Opinion Mining on Social Media Data

The ready access to and availability of social media has opened up a wealth of data that marketers are leveraging for strategic insight and digital marketing. Yet there is a lack of professional norms regarding the use of social media in marketing and a gap in understanding consumers’ comfort with marketers’ use of their social media data. This study analyzes a census-balanced sample of online adults (n=751) to identify consumers’ perceptions of using social media data for marketing purposes. The research finds that consumers’ perceived risks and benefits of using social media have a relationship with their comfort with marketers using their publicly available social media data. The research extends the applicability of communication privacy management theory to social media and introduces marketing comfort—a new construct of high importance for future marketing research. Marketing comfort refers to an individual’s comfort with the use of information posted publicly on social media for targeted advertising, customer relations, and opinion mining. In the context of the construct development, we find that targeted advertising is the strongest contributing component to marketing comfort, relative to the other two dimensions: opinion mining and customer relations. By understanding what drives consumer comfort with this emerging marketing practice, the research proposes strategies for marketers that can support and mitigate consumers’ concerns so that consumers can maintain trust in marketers’ digital practice

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Social media marketing: who is watching the watchers?

10.32920/14636028 ◽

2021 ◽

Author(s):

Jenna Jacobson ◽

Anatoliy Gruzd ◽

Angel Hernandez-Garcia

Keyword(s):

Social Media ◽

Opinion Mining ◽

Customer Relations ◽

Marketing Research ◽

Two Dimensions ◽

Marketing Practice ◽

Social Media Data ◽

Targeted Advertising ◽

Use Of Social Media ◽

Media Data

The ready access to and availability of social media has opened up a wealth of data that marketers are leveraging for strategic insight and digital marketing. Yet there is a lack of professional norms regarding the use of social media in marketing and a gap in understanding consumers’ comfort with marketers’ use of their social media data. This study analyzes a census-balanced sample of online adults (n=751) to identify consumers’ perceptions of using social media data for marketing purposes. The research finds that consumers’ perceived risks and benefits of using social media have a relationship with their comfort with marketers using their publicly available social media data. The research extends the applicability of communication privacy management theory to social media and introduces marketing comfort—a new construct of high importance for future marketing research. Marketing comfort refers to an individual’s comfort with the use of information posted publicly on social media for targeted advertising, customer relations, and opinion mining. In the context of the construct development, we find that targeted advertising is the strongest contributing component to marketing comfort, relative to the other two dimensions: opinion mining and customer relations. By understanding what drives consumer comfort with this emerging marketing practice, the research proposes strategies for marketers that can support and mitigate consumers’ concerns so that consumers can maintain trust in marketers’ digital practice

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Competitive intelligence in social media Twitter: iPhone 6 vs. Galaxy S5

Online Information Review ◽

10.1108/oir-03-2015-0068 ◽

2016 ◽

Vol 40 (1) ◽

pp. 42-61 ◽

Cited By ~ 17

Author(s):

Yoosin Kim ◽

Rahul Dwivedi ◽

Jie Zhang ◽

Seung Ryul Jeong

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Purchase Intention ◽

Opinion Mining ◽

Sales Performance ◽

Competitive Intelligence ◽

Social Media Data ◽

Content Type ◽

The Social ◽

Media Data

Purpose – The purpose of this paper is to mine competitive intelligence in social media to find the market insight by comparing consumer opinions and sales performance of a business and one of its competitors by analyzing the public social media data. Design/methodology/approach – An exploratory test using a multiple case study approach was used to compare two competing smartphone manufacturers. Opinion mining and sentiment analysis are conducted first, followed by further validation of results using statistical analysis. A total of 229,948 tweets mentioning the iPhone6 or the GalaxyS5 have been collected for four months following the release of the iPhone6; these have been analyzed using natural language processing, lexicon-based sentiment analysis, and purchase intention classification. Findings – The analysis showed that social media data contain competitive intelligence. The volume of tweets revealed a significant gap between the market leader and one follower; the purchase intention data also reflected this gap, but to a less pronounced extent. In addition, the authors assessed whether social opinion could explain the sales performance gap between the competitors, and found that the social opinion gap was similar to the shipment gap. Research limitations/implications – This study compared the social media opinion and the shipment gap between two rival smart phones. A business can take the consumers’ opinions toward not only its own product but also toward the product of competitors through social media analytics. Furthermore, the business can predict market sales performance and estimate the gap with competing products. As a result, decision makers can adjust the market strategy rapidly and compensate the weakness contrasting with the rivals as well. Originality/value – This paper’s main contribution is to demonstrat the competitive intelligence via the consumer opinion mining of social media data. Researchers, business analysts, and practitioners can adopt this method of social media analysis to achieve their objectives and to implement practical procedures for data collection, spam elimination, machine learning classification, sentiment analysis, feature categorization, and result visualization.

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Research on the Opinion Mining System for Massive Social Media Data

Communications in Computer and Information Science - Natural Language Processing and Chinese Computing ◽

10.1007/978-3-642-41644-6_42 ◽

2013 ◽

pp. 424-431

Author(s):

Lijun Zhao ◽

Yingjie Ren ◽

Ju Wang ◽

Lingsheng Meng ◽

Cunlu Zou

Keyword(s):

Social Media ◽

Opinion Mining ◽

Mining System ◽

Social Media Data ◽

Media Data

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Mining Public Opinion about Economic Issues

International Journal of Strategic Decision Sciences ◽

10.4018/ijsds.2018010102 ◽

2018 ◽

Vol 9 (1) ◽

pp. 18-28 ◽

Cited By ~ 20

Author(s):

Amir Karami ◽

London S. Bennett ◽

Xiaoyun He

Keyword(s):

Social Media ◽

Public Opinion ◽

Text Mining ◽

Opinion Mining ◽

Social Media Data ◽

Economic Issues ◽

Election Prediction ◽

Us Presidential Election ◽

Mining Methods ◽

Media Data

Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This article proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.

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