and our simple trading engine (3.04% return vs 2.41% without sentiment). Investigating the negative sentiment further, we found that AZFinText was best able to predict price decreases in articles of a positive sentiment (53.5%) and price increases in articles of a negative or neutral sentiment (52.4% and 49.5% respectively). (shrink)

The annotated dataset is an essential requirement to develop an artificial intelligence system effectively and expect the generalization of the predictive models and to avoid overfitting. Lack of the training data is a big barrier so that AI systems can broaden in several domains which have no or missing training data. Building these datasets is a tedious and expensive task and depends on the domains and languages. This is especially a big challenge for low-resource languages. In this paper, we experiment (...) and evaluate many various approaches on sentiment analysis problems so that they can still obtain high performances under limited training data. This paper uses the preprocessing techniques to clean and normalize the data and generate the new samples from the limited training dataset based on many text augmentation techniques such as lexicon substitution, sentence shuffling, back translation, syntax-tree transformation, and embedding mixup. Several experiments have been performed for both well-known machine learning-based classifiers and deep learning models. We compare, analyze, and evaluate the results to indicate the advantage and disadvantage points of the techniques for each approach. The experimental results show that the data augmentation techniques enhance the accuracy of the predictive models; this promises that smart systems can be applied widely in several domains under limited training data. (shrink)

The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, (...) the National Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier. From a practical perspective, the method could be generalized to other topical domains, such as public health monitoring and crisis management. It would help public health officials identify the progression and peaks of concerns for a disease in space and time, which enables the implementation of appropriate preventive actions to mitigate these diseases. (shrink)

The provision of services by companies in specific domains requires continuous commitment and a constructive approach to meet customer requirements, however, it is often challenging to determine the level of customer satisfaction without their feedback. Therefore, this paper attempts to provide a solution to this problem by using comments from social networks and evaluating their sentiment using the VADER model. In order to accomplish the aim of our research, a lexicon has been built with more than 9500 adjectives and (...) verbs from the Albanian language based on VADER which is just in the initial form and the sentiments are evaluated as positive, neutral, or negative. The lexicon was constructed for the Albanian language and two companies of the Republic of Kosovo were researched as case studies. Furthermore, the sentiment estimation, using the VADER model, in case of our datasets, we obtained a high accuracy, approximately between 89% and 95%. This level of accuracy has been primarily attributed to the application of all preprocessing steps within the dataset, which significantly enhances the model’s performance. (shrink)

Mining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. (...) After precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately. (shrink)

This paper addresses the problem of Twitter sentiment analysis through a hybrid approach in which SentiWordNet (SWN)-based feature vector acts as input to the classification model Support Vector Machine. Our main focus is to handle lexical modifier negation during SWN score calculation for the improvement of classification performance. Thus, we present naive and novel shift approach in which negation acts as both sentiment-bearing word and modifier, and then we shift the score of words from SWN based on (...) their contextual semantic, inferred from neighbouring words. Additionally, we augment negation accounting procedure with a few heuristics for handling the cases in which negation presence does not necessarily mean negation. Experimental results show that the contextual-based SWN feature vector obtained through shift polarity approach alone led to an improved Twitter sentiment analysis system that outperforms the traditional reverse polarity approach by 2–6%. We validate the effectiveness of our hybrid approach considering negation on benchmark Twitter corpus from SemEval-2013 Task 2 competition. (shrink)

The COVID-19 pandemic has created an urgent need for volunteers to complement overwhelmed public health systems. This study aims to explore Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic. To this end, we identify the latent topics in volunteerism-related microblogs on Weibo, the Chinese equivalent of Twitter using the topic modeling analysis via Latent Dirichlet Allocation. To further investigate the public sentiment toward the topics generated by LDA, we also conducted sentiment analysis on the sample (...) posts using the open-source natural language processing technique from Baidu. Through an in-depth analysis of 91,933 Weibo posts, this study captures 10 topics that are, in turn, distributed into five factors associated with volunteerism in China as motive fulfillment, fear of COVID-19, individual characteristic, government support, and community effect. The results show that motive fulfillment, government support, and community effect are the factors that could enhance positive attitudes toward volunteerism since the topics related to these factors report high proportions of positive emotion. Fear of COVID-19 and individual characteristic are the factors inducing negative sentiment toward volunteerism as the topics related to these factors show relatively high proportions of negative emotion. The provision of tailored strategies based on the factors could potentially enhance Chinese people’s willingness to participate in volunteer activities during the COVID-19 pandemic. (shrink)

Conventional quantitative methods for the measurement of organizational legitimacy consider mainly three sources that make judgments about organizations visible: news media, accreditation bodies, and surveys. Over the last decade, however, social media have enabled ordinary citizens to bypass the gatekeeping function of these institutional evaluators and autonomously make individual judgments public. This inclusion of voices beyond functional and formally organized stakeholder groups potentially pluralizes the ongoing discussions about organizations. The individual judgments in blogs, tweets, and Facebook posts give indication about (...) the broader fit between an organization’s perceived behavior and heterogeneous social norms and therefore constitute an indicator of organizational legitimacy that can be accessed and measured. We propose the use of social media data and sentiment analysis to study the affect-based responses to organizational actions by citizens. We critically discuss and compare the method with existing quantitative methods for legitimacy measurement and apply them to a recent case in the banking industry. We discuss the value of the method for studying the process of legitimacy construction as the expression and negotiation of normative judgments about organizations by various evaluators. (shrink)

This article continues the line of research that combines the paradigm of Critical Discourse Analysis with quantitative methods. We propose that Linguistic Inquiry and Word Count, a software for automated text analysis widely used in social sciences, can enrich the CDA toolkit. The methodological advantage of LIWC is that its semantic categorization was developed and validated independently, which addresses the concerns about subjectivity. In two case studies we use LIWC to analyze the construction and representation of the ‘Other’ (...) in mass media. Study 1 focuses on the representation of Russia in The New York Times before and after its annexation of Crimea; Study 2 analyzes the change in sentiment toward Islam in NYT before and after 9/11. We find that in both cases the change in attitude is driven by an increase in negative emotion words rather than by a decrease in positive words. (shrink)

Social media commentary, satellite imagery and GPS data are a part of ‘alternative data’, that is, data that originate outside of the standard repertoire of market data but are considered useful for predicting stock prices, detecting different risk exposures and discovering new price movement indicators. With the availability of sophisticated machine-learning analytics tools, alternative data are gaining traction within the investment management and algorithmic trading industries. Drawing on interviews with people working in investment management and algorithmic trading firms utilizing alternative (...) data, as well as firms providing and sourcing such data, we emphasize social media-based sentiment analytics as one manifestation of how alternative data are deployed for stock price prediction purposes. This demonstrates both how sentiment analytics are developed and subsequently utilized by investment management firms. We argue that ‘alternative data’ are an open-ended placeholder for every data source potentially relevant for investment management purposes and harnessing these disparate data sources requires certain standardization efforts by different market participants. Besides showing how market participants understand and use alternative data, we demonstrate that alternative data often undergo processes of prospecting and assetization. We further contend that the widespread embracement of alternative data in investment management and trading encourages a financialization process at the data level which raises new governance issues. (shrink)

In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor. This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of (...) the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model. (shrink)

_The present study delves into the sentiment analysis of Pakistani youth towards TikTok influencers, employing a mixed-methods research design. The data for the present study was collected using a questionnaire consisting of both open-ended and closed-ended items. The data was collected from Pakistani youth ranging from 15 to 30 years of age (timespan was from February 2023 to September 2023). The sample of the study were 300 Pakistani youth, comprising 41.1% males and 58.9% females. Statistical Package for Social (...) Sciences (SPSS-v20) was employed for descriptive statistics and Chi-Square test on quantitative data. While for sentiment analysis of qualitative data Azure Machine Learning add-on was used in Microsoft Excel for polarity categorization of sentiments. Chi-Square statistics revealed that the relationship between age group and following TikTok influencers was statistically significant (p=0.010) while there was no significant correlation between gender and following TikTok influencers (p= 0.421). While the qualitative analysis of the data i.e., content analysis revealed a wide spectrum of emotions and sentiments among the respondents, showcasing a varied significance of the TikTok influencers in shaping their thoughts, perceptions, and worldviews. The study offers both qualitative and quantitative insights to inform ongoing research in the realm of digital culture._. (shrink)

This article addresses how Asian racialization grounds contemporary social media experimentation on—and comprehensive surveillance of—users. To make this point, we focus on the relationship between the sentimentality of white benevolence as an expression of US empire and the social scientific history of sentiment analysis, which derives from early twentieth-century analyses of women workers and Japanese internment camps. The drive to “read” the inscrutable other—framed as a benevolent alternative to direct coercion—underlies methods to better capture and control individuals by (...) understanding their reactions within experimental and disruptive environments. In tracing these histories, we contribute to larger efforts to unpack the centrality of racial formations to current forms of social media and to reveal how contemporary digital campaigns to protect Asian Americans sustain the mutually constitutive logic of white love and white hate. We conclude by reading for moments of Asian affective opacity as ways to move beyond the sentimental economy of love and hate that so easily feeds the digital economy of sentiment analysis. These examples of opacity make possible contradictory meanings to any particular affective response and makes possible practices of care that coexist with, but exceed, the extractive logics of sentiment analysis. (shrink)

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

As an important factor related to the interests of enterprises, the attitude and behavior of employees are related to the company’s survival and the realization of business objectives. However, in recent years, with the rapid development of emerging industries and industry changes, the turnover rate of employees in the whole enterprise has been greatly improved. Frequent turnover of personnel will have a great impact on the stability of the company, the competitiveness of the enterprise, and the operating cost. Employee loyalty (...) and corporate culture satisfaction assessment is the core of sustainable corporate development, but most of the traditional analysis studies are based on the performance approach, using employee overtime, employee achievement, and company effectiveness as data sources, the former performance data is only a side view of the company’s situation, the latter as the data source of this study is a one-sided and involute form, and the data is not stable. Therefore, this paper proposes an analysis of employee loyalty and corporate culture satisfaction assessment based on sentiment analysis, using association rules and sentiment lexicons from anonymous employee evaluations to analyze the actual data. (shrink)

More and more tourists are sharing their travel feelings and posting their real experiences on the Internet, generating tourism big data. Online travel reviews can fully reflect tourists’ emotions, and mining and analyzing them can provide insight into the value of them. In order to analyze the potential value of online travel reviews by using big data technology and machine learning technology, this paper proposes an improved support vector machine algorithm based on travel consumer sentiment analysis and builds (...) an Hadoop Distributed File System system based on Map-Reduce model. Firstly, Internet travel reviews are pre-processed for sentiment analysis of the review text. Secondly, an improved SVM algorithm is proposed based on the main features of linear classification and kernel functions, so as to improve the accuracy of sentiment word classification. Then, HDFS data nodes are deployed on the basis of Hadoop platform with the actual tourism application context. And based on the Map-Reduce programming model, the map function and reduce function are designed and implemented, which greatly improves the possibility of parallel processing and reduces the time consumption at the same time. Finally, an improved SVM algorithm is implemented under the built Hadoop platform. The test results show that online travel reviews can be an important data source for travel big data recommendation, and the proposed method can quickly and accurately achieve travel sentiment classification. (shrink)

This paper aims to explore coevolution of emotional contagion and behavior for microblog sentiment analysis. Accordingly, a deep learning architecture is proposed for the target microblog. Firstly, the coevolution of emotional contagion and behavior is described by the tie strength between microblogs, that is, with the spread of emotional contagion, user behavior such as emotional expression will be affected. Then, based on user interaction and the correlation with target microblog, the Hawkes process is adopted to quantify the tie (...) strength between microblogs so as to build the corresponding weighted network. Secondly, in the weighted network, the Deepwalk algorithm is used to build the sequence representation of microblogs which are similar to the target microblog. Next, a CNN-BiLSTM-Attention network is designed to analyze the sentiment analysis of target and similar microblogs. Finally, the experimental results on two real Twitter datasets demonstrate that the proposed MSA-UITC has advanced performance compared with the existing state-of-the-art methods. (shrink)

This paper takes laptops as an example to carry out research on quantitative model of brand recognition based on sentiment analysis of big data. The basic idea is to use web crawler technology to obtain the most authentic and direct information of different laptop brands from first-line consumers from public spaces such as buyer reviews of major e-commerce platforms, including review time, text reviews, satisfaction ratings and relevant user information, etc., and then analyzes consumers’ sentimental tendencies and recognition (...) status of the product brands. This study extracted a total of 437,815 user reviews of laptops from e-commerce platforms from January 1, 2019 to December 31, 2021, and performed data preprocessing on the obtained review data, followed by sentiment dictionary construction, attribute expansion, text quantification and algorithm evaluation. This paper analyzed the information receiving and processing hierarchy of the quantitative model of brand recognition, discussed the interactive relationship between brand recognition and consumer sentiment, discussed the brand recognition bias, style and demand in the context of big data, and performed the sentiment statistics and dimension analysis in the quantitative model of brand recognition. The study results show that the quantitative model of brand recognition based on sentiment analysis of big data can transform and map the keywords in text to word vectors in the high-dimensional semantic space by performing unsupervised machine learning on the text based on artificial neural network computer bionic metaphors; the model can accumulate each brand-related buyer review in the corresponding brand recognition dimension, so as to obtain the value of each product in each dimension of brand recognition; finally, the model will add the values of each dimension of brand recognition, that is, obtain the relevant value of the sum of each brand recognition. The results of this paper may provide a reference for further research on the quantitative model of brand recognition based on sentiment analysis of big data. (shrink)

With the development of sentiment analysis, studies have been gradually classified based on different researched candidates. Among them, aspect-based sentiment analysis plays an important role in subtle opinion mining for online reviews. It used to be treated as a group of pipeline tasks but has been proved to be analysed well in an end-to-end model recently. Due to less labelled resources, the need for cross-domain aspect-based sentiment analysis has started to get attention. However, challenges (...) exist when seeking domain-invariant features and keeping domain-dependent features to achieve domain adaptation within a fine-grained task. This paper utilizes the domain-dependent embeddings and designs the model CD-E2EABSA to achieve cross-domain aspect-based sentiment analysis in an end-to-end fashion. The proposed model utilizes the domain-dependent embeddings with a multitask learning strategy to capture both domain-invariant and domain-dependent knowledge. Various experiments are conducted and show the effectiveness of all components on two public datasets. Also, it is also proved that as a cross-domain model, CD-E2EABSA can perform better than most of the in-domain ABSA methods. (shrink)

The number of comments/reviews for movies is enormous and cannot be processed manually. Therefore, machine learning techniques are used to efficiently process the user’s opinion. This research work proposes a deep neural network with seven layers for movie reviews’ sentiment analysis. The model consists of an input layer called the embedding layer, which represents the dataset as a sequence of numbers called vectors, and two consecutive layers of 1D-CNN for extracting features. A global max-pooling layer is used to (...) reduce dimensions. A dense layer for classification and a dropout layer are also used to reduce overfitting and improve generalization error in the neural network. A fully connected layer is the last layer to predict between two classes. Two movie review datasets are used and widely accepted by the research community. The first dataset contains 25,000 samples, half positive and half negative, whereas the second dataset contains 50,000 specimens of movie reviews. Our neural network model performs sentiment classification among positive and negative movie reviews called binary classification. The model achieves 92% accuracy on both datasets, which is more efficient than traditional machine learning models. (shrink)

Unsolicited email campaigns remain as one of the biggest threats affecting millions of users per day. During the past years several techniques to detect unsolicited emails have been developed. This work provides means to validate the hypothesis that the identification of the email messages’ intention can be approached by sentiment analysis and personality recognition techniques. These techniques will provide new features that improve current spam classification techniques. We combine personality recognition and sentiment analysis techniques to analyse (...) email content. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best email spam classifiers and filters to each dataset in order to compare results. (shrink)

The hypothesis of this paper is that writers with similar academic backgrounds express personal attitudes in English and in Spanish differently in research papers. Thus, the main objectives are, first, to study the differences in the use of attitude devices in Spanish and English academic discourse; second, to compare the results in the different sections of articles; and finally to study the positive or negative semantic implications of the lexical items by carrying out a sentiment analysis. To this (...) end, fifteen Spanish industrial engineering papers were compared with fifteen English industrial engineering papers. The results showed that there are in fact differences in the way academic writers communicate attitude, but the sentiment analysis revealed that neutral lexical items were the most commonly used in engineering research papers. Even though engineering researchers share the knowledge of the specialist content and the academic style of expressing their thoughts, personal attitudes were expressed in different ways in Spanish and in English. (shrink)

The mobile game “Immortal Conquest,” created by NetEase Games, caused a dramatic user dissatisfaction event after an introduction of a sudden and uninvited “pay-to-win” update. As a result, many players filed grievances against NetEase in a court. The official game website issued three apologies, with mix results, to mitigate the crisis. The goal of the present study is to understand user feedback content from the perspective of Situational Crisis Communication Theory through semantic network analysis and sentiment analysis (...) to explore how an enterprise’s crisis communication strategy affects users’ attitudes. First, our results demonstrate that the diminishing crisis communication strategies do not change players’ negative attitudes. It was not a failure because it successfully alleviated the players’ legal complaints and refocused their attention on the game itself. Second, the rebuild strategy was effective because it significantly increased the percentage of positive emotions and regenerated expectations for the game. The litigation crisis was identified within gamer communications with respect to Chinese gaming companies for the first time. Nevertheless, this does not indicate an increase in overall legal awareness among the larger Chinese population. It may only reflect greater legal awareness among Chinese online gamers. Fourth, gamers emphasized that they and enterprises should be equally involved when communicating with each other. Finally, in-game paid items should be reasonably priced, otherwise, they will drive users to competitors. (shrink)

With the accelerated evolution of the internet in the form of web-sites, social networks, microblogs, and online portals, a large number of reviews, opinions, recommendations, ratings, and feedback are generated by writers or users. This user-generated sentiment content can be about books, people, hotels, products, research, events, etc. These sentiments become very beneficial for businesses, governments, and individuals. While this content is meant to be useful, a bulk of this writer-generated content requires using text mining techniques and sentiment (...) analysis. However, there are several challenges facing the sentiment analysis and evaluation process. These challenges become obstacles in analyzing the accurate meaning of sentiments and detecting suitable sentiment polarity specifically in the Bangla language. Sentiment analysis is the practice of applying natural language processing and text analysis techniques to identify and extract subjective information from text. This paper presents how the attention mechanism could be incorporated effectively and efficiently in analyzing the Bangla sentiment or opinion. (shrink)

Users are increasingly turning to the internet to acquire and consume goods. Online purchasing builds demand between customers in modern years. E-commerce is a business strategy that allows individuals and businesses to buy and sell goods and services through the Internet. Ecommerce can be used on computers, tablets, cellphones, and other smart devices, and it operates in four key market categories. The way individuals buy and consume goods and services has changed as a result of e-commerce. People are increasingly using (...) their computers and smart devices to place orders for things that can be delivered quickly to their homes. In the 1960s, ecommerce made use of an electronic system called electronic data interchange to help in document conversion. In the world of e-commerce, Amazon is a monster. It is, in reality, the world's largest online store, and it is still growing. As a result, it has become a significant roadblock in the retail industry, prompting some major merchants to rethink their plans and adjust their focus. This article is based on literary reviews. Developing a research framework for consumer trends, particularly in terms of purchasing behavior, is very much necessary. The sample size for this investigation was determined using a simple rule of thumb for successful partial least squares structural equation modeling estimation. Consumer sentiment tendencies play a major role in this research. This research's most valuable factors include a promotion, price, brand loyalty, product review, and product quality. We looked into how these aspects analyzed a customer's tendency. These are the primary topics of discussion in this study. (shrink)

This paper investigates if and to what point it is possible to trade on news sentiment and if deep learning, given the current hype on the topic, would be a good tool to do so. DL is built explicitly for dealing with significant amounts of data and performing complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking to improve their (...) trading process. Moreover, DL and specifically LSTM seem a good pick from a linguistic perspective too, given its ability to “remember” previous words in a sentence. After having explained how DL models are built, we will use this tool for forecasting the market sentiment using news headlines. The prediction is based on the Dow Jones industrial average by analyzing 25 daily news headlines available between 2008 and 2016, which will then be extended up to 2020. The result will be the indicator used for developing an algorithmic trading strategy. The analysis will be performed on two specific cases that will be pursued over five time-steps and the testing will be developed in real-world scenarios. (shrink)

In this paper, I examine the notion of the ‘psychological dynamics’ that Professor Shun uses for explicating Confucian moral anger, based on David Hume’s (1711–76) psychological account of mind, to reconsider the role that object-based distinctions of emotions play in the Confucian moral tradition. First, by appealing to Hume’s investigation of the mental processes involved in feeling moral sentiments, I suggest that imagination, as a component in the ‘psychological dynamics’, explains how ‘dust’ settles on the mind to yield inappropriate emotional (...) responses. Second, by re-examining Confucius’s approval of Yan Hui’s moral anger, that he ‘never misdirects his anger’, I argue that though Confucians would not characterize the difference in emotional responses by distinguishing the first- and third-person perspectives, their goal of moral self-cultivation would encourage them to recognize the practical significance of distinguishing objects in situations, along with perceiving situations as a whole. While our minds as mental mirrors can reflect the whole situation, they are unique mirrors that resonate more vigorously with specific objects such as others’ minds. (shrink)

In a reply to Mizrahi (2019), Bryant (2020) raises several methodological concerns regarding my attempt to test hypotheses about the observation that academic philosophers tend to find “scientism” threatening empirically using quantitative, corpus based methods. Chief among her methodological concerns is that numbers of philosophical publications that mention “scientism” are a “poor proxy for scholarly sentiment” (Bryant 2020, 31). In reply, I conduct a sentiment analysis that is designed to find out whether academic philosophers have negative, positive, (...) or neutral sentiments toward “scientism.” The results of this analysis suggest that, for the most part, articles on “scientism” written by academic philosophers tend to contain mostly negative rather than positive (or neutral) sentiments about “scientism.”. (shrink)

COVID-19 is a disease that affects the quality of life in all aspects. However, the government policy applied in 2020 impacted the lifestyle of the whole world. In this sense, the study of sentiments of people in different countries is a very important task to face future challenges related to lockdown caused by a virus. To contribute to this objective, we have proposed a natural language processing model with the aim to detect positive and negative feelings in open-text answers obtained (...) from a survey in pandemic times. We have proposed a distilBERT transformer model to carry out this task. We have used three approaches to perform a comparison, obtaining for our best model the following average metrics: Accuracy: 0.823, Precision: 0.826, Recall: 0.793 and F1 Score: 0.803. (shrink)

This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the "public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be (...) preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. (shrink)

This study uses data from Google Trends and Twitter to analyze how public interest and sentiment towards Universal Basic Income (UBI) changed across all 50 states and Washington D.C. between 2018 and 2021. We specifically selected this time period as it includes both Andrew Yang’s UBI campaign during the Democratic primaries in 2019 and the COVID-19 pandemic in 2020 when UBI gained attention due to the federal government’s unconditional cash payment to almost all citizens. To overcome the limitations of (...) sporadic opinion polls, we built on a recent development of the rescaling method to generate longitudinal Google Trends and conducted Twitter sentiment analysis. We observed a modest rise in public interest in UBI during Andrew Yang’s campaign, especially in blue states, and a significant increase across all states at the onset of the COVID-19 pandemic. Although it quickly waned, the level of public attention became elevated compared to the pre-pandemic level. Contrary to previous studies, our analysis also reveals that overall sentiment became less positive after the peak interest during the pandemic, as more people engaged in online discussions. (shrink)

Mass media have been accused of cultivating anti-immigrant sentiments in Western societies. Most studies on this topic, however, have not made a distinction between the types of television program (information vs. entertainment) or television station (public vs. commercial). Adopting a comparative approach, we use data from the six waves of the European Social Survey (ESS, 2002–2012, n = 162,987) to assess the relationship between individual and aggregate level patterns of television consumption and anti-immigrant sentiments in European societies. Individual television viewing (...) time is positively associated with anti-immigrant sentiments, while frequent exposure to news and information programs is associated with lower levels of anti-immigrant sentiments. At the aggregate level we observe a positive effect of the total viewing time in society on anti-immigrant sentiments. In the conclusion we offer some suggestions on how this effect could be explained. (shrink)

I analyse the concept of sentimental value, with a view to identifying its relations with the notions of intrinsic, final, extrinsic and instrumental value. The analysis explores issues arising in the understanding of an object as sentimentally valuable, and reveals a serious tension in the common sense extrinsic conception of sentimental value.

The initial problem to be faced by naturalist investigations into morals is how to conceptualize it in a way that explains what ordinarily occurs in nature. In this sense, a vertical explication where actions are catalogued according to abstract theoretical constructions says little about the world. Therefore, the investigation presented in this article will be guided by an inverse methodology. It will refer to analysis of ordinary social behaviours which I will advance through the understanding of morality according to (...) its functionality. The concept of morals presented by Tugendhat, for whom morality is a combination of reciprocal demands expressed through sentences of duty based on sentiments, is of major importance to the way in which I will address the question. Moreover, I will carry out an accurate study of humean moral thought, with the objective of determining to what extent sentiment and reason influence the motivation to approve or censor socially relevant actions. Once we have investigated the natural aspects of the moral phenomenon, I will conclude the article presenting a possible compatibility between theoretical marks of humean empiricism and Darwin’s evolutionism. (shrink)

This thesis defends a naturalistic interpretation, and offers a critical analysis, of the views of David Hume on the subject of free will and moral responsibility. A central theme is that Hume's views should be understood and assessed in relation to P.F. Strawson's influential paper "Freedom and Resentment" (1962). -/- The work in this thesis lays the foundation for "Freedom and Moral Sentiment: Hume's Way of Naturalizing Responsibility" (Oxford University Press: 1995).

This article considers the ongoing difficulties for mainstream political theory of actualizing human rights, with particular reference to Rorty’s attempt to transcend their liberal foundations. It argues that there is a problematic disjuncture between his articulation of exclusion and his hope for inclusion via the expansion of the liberal human rights culture. More specifically, it shows that Rorty’s description of victimhood is based on premises unavailable to him, with the consequence that stateless persons are rendered inhuman, and, further, that his (...) accounts of sentimentality and solidarity have limited potential for the inclusion of such victims within the liberal ‘community of justification’. In the final analysis, the article argues that there is a substantial mismatch between Rorty’s dependence on both liberal norms and international political practice, and his hopes for the human rights culture to include those stripped of human dignity. (shrink)

This article examines the role played by ‘feeling’ (Gefühl) and ‘love’ within the philosophy of German Romanticism. After an introduction (I) to the actual debate on German Romanticism, paragraph II sketches an analysis of the concept of Gefühl at the end of the eighteenth century and highlights the differences with its actual meaning. The successive three sections are dedicated to three pivotal figures of German Romanticism: F. Schlegel (III), Novalis (IV), and Baader (V). Similarities and differences between these authors (...) will be emphasized. It will be demonstrated that they deny the reason˗feeling opposition, and that ‘feeling’ and ‘love’ are at the heart of their ontology, epistemology, and conception of subjectivity, conditioning the Romantic human-nature relationship and Romantic political philosophy. In the concluding remarks (VI), this paper explains how German Romanticism can still help to better understand the role of feelings in politics. (shrink)

Mizrahi (2017a) advances an argument in support of Weak Scientism, which is the view that scientific knowledge is the best (but not the only) knowledge we have, according to which Weak Scientism follows from the premises that scientific knowledge is quantitatively and qualitatively better than non-scientific knowledge. In this paper, I develop a different argument for Weak Scientism. This latter argument for Weak Scientism proceeds from the premise that academic disciplines that make progress are superior to academic disciplines that do (...) not make progress. In other words, other things being equal, it is generally better for an academic discipline to make progress than to make little or no progress, given that an academic discipline that is making little or no progress is an academic discipline that is failing to achieve its epistemic goals. Now, if there is no question among academic philosophers that science makes progress, and significantly so, but there is an open question among academic philosophers as to whether academic philosophy makes progress (and if so, how much), then academic philosophers would have to agree that science is superior to academic philosophy in terms of making progress. I develop this argument in this paper and provide empirical evidence suggesting that the premises would be acceptable to academic philosophers. (shrink)

Certains auteurs ont avancé que le premier confinement de 2020 avait profondément remis en cause nos manières d’appréhender le temps. Cette affirmation est ici discutée à partir de l’étude empirique des variations d’une dimension des rapports au temps – le sentiment de manquer de temps – avant la crise sanitaire, pendant le premier confinement, et pendant le second confinement. L’analyse statistique de données de quatre enquêtes en population générale met en évidence une atténuation d’ensemble du sentiment de manquer (...) de temps pendant le premier confinement, puis un retour partiel à la normale pendant le second confinement. La complainte du manque de temps n’a donc connu qu’une éclipse de deux mois avant de reprendre une place plus centrale dans le quotidien des Français. La rémanence des rapports au temps se manifeste également dans la remarquable stabilité de la polarisation sociale du sentiment de manque de temps, qui concerne, aussi bien en confinement qu’en temps « normal », davantage les classes supérieures, les âges médians et les femmes. Ces résultats prennent sens dans une approche sociologique des rapports au temps, saisis comme une des composantes premières du rapport au monde des individus, ancrée dans des rapports sociaux, notamment de classe et de genre. (shrink)

Funerals are a reflective practice to bid farewell to the departed soul. Different religions, cultural traditions, rituals, and social beliefs guide how funeral practices take place. Family and friends gather together to support each other in times of grief. However, during the coronavirus pandemic, the way funerals are taking place is affected by the country's rules and region to avoid the spread of infection. The present study explores the media portrayal of public sentiments over funerals. In particular, the present study (...) tried to identify linguistic dimensions associated with lexical components of social processes, affective processes, fear, and disgust. An exhaustive search of newspaper coverage of funeral and related articles was made for a specific corona period. After an initial screening for the details and language used, a total of 46 newspaper articles on funerals were finalized for the analysis. Linguistic Inquiry and Word Count (LIWC) software was used to determine the association between linguistic dimensions of function words and words related to social and affective processes, as presented in the newspaper articles. Sentiment Analysis and Cognition Engine (SEANCE) was applied for the analysis of sentiment, social cognition, and social order. Bayesian correlation analysis and regression revealed positive and significant associations between function words and affective processes, between pronouns and social processes, and between negative adjectives and psychological processes of fear and disgust. Also, significant negative associations were found between polarity nouns and psychological processes of fear and disgust and between polarity verbs and psychological processes of fear and disgust. Bayes factor 10 provides strong evidence in favor of the study hypotheses. The media is influenced by the prevailing sentiments in society and reflects their perception of the current social order and beliefs. The findings provide a glimpse into the prevailing sentiment of society through the lens of media coverage. These understandings are expected to enhance our observations of how people express their feelings over the loss of their loved ones and help mental health professionals develop their therapeutic protocols to treat the coronavirus disease 2019 (COVID-19)-affected population. (shrink)

One of the interesting aspects of Dewey’s early educational thought is his apparent hostility toward children’s imaginative pursuits, yet the question of why this antipathy exists remains unanswered. As will become clear, Dewey’s hostility towards imaginative activities stemmed from a broad variety of concerns. In some of his earliest work, Dewey adopted a set of anti-Romantic criticisms and used these concerns to attack what one might call “runaway” imaginative and emotional tendencies. Then, in his early educational writings, these earlier concerns (...) were augmented by several other factors, including problematic trends in progressive education, new developments in psychology, and Dewey’s own educational aims. This analysis explores the roots of these criticisms, and explains how they culminate in the stance on the imagination that Dewey eventually outlined in the early educational writings. Notably, these findings have some important implications for certain prominent contemporary critics of progressivism. (shrink)

SummaryThis article focuses on the analysis of sensibility in the works of three major late eighteenth-century philosophers: Smith, Cabanis and the young Wilhelm von Humboldt. It analyses to what extent Smith's concept of sympathy influenced Cabanis in France and Humboldt in Germany. It argues that modern anthropology, based on a specific theory of sensibility, assumes a strong connection between knowledge acquisition and life in society. This article reveals the strong links between the three authors which were made possible precisely (...) because of their common philosophical background. It proves, for the first time, that Humboldt had access to Condillac's ideas before 1798, since in an early work on the state, the former makes numerous borrowings from speeches Cabanis wrote for Mirabeau, which were in turn strongly influenced by Condillac. (shrink)