Natural Language Processing techniques for feedback on text improvement : A qualitative study on press releases

Detta är en Master-uppsats från KTH/Skolan för elektroteknik och datavetenskap (EECS)

Sammanfattning: Press releases play a key role in today’s news production by being public statements of newsworthy content that function as a pre-formulation of news. Press releases originate from a wide range of actors, and a common goal is for them to reach a high societal impact. This thesis examines how Natural Language Processing (NLP) techniques can be successful in giving feedback to press release authors that help enhance the content and quality of their texts. This could, in turn, contribute to increased impact. To examine this, the research question is divided into two parts. The first part examines how content-perception feedback can contribute to improving press releases. This is examined by the development of a web tool where user- written press releases get analyzed. The analysis consists of a readability assessment using the LIX metric and linguistic bias detection of weasel words and peacock words through rule-based sentiment analysis. The user experiences and opinions are evaluated through an online questionnaire and semi-structured interviews. The second part of the research question examines how trending topic information can contribute to improving press releases. This part is examined theoretically based on a literature review of state-of-the- art methods and qualitatively by gathering opinions from press release authors in the previously mentioned questionnaire and interviews. Based on the results, it is identified that for content-perception feedback, it is especially lesser experienced authors and scientific content aimed at the general public that would achieve improved text quality from objective readability assessment and detection of biased expressions. Nevertheless, most of the evaluation participants were more satisfied with their press releases after editing based on the readability feedback, and all participants with biased words in their texts reported that the detection led to positive changes resulting in improved text quality. As for the theoretical part, it is considered that both text quality and the number of publications increase when writing about trending topics. To give authors trending topic information on a detailed level is indicated to be the most helpful. 

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