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

Abstract

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.

Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media

“…Furthermore, we extend the existing literature by presenting multiple techniques for the analysis of brand-related social media data. Most previous studies analysing social media data have focused on a single technique (Kim, Dwivedi, et al 2016;Li and Liu 2017). More importantly, our study responds to the work of Ghiassi et al (2017) and Lansdall-Welfare et al (2016), both of which required the accurate analysis of big data, by presenting more accurate analytical techniques for evaluating customers' opinions, despite the vast amount of unstructured data on the social media platform.…”

“…Hence, this demands more research to offer solutions that address these challenges and fulfil the potentials. Second, most of the previous studies on social media research (Kim, Dwivedi, et al, 2016;Li & Liu, 2017) have focused mainly on one or two techniques. By integrating sentiment analysis, topic modelling, and time series analysis in decoding the sentiment dynamics of online retailing customers, this research introduces distinct approach and contributes to the need for more accurate analysis of customer opinions on the social media (Ghiassi et al, 2017).…”

“…Using a machine learning method, we extract the primary concerns of customers expressed through their tweets to theorise about the causes behind changes in sentiment. This method has been used in previous studies that applied text analytical techniques to obtain richer insights (Daniel, Neves, and Horta 2017;Ghose, Ipeirotis, and Li 2012;Kim, Dwivedi, Zhang, and Jeong 2016;Lee and BradLow 2011). Our analysis incorporates sentiment analysis and topic modelling into time series analysis to gain an understanding of changes in sentiment and the related causal factors.…”

Gender bias in sentiment analysis

“…This bias may occur if the positive aspects of product A are difficult to describe explicitly or if its admirers are from a group that express sentiment less directly, the case that is considered here for gender. For example, a sentiment-based comparison of smartphones (Kim, Dwivedi, Zhang, & Jeong, 2016) might give gender biased results if the system is better at identifying sentiment from one gender than from another, and a system that detects sentiment to help select good ideas (Lee & Suh, 2016) might have a bias towards the opinions of one gender.…”

OpinionML—Opinion Markup Language for Sentiment Representation

“…Existing opinion annotations schemes (i.e., OpinionMining-ML, EmotionML and SentiML) fail to deal with many situations which, if annotated well, could be influential for developing better opinion mining systems. Problems like contextual ambiguities [6,7], lack of semantics interpretation on sentence level, tackling temporal expressions [8,9], identification of opinion holders [10][11][12], opinion aggregation and their comparison [13,14] remain unanswered by these annotations. Each of the opinion annotation schemes have positive and negative features associated with them but there is a need to have a strong opinion annotation which combines positive features of existing schemes (like flexible emotion vocabulary choice in EmotionML, feature-level processing of OpinionMining-ML, etc.)…”