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Today, people abundantly express and share emotions through social media. Technological advancements in such platforms enable sharing opinions or expressing any specific emotions towards what others have shared, mainly in the form of textual data. This entails an interesting arena for analysis; as to whether there is a disconnect between the writer\u2019s intended emotion and the reader\u2019s perception of textual content. In this paper, we present experiments for Readers\u2019 Emotion Detection through multi-target regression settings by exploring a Bi-LSTM-based Attention model, where our major intention is to analyze the interpretability and effectiveness of the deep learning model for the task. To conduct experiments, we procure two extensive datasets REN-10k and RENh-4k, apart from using a popular benchmark dataset from SemEval-2007. We perform a two-phase experimental evaluation, first being various coarse-grained and fine-grained evaluations of our<jats:italic>model performance<\/jats:italic>in comparison with several baselines belonging to different categories of emotion detection, viz., deep learning, lexicon based, and classical machine learning. Secondly, we evaluate<jats:italic>model behavior<\/jats:italic>towards readers\u2019 emotion detection assessing attention maps generated by the model through devising a novel set of qualitative and quantitative metrics. The first phase of experiments shows that our Bi-LSTM + Attention model significantly outperforms all baselines. The second analysis reveals that emotions may be correlated to specific words as well as named entities.<\/jats:p>","DOI":"10.1186\/s40537-022-00614-2","type":"journal-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T15:03:55Z","timestamp":1655737435000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Readers\u2019 affect: predicting and understanding readers\u2019 emotions with deep learning"],"prefix":"10.1186","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4335-5544","authenticated-orcid":false,"given":"Anoop","family":"K.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1336-2356","authenticated-orcid":false,"given":"Deepak","family":"P.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3902-2867","authenticated-orcid":false,"given":"Savitha","family":"Sam Abraham","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8897-3936","authenticated-orcid":false,"given":"Lajish","family":"V. 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