Social Sentiment Research Articles

A Review on Opinion Mining: Approaches, Practices and Application

Opinion Mining also known as Sentiment Analysis (SA) has recently become the focus of many researchers, because analysis of online text is useful and demanded in many different applications. Analysis of social sentiments is a trending topic in this era because users share their emotions in more suitable format with the help of micro blogging services like twitter. Twitter provides information about individual's real-time feelings through the data resources provided by persons. The essential task is to extract user's tweets and implement an analysis and survey. However, this extracted information can very helpful to make prediction about the user's opinion towards specific policies. The motive of this paper is to perform a survey on sentiment analysis algorithms that shows the utilizing of different ML and Lexicon investigation methodologies and their accuracy. Our paper also focuses on the three kinds of machine learning algorithms for Sentiment Analysis- Supervised, Unsupervised Algorithms.

Social emotion classification of Japanese text information based on SVM and KNN

How to model multi-modal data and rich context information under the same model framework has become the key to user sentiment analysis in social media. This research uses SVM and KNN to build models for Japanese social sentiment classification and proposes a topic model for user sentiment analysis based on SVM and KNN. Moreover, taking TE process data as an example, this paper selects radial basis kernel function and grid search method for the construction of SVM classifier. In addition, by introducing the correlation variable between the sentiment of the comment and the sentiment of the tweet, the comment is related to the original tweet. Based on the establishment of the model, this research proposes a parameter estimation algorithm for model solving based on the idea of SVM-KNN, and uses the Twitter real data set as the experimental data set to verify the effectiveness of the user sentiment analysis model proposed in this paper. The research results show that the method proposed in this paper has a certain effect.

Do Google Trends forecast bitcoins? Stylized facts and statistical evidence

In early 2018 Bitcoin prices peaked at US$ 20,000 and, almost two years later, we still continue debating if cryptocurrencies can actually become a currency for the everyday life or not. From the economic point of view, and playing in the field of behavioral finance, this paper analyses the relation between Bitcoin price and the search interest on Bitcoin since 2014. We questioned the forecasting ability of Google Bitcoin Trends for the behavior of Bitcoin price by performing linear and nonlinear dependency tests, and exploring performance of ARIMA and Neural Network models enhanced with this social sentiment indicator. Our analyses and models are founded upon a set of statistical properties common to financial returns that we establish for Bitcoin, Ethereum, Ripple and Litecoin.

Measuring topic network centrality for identifying technology and technological development in online communities

Online communities are a rapidly growing knowledge repository that provides scholarly research, technical discussion, and social interactivity. This abundance of online information increases the difficulty of keeping up with new developments difficult for researchers and practitioners. Thus, we introduced a novel method that analyses both knowledge and social sentiment within the online community to discover the topical coverage of emerging technology and trace technological trends. The method utilizes the Weibull distribution and Shannon entropy to measure and link social sentiment with technological topics. Based on question-and-answer and social sentiment data from Zhihu, which is an online question and answer (Q&A) community with high-profile entrepreneurs and public intellectuals, we built an undirected weighting network and measured the centrality of nodes for technology identification. An empirical study on artificial intelligence technology trends supported by expert knowledge-based evaluation and cognition provides sufficient evidence of the method's ability to identify technology. We found that the social sentiment of hot technological topics presents a long-tailed distribution statistical pattern. High similarity between the topic popularity and emerging technology development trends appears in the online community. Finally, we discuss the findings in various professional fields that are widely applied to discover and track hot technological topics.

WITHDRAWN: Sentiment analysis of product feedback using natural language processing

Sentiment Analysis is characterised as contextual manipulation of the text that recognises subjective information in the source material and then extracts it and also helps many companies understand their product or service's social sentiment. Nearly all professional and non-technical companies use sentiment analysis in order to reliably interpret customer input and wishes on how the product or service is fit. Sentimental research is used by top companies in nontechnology and technology. This is more or less organisational and not available to the public. More feedback applications are based on the organisational level and a standard interface that almost any company can use in any field can be very useful to recognise feedback easily using the natural language processing algorithm, without needing a particular programme or application at the organisational level.Customer reviews are digitally gathered and analysed using the NLP algorithm for fast and accurate review. The usage for such purposes can be used by top organisations which have revealed APIs and SDK resources. In this input review method APIs from organisations such as IBM, Parallel points etc can be improved like Twitter, Facebook and WordPress.

Stock Movement Prediction with Social Sentiments and Interactional Data: Integrating NLP and Bayesian Frameworks

Stock Movement Prediction with Social Sentiments and Interactional Data: Integrating NLP and Bayesian Frameworks

Collaborating The Textual Reviews Of The Merchandise and Foretelling The Rating Supported Social Sentiment

Lately, we have seen a twist of audit sites. It presents a decent opportunity to share our experience for a considerable length of time we have bought. Be that as it may, we tend to confront the information over-burdening issue. A method for mining significant information from surveys to know a client's inclinations and produce precise proposal is fundamental. Since quite a while ago settled recommender Systems (RS) considers a few variables, similar to client's buy records, item class, and geographic area. During this work, we have proposed sentiment-based rating prediction technique (RPS) to help up the expectation precision in recommender Systems. First and foremost, we examine the social user sentimental measuring approach and calculate every user’s sentiment on things/items. Furthermore, we don't exclusively consider a client's own wistful properties anyway moreover take interpersonal social sentimental influence into study. Then, at that point, we propose to consider item name, which might be deduced by the sentimental distributions of a user set that reflect clients' comprehensive analysis. Finally, we tend to intertwine 3 factors-user sentiment similarity, interpersonal social sentimental distributions of a client opinion likeness, interpersonal social sentimental influence, associate the thing's reputation relationship into our recommender system to make a talented rating prediction. Then, at that point, we arranged a presentation analysis of the 3 sentimental factors on a genuine world dataset gathered from Yelp. Our exploratory outcomes show, the sentiment will well describe user preferences, which facilitate to hike the proposal execution.

Should investors consider the sentiment of online discussions? An analysis of the link between fundamental information, social media sentiment and the stock market

Should investors consider the sentiment of online discussions? An analysis of the link between fundamental information, social media sentiment and the stock market

The Value of Accurate Weather Forecasts: Social Sentiment Responses Reflected in Social Media in China

The Value of Accurate Weather Forecasts: Social Sentiment Responses Reflected in Social Media in China

СТИЛЬ ЖИЗНИ СОВРЕМЕННОЙ РОССИЙСКОЙ МОЛОДЕЖИ: УСТАНОВКИ И ПОВЕДЕНИЕ

Fundamental changes in Russian society entailed a change in living conditions, ways of organizing it, and life styles. The relevance of the practical study of life styles as the basis for the stratification of society is justified by its ability to reflect the totality of the individual’s life practices that are closely related to his attitudes, life principles. The lifestyle shows the behavior of a person in labor activity, the main consumer practices, the organization of leisure activities, inclusion in public organizations, his orientation to socio-economic activity in general. In conditions of sufficient theoretical elaboration of this topic by foreign and domestic authors, there is a clear gap in the practical multicomponent, in accordance with the studied value, measurement of the prevailing lifestyle. Based on the theoretical model, starting from the works of P. Bourdieu G.-P. Muller, Bauman, J. Baudrillard, the author offers a practical indicator model for measuring lifestyle and gives an example of its testing on the population of the city of Cherepovets. The main practical indicators of lifestyle are: locus of responsibility (internal or external), level of claims, attitudes toward work, leisure preferences, breadth of developed consumer practices. In the practical part of the article, the author, citing the results of the study for all age groups, focuses on the specifics of the stylized attitudes of youth as one of the most promising categories for the development of society. Argued evidence of the passivity of modern youth, overstated claims and self-esteem with a low level of attitudes toward social, economic and social participation and participation in society are given. Young people are traditionally distinguished by a higher level of socio-economic optimism, the index of social sentiments in this group is higher than among the entire population. Young people have a locus of responsibility that is ambiguous for interpretation. On the one hand, they believe that their financial situation and career depend on themselves, on the other hand, external factors (luck, good connections, rich influential parents) often consider the factor to achieve a high position in society. Young people are not distinguished by high socio-economic activity and readiness for it. The results of the study confirm that not all young people seek to take an active part in volunteer activities. Young people participate in actions most often of an entertainment plan and are much less likely to take responsibility and take part in political and public affairs.

Цільова та етична неефективність формальних правил як чинник поширення маніпулювання суспільними настроями та уповільнення інституційних реформ

The article is aimed at identifying criteria for the feasibility of reforming the formal rules with significant purposeful and ethical inefficiency in the context of spreading the practice of manipulation of social sentiment. It is found that in the structure of formal rules with purposeful or ethical inefficiency, two following components can be allocated: basic formal rules adopted to achieve a specific goal; secondary formal rules that should ensure the proper implementation of the relevant basic formal rules and compensate for their inefficiency. In the absence of manipulation of social sentiment, the main factor that causes the negative impact of formal rules with purposeful or ethical inefficiency on the dynamics of formal rules and the effectiveness of institutional reforms is an increase in the number of secondary formal rules. In contrast, in the context of the spread of the practice of manipulation of social sentiment, the main threats to the success of institutional reforms are: significant inverse asymmetry of basic formal rules; the ability of formal rules with purposeful or ethical inefficiency to independently create conditions that are favorable for the manipulation of social sentiment. It is identified that the feasibility of reforming formal rules characterized by purposeful or ethical inefficiency is determined by whether the relevant formal rules have turned into an instrument for manipulation of social sentiment: in cases where ineffective formal rules have become such an instrument, their reform becomes impractical and should be postponed until the practice of manipulation of social sentiment around issues regulated by these formal rules is eliminated. It is shown that in the context of the spread of the practice of manipulation of social sentiment, an additional criterion for the feasibility of reforming ineffective formal rules is the absence of spread of the practice of manipulation of social sentiment into a range of problems regulated by the relevant ineffective formal rules.

Soviet Identity in Socio- and Ethnocultural Space of Modern Ukraine

The article deals with the phenomenon of Soviet identity in the modern Ukrainian society. On the basis of the survey, conducted among the representatives of different regions of Ukraine, the author identified respondents with Soviet identity, analyzed their social sentiments and their impact on the formation or breaking of the all-Ukrainian identity. Also, the factors shaping the positive image of the Soviet past were defined.The paper discovered that nostalgia for the Soviet past and devotion to the Soviet cultural and historical narratives are caused by such factors as social security and wealth, confidence in the future; paternalism; deep-rooted ideological clichés and the Soviet value system; remnants of the Soviet cultural heritage in the Ukrainian sociocultural space; family background of the Soviet memory; lack of critical re-evaluation of the Soviet past, etc. The author considers that “hybrid post-memory” is one of the factors that hold back Ukraine’s escaping from the Soviet historical and cultural space. This is when the people’s “memory” about political repressions and dekulakization is getting weak if their forefathers had not suffered from those tragedies. The survey demonstrates the low level of public activity among the respondents, caused by the autocratic and paternalistic model of the Soviet society, which means the need to be ruled and, as a result, disclaims personal and public responsibility and delegates it to others who can bring discipline into all spheres of life.The process of obtaining a new identity by Ukrainians is slow and sometimes contradictory, especially in the southeastern regions of Ukraine, where the majority of the population come under the strong impact of Russian and Soviet cultural and historical narratives. In particular, the survey results show that extreme anti-Ukrainian sentiments are revealed by the respondents with a strong Soviet identity.

Folktales of West Odisha: A Study

Folktales are a powerful source of oral tradition. Regional culture, environment, folk customs, customs and traditions, social customs, manners, beliefs, religious sentiments, and supernatural fantasies shape the content. The story also tells the story of the various cunning, conflicting concepts, life and physical creation, and birth mysteries of the groups involved. The article seeks to discuss the traditional and scholarly classification, the performers, and performance of folktales in West Odisha, also concerned with its socio-cultural implications.

Social mood of the residents of Moscow city at the beginning of the novel coronavirus pandemic (covid-19): possibilities of medical and sociological analysis

A new coronavirus infection, which affected most countries in the world, besides the direct risk to the health and socio-economic well-being (stability) of the population, determined the large-scale coverage of states and societies with associated risks, including the observed transformation of social space, the increase in the phenomena of voluntary social isolation and encapsulation of part among the population, and the revision of the value of social ties. In temporal terms, the density of the observed changes in the social sentiments of society and the socio-structural characteristics of this period need both retrospective reflection and sociological analysis, as foresight analysis appears to be applicable to the current social situation. This study presents the results of a medical and sociological study (questionnaire survey), implemented during the beginning of the spread of the COVID-19 pandemic in Russia on a sample of residents of Moscow. According to the study, despite the high level of awareness of Muscovites about the immediate risk and consequences of the coronavirus infection on their health, the respondents are mainly concerned about the socio-economic consequences of the pandemic. At the same time, the ideas of the respondents about the post-pandemic world include a large-scale economic decline, a violation of the functionality of the health system, and an increase in social conflicts and social disunity in the society. The main difficulties of medical and sociological research (respondent recruitment and data collection) were determined by epidemiological conditions. The study toolkit was developed considering the main trends in the spread of infection in the country and the observed social sentiment of Russians, but the high dynamics of changes did not allow to cover a wider range of issues that became relevant for Muscovites in the self-isolation period (AprilMay), which determined the understanding of the need for a second wave of research (planned for autumn 2020).

Characterizing News Report of the Substandard Vaccine Case of Changchun Changsheng in China: A Text Mining Approach.

Background: The substandard vaccine case of that broke out in July 2018 in China triggered an outburst of news reports both domestically and aboard. Distilling the abundant textual information is helpful for a better understanding of the character during this public event. Methods: We collected the texts of 2211 news reports from 83 mainstream media outlets in China between 15 July and 25 August 2018, and used a structural topic model (STM) to identify the major topics and features that emerged. We also used dictionary-based sentiment analysis to uncover the sentiments expressed by the topics as well as their temporal variations. Results: The main topics of the news report fell into six major categories, including: (1) Media Investigation, (2) Response from the Top Authority, (3) Government Action, (4) Knowledge Dissemination, (5) Finance Related and (6) Commentary. The topic prevalence shifted during different stages of the events, illustrating the actions by the government. Sentiments generally spanned from negative to positive, but varied according to different topics. Conclusion: The characteristics of news reports on vaccines are shaped by various topics at different stages. The inner dynamics of the topic and its alterations are driven by the interaction between social sentiment and governmental intervention.

Dynamics of social and Political Attitudes and Analysis of the Results of the Regional Elections of 2015–2019 in the Irkutsk Region

The article presents the data of the study of the dynamics of social and political attitudes and the analysis of the results of the regional elections of 2015–2019 in the Irkutsk region. In 2019, a sociological study was conducted in this region, which allowed determining the reasons for the formation of protest social and political sentiments. For the analysis, the author used the results of elections of the Governor of Irkutsk region in 2015 and results of elections of deputies of Legislative Assembly of Irkutsk region in 2018. Further, the author compared also votes to the Duma of the city of Irkutsk in 2019. The study identified features of the formation of the protest political attitude of residents of Irkutsk oblast and the trends of their impact on the election results at the regional level (protest voting). Finally, the author explained the reasons of the defeat of the last Governor Sergei Eroschenko and the victory of S. Levchenko, causes of deterioration of the results of the party “United Russia” and the victory of the Communist Party in the elections to the Legislative Assembly of the Irkutsk region in 2018.

Frances Power Cobbe on Brutes, Women, and the Irish (Human) Landscape: Ethics, Environment, and Imperialism

This essay focuses on how the Irish philanthropist, feminist, and animal-rights defender Frances Power Cobbe (1822-1904) uses similar terms of reference and methodologies of exposition in the pamphlets and essays she published on ‘the claims of brutes’ and on ‘the claims of women’. Both discourses are tinged with hues of imperialism proper to her Anglo-Irish upbringing, which deploy a third, less-known interest on the part of Cobbe: ‘the Irish Question’ (O’Connor 2010). To make these points, the essay studies the author’s autobiography and five of her essays and pamphlets: “The Rights of Man and the Claims of Brutes” (1863), “Life in Donegal” (1866), “The Evolution of the Social Sentiment” (1874), “Wife-Torture in England” (1878), and Light in Dark Places (1883).

Sentiment Analysis for Arabic Social Media

With the spread of social media services in Arabic societies, it leads to the explosive growth of Arabic posts, or comments. These services generate a huge volume of opinionated data on different topics such as politics and businesses. Analyzing valuable subjective information from data would assist in a better understanding and making decisions. Therefore, sentiment analysis coincides with social media networks and has become the most interesting research field in the sentiment analysis process. However, there are several challenges faced the sentiment analysis process. Arabic Sentiment analysis is indeed in its infantile stage and it has not obtained thoroughly attention wherein several challenges still need to address. Some of these challenges result from the complexity of Arabic natural language and other challenges result from social media platform itself. In this manuscript, we first study the impact of social media challenges on the challenges of Arabic language. Our findings show that such challenges add more complexities to the sentiment analysis process. Based on these findings, we review the contributed proposals, which give rise on analyzing Arabic social media data. Our review methodology is based on a set of criteria, which we propose to assess the advantages and limitations of these proposals. The interesting point here is to help researchers identify the social sentiment analysis problems along with a comprehensive survey on the sentiment analysis levels and classification approaches. Finally, we compare these proposals in terms of the average accuracy and suggest a new hybrid approach based on our findings.

THE DANGERS OF THE GEOGRAPHICAL IMAGINATION IN THE U.S. EUGENICS MOVEMENT

This article offers a new synthesis of the role of geography and geographical thinking in the inception and diffusion of the U.S. eugenics movement and its relevance in social and racial sentiments today. This role was supported by the salience of the geographical imagination and the influence of eminent Ivy League-educated elites during the first quarter of the twentieth century. Moreover, the significance here is that the eugenics movement and its geographical imagination transferred into the implementation of policy, notably immigration restriction, anti-miscegenation, and sterilization laws. The geographical imagination provided fodder for xenophobic, anti-Semitic, racist, and anti-immigrant public sentiments that left a powerful legacy to this today.

Investigating the role of Word Embeddings in sentiment analysis

During decades, Natural language processing (NLP) expanded its range of tasks, from document classification to automatic text summarization, sentiment analysis, text mining, machine translation, automatic question answering and others. In 2018, T. Young described NLP as a theory-motivated range of computational techniques for the automatic analysis and representation of human language. Outside and before AI, human language has been studied by specialists from various disciplines: linguistics, philosophy, logic, psychology. The aim of this work is to build a neural network to perform a sentiment analysis on Italian reviews from the chatbot customer service. Sentiment analysis is a data mining process which identifies and extracts subjective information from text. It could help to understand the social sentiment of clients, respect a business product or service. It could be a simple classification task that analyses a sentence and tells whether the underlying sentiment is positive or negative. The potentiality of deep learning techniques made this simple classification task evolve, creating new, more complex sentiment analysis, e.g. Intent Analysis and Contextual Semantic Search.