RSentiment: A Tool to Extract Meaningful Insights from Textual Reviews

Bose, Subhasree; Saha, Urmi; Kar, Debanjana; Goswami, Saptarsi; Nayak, Amlan Kusum; Chakrabarti, Satyajit

doi:10.1007/978-981-10-3156-4_26

Cited by 15 publications

(12 citation statements)

References 11 publications

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“…In the present research study, the required data were searched and obtained programmatically by using twitteR[2] package from Twitter (Bose et al , 2017). twitteR package is developed in R programming language and mainly interacts with Twitter web API, which delivers access to the Twitter data.…”

Section: Methodsmentioning

confidence: 99%

Crypto-currencies narrated on tweets: a sentiment analysis approach

Rouhani

Abedin

2019

IJOES

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Purpose Crypto-currencies, decentralized electronic currencies systems, denote a radical change in financial exchange and economy environment. Consequently, it would be attractive for designers and policy-makers in this area to make out what social media users think about them on Twitter. The purpose of this study is to investigate the social opinions about different kinds of crypto-currencies and tune the best-customized classification technique to categorize the tweets based on sentiments. Design/methodology/approach This paper utilized a lexicon-based approach for analyzing the reviews on a wide range of crypto-currencies over Twitter data to measure positive, negative or neutral sentiments; in addition, the end result of sentiments played a training role to train a supervised technique, which can predict the sentiment loading of tweets about the main crypto-currencies. Findings The findings further prove that more than 50 per cent of people have positive beliefs about crypto-currencies. Furthermore, this paper confirms that marketers can predict the sentiment of tweets about these crypto-currencies with high accuracy if they use appropriate classification techniques like support vector machine (SVM). Practical implications Considering the growing interest in crypto-currencies (Bitcoin, Cardano, Ethereum, Litcoin and Ripple), the findings of this paper have a remarkable value for enterprises in the financial area to obtain the promised benefits of social media analysis at work. In addition, this paper helps crypto-currencies vendors analyze public opinion in social media platforms. In this sense, the current paper strengthens our understanding of what happens in social media for crypto-currencies. Originality/value For managers and decision-makers, this paper suggests that the news and campaign for their crypto in Twitter would affect people’s perspectives in a good manner. Because of this fact, the firms, investing in these crypto-currencies, could apply the social media as a magnifier for their promotional activities. The findings steer the market managers to see social media as a predictor tool, which can analyze the market through understanding the opinions of users of Twitter.

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Section: Methodsmentioning

confidence: 99%

Crypto-currencies narrated on tweets: a sentiment analysis approach

Rouhani

Abedin

2019

IJOES

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“…It first classifies each word in the sentence as one of the above categories, and then calculates the overall classification of the comment. To account for negation, the package checks whether each word has been preceded by any negative quantifier and if so, adjusts the score accordingly (Bose et al, 2017).…”

Section: Conversation Tone and Sentiment Extremitymentioning

confidence: 99%

Online Engagement Between Opposing Political Protest Groups via Social Media is Linked to Physical Violence of Offline Encounters

Gallacher

Heerdink

Hewstone

2021

Social Media + Society

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The rise of the Internet and social media has allowed individuals with different backgrounds, experiences, and opinions to communicate with one another in an open and largely unstructured way. One important question is whether the nature of online engagements between groups relates to the nature of encounters between these groups in the real world. We analyzed online conversations that occurred between members of protest groups from opposite sides of the political spectrum, obtained from Facebook event pages used to organize upcoming political protests and rallies in the United States and the United Kingdom and the occurrence of violence during these protests and rallies. Using natural language processing and text analysis, we show that increased engagement between groups online is associated with increased violence when these groups met in the real world. The level of engagement between groups taking place online is substantial, and can be characterized as negative, brief, and low in integrative complexity. These findings suggest that opposing groups may use unstructured online environments to engage with one another in hostile ways. This may reflect a worsening of relationships, in turn explaining the observed increases in physical violence offline. These findings raise questions as to whether unstructured online communication is compatible with positive intergroup contact, and highlights the role that the Internet might play in wider issues of extremism and radicalization.

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“…POS tagger assigns a tag [29] to each token and then the word is passed in Sen-tiWordnet to check the score as well as polarity of that particular word. SentiWordnet will return the sentimenttype of the seven possible categories strong-positive, positive, weak-positive, neutral, weak-negative, negative and strong-negative words [30,31]. Now the number of positive (pos_count) and number of negative (neg_count) adjectives in each sentence have been counted.…”

Section: Score Evaluation Methods Using Sentiwordnetmentioning

confidence: 99%

Demonetization and its aftermath: an analysis based on twitter sentiments

Ray¹,

Chakrabarti

Ganguli

et al. 2018

Sādhanā

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Sentiment analysis has become a very useful tool in recent times for studying people's opinions, sentiments and subjective evaluation of any event of social and economic relevance, and in particular, policy decisions. The present paper proposes a framework for sentiment analysis using twitter data for the 'demonetization' effort of the Government of India. The paper employs twitter data using Twitter API. The methodology of the paper involves collection of data from twitter from different cities of India using geolocation and preprocessing followed by a lexicon-based approach to analyse users' sentiments over a period of five weeks preceding the policy announcement. In addition to this, the paper also attempts to analyse the sentiments of specific groups of people representing diverse interest groups.

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RSentiment: A Tool to Extract Meaningful Insights from Textual Reviews

Cited by 15 publications

References 11 publications

Crypto-currencies narrated on tweets: a sentiment analysis approach

Crypto-currencies narrated on tweets: a sentiment analysis approach

Online Engagement Between Opposing Political Protest Groups via Social Media is Linked to Physical Violence of Offline Encounters

Demonetization and its aftermath: an analysis based on twitter sentiments

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