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We present a novel approach for analysing the qualitative content of annual reports. Using natural language processing techniques we determine if sentiment expressed in the text matters in fraud detection. We focus on the Management Discussion and Analysis (MD&A) section of annual reports because of the nonfactual content present in this section, unlike other components of the annual reports. We measure the sentiment expressed in the text on the dimensions of polarity, subjectivity, and intensity and investigate in depth whether truthful and fraudulent MD&As differ in terms of sentiment polarity, sentiment subjectivity and sentiment intensity. Our results show that fraudulent MD&As on average contain three times more positive sentiment and four times more negative sentiment compared with truthful MD&As. This suggests that use of both positive and negative sentiment is more pronounced in fraudulent MD&As. We further find that, compared with truthful MD&As, fraudulent MD&As contain a greater proportion of subjective content than objective content. This suggests that the use of subjectivity clues such as presence of too many adjectives and adverbs could be an indicator of fraud. Clear cases of fraud show a higher intensity of sentiment exhibited by more use of adverbs in the 'adverb modifying adjective' pattern. Based on the results of this study, frequent use of intensifiers, particularly in this pattern, could be another indicator of fraud. Moreover, the dimensions of subjectivity and intensity help in accurately classifying borderline examples of MD&As (that are equal in sentiment polarity) into fraudulent and truthful categories. When taken together, these findings suggest that fraudulent MD&As in contrast to truthful MD&As contain higher sentiment content. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
