22
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      More than words: Social networks’ text mining for consumer brand sentiments

      Expert Systems with Applications
      Elsevier BV

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references50

          • Record: found
          • Abstract: not found
          • Article: not found

          Lexicon-Based Methods for Sentiment Analysis

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Sentiment strength detection in short informal text

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Twitter mood predicts the stock market

              Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e., can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%.
                Bookmark

                Author and article information

                Journal
                Expert Systems with Applications
                Expert Systems with Applications
                Elsevier BV
                09574174
                August 2013
                August 2013
                : 40
                : 10
                : 4241-4251
                Article
                10.1016/j.eswa.2013.01.019
                dd2be292-aea2-4599-962f-91f711b981c6
                © 2013

                http://www.elsevier.com/tdm/userlicense/1.0/

                History

                Comments

                Comment on this article