Effects Behind Words: Quantifying Qualitative Information in Corporate Announcements

Liebmann, Michael; Hagenau, Michael; Häußler, Matthias; Neumann, Dirk

doi:10.2139/ssrn.2136880

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Estimates the level of emotional intensity contained in text in order to make a prediction Aiming to predict whether a reviewer recommends a product or not Liebmann et al [22] 2013…”

mentioning

confidence: 99%

Star Ratings versus Sentiment Analysis -- A Comparison of Explicit and Implicit Measures of Opinions

Lak

Turetken

2014

2014 47th Hawaii International Conference on System Sciences

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A typical trade-off in decision making is between the cost of acquiring information and the decline in decision quality caused by insufficient information. Consumers regularly face this trade-off in purchase decisions. Online product/service reviews serve as sources of product/service related information. Meanwhile, modern technology has led to an abundance of such content, which makes it prohibitively costly (if possible at all) to exhaust all available information. Consumers need to decide what subset of available information to use. Star ratings are excellent cues for this decision as they provide a quick indication of the tone of a review. However there are cases where such ratings are not available or detailed enough. Sentiment analysis -text analytic techniques that automatically detect the polarity of text-can help in these situations with more refined analysis. In this study, we compare sentiment analysis results with star ratings in three different domains to explore the promise of this technique.

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“…Estimates the level of emotional intensity contained in text in order to make a prediction Aiming to predict whether a reviewer recommends a product or not Liebmann et al [22] 2013…”

mentioning

confidence: 99%