Analyzing Political Sentiment Using Twitter Data

Bose, Rajesh; Dey, Raktim Kumar; Roy, Sandip; Sarddar, Debabrata

doi:10.1007/978-981-13-1747-7_41

Cited by 34 publications

(10 citation statements)

References 10 publications

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“…This is especially true recently, revealing the potential and topicality of the subject in many fields. This kind of analysis has been devoted, for instance, to consumer goods [14], [15], to brand recognition [16], to characterize political sentiment [17], [18], or to even real-time television scheduling of tv-programming [19], among others. But the area of stock markets and companies evaluation is probably the most intensively analyzed, as the titanic economic environment surrounding this specific topic encourages any potential new perspective that may provide tools or support for investor business cases.…”

Section: Introductionmentioning

confidence: 99%

On the Statistical and Temporal Dynamics of Sentiment Analysis

et al. 2020

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Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at the same time an interesting way to identify singularities among peers. We conclude that temporal properties of messages provide with information about the sentiment dynamics, which is different in terms of lexicons and users, but commonalities can be exploited in this field using appropriate temporal digital processing tools.

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Section: Introductionmentioning

confidence: 99%

On the Statistical and Temporal Dynamics of Sentiment Analysis

et al. 2020

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“…Sentiment analysis, on the other side, is constituted by a series of techniques-based on natural language processing-used to study affective states, opinions, and other subjective information conveyed by a text. 114,115 While automated approaches to text still struggle to adequately frame sarcasm and irony, 116 posing relevant methodological concerns, topic modeling and sentiment analysis can be combined 117 to compare and contrast the characteristics of non-religious discourses in time and space.…”

Section: Text Analyticsmentioning

confidence: 99%

How can big data shape the field of non-religion studies? And why does it matter?

2021

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The shift of attention from the decline of organized religion to the rise of post-Christian spiritualities, anti-religious positions, secularity, and religious indifference has coincided with the deconstruction of the binary distinction between ''religion'' and ''non-religion''-initiated by spirituality studies throughout the 1980s and recently resumed by the emerging field of non-religion studies. The current state of cross-national surveys makes it difficult to address the new theoretical concerns due to (1) lack of theoretically relevant variables, (2) lack of longitudinal data to track historical changes in non-religious positions, and (3) difficulties in accessing small and/or hardly reachable sub-populations of religious nones. We explore how user profiling, text analytics, automatic image classification, and various research designs based on the integration of survey methods and big data can address these issues as well as shape non-religion studies, promote its institutionalization, stimulate interdisciplinary cooperation, and improve the understanding of non-religion by redefining current methodological practices. ll

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“…Twitter is platform where many people around in the world express their thoughts, opinions about something and even their daily activities in the form of sentence or phrase. In the world, there are more 300 million users with 500 million tweets typed every day [14]. However, analyzing unstructured data like tweets is a tough business and also extracting beneficial information from twitter is an enormous challenge for scientists [15].…”

Section: Introductionmentioning

confidence: 99%

Data mining applied about polygamy using sentiment analysis on Twitters in Indonesian perception

et al. 2021

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Polygamy remains one of the interesting key topics in various society, especially Indonesia. Polygamy is the act of marrying multiple spouses that means having more one wife at the same time, is common case in worldwide. In this problem, the most of Muslim in Indonesia adopt Islamic law that let men to do the polygamy with certain requirements. In worldwide communities, this has been a prominent feature and has become the subject of numerous books, heated debates, journal articles, discussion papers, and so on. Furthermore, in Indonesia the polygamous marriage has recently become a heated topic. Despite controversy, Indonesia’s law, of recent, allows people for doing polygamy in certain conditions. Because the polygamy is often debated, this study focuses on assessment of Indonesian’s perceptions through sentiment analysis and would determine people’s perception about polygamy issue from 500 tweets on Twitter. To conclude, it elucidates that the polygamy in Indonesia is a normal thing and few assume the case is as negative thing.

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Analyzing Political Sentiment Using Twitter Data

Cited by 34 publications

References 10 publications

On the Statistical and Temporal Dynamics of Sentiment Analysis

On the Statistical and Temporal Dynamics of Sentiment Analysis

How can big data shape the field of non-religion studies? And why does it matter?

Data mining applied about polygamy using sentiment analysis on Twitters in Indonesian perception

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