As Bifet and Frank explain in their paper Sentiment Knowledge Discovery in Twitter Streaming Data[i], Twitter is a:

    “potentially valuable source of data that can be used to delve into the thoughts of millions of people as they are uttering them. Twitter makes these utterances immediately available in a data stream, which can be mined for information by using appropriate stream mining techniques. In principle, this could make it possible to infer people’s opinions, both at an individual level as well as in aggregate, regarding potentially any subject or event.”

    Services offered by companies like Rival iQ[ii] can track a list of brands of one’s choosing and monitor their activity on Facebook, Twitter, and Google. Rival IQ could not only provide insight into an IR’s competitor, but also insight into an industry as a whole. For instance, IR’s could learn from the “Day of the Week” chart when content from the casino and hospitality industry is most likely to go viral.

    Buzz Sumo[iii] also has a search tool that tracks the most popular content on any given topic or website and ranks it according to shares on Facebook, Twitter, LinkedIn, and Google. Later in this chapter, I will discuss the importance of sentiment and influencers.

    As Bifet and Frank note, “There are also a number of interesting tasks that have been tackled using Twitter text mining: sentiment analysis, classification of tweets into categories, clustering of tweets and trending topic detection. Considering sentiment analysis.”194 O’Connor et al. found that surveys of consumer confidence correlate with sentiment word frequencies in tweets, and they proposed text stream mining as a substitute for traditional polling.[iv] Jansen et al. discuss the implications for organizations of using micro-blogging as part of their marketing strategy. Free sentiment analysis services like twittersentiment.appspot.com can be used to analyze a company’s sentiment.

     

    [i] Bifet, A. a. (2010). Sentiment knowledge discovery in twitter streaming data. Retrieved from University of Waikato, Hamilton, New Zealand: http://www.cs.waikato.ac.nz/~ml/publications/2010/Twitter-crc.pdf

    [ii] https://www.rivaliq.com

    [iii] http://buzzsumo.com

    [iv] . O’Connor, B., Balasubramanyan, R., Routledge, B. R. and Smith, N. A. From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the International AAAI Conference on Weblogs and Social Media, pages 122–129, 2010.

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