Emotion detection in suicide notes

A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

“…Applications of SA 21 [13], [18], [43], [47], [49], [51], [53], [58], [64], [73], [77], [79], [80], [90], [91], [124], [131], [155], [158], [183], [184] Therefore, results of different studies are not directly comparable. Still, we tried our best to compare some related works wherever possible.…”

A rule-based approach to emotion cause detection for Chinese micro-blogs

“…Perhaps some of them are full of emotions and sentiments. As a result, many researchers in the field of natural language processing pay more attention to Chinese micro-blog textual emotion processing (Liu, 2012), especially the emotion classification (Desmet & Hoste, 2013;Huang, Peng, Li, & Lee, 2013;Kontopoulos, Berberidis, Dergiades, & Bassiliades, 2013;Yang & Yu, 2013), and the corresponding social relations (Hu, Tang, Tang, & Liu, 2013). Generally, individual emotion generation, expression and perception are influenced by many factors.…”

Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

“…Emotions in text may be expressed explicitly (for example, emoticons and lexicon) (Fukushima et al, 2008;Loia and Senatore, 2013;Ptaszynski et al, 2013) as well as implicitly (Balahur et al, 2012;Lau et al, 2014;Wang et al, 2013b). Affective computing enables companies to care more about their customers (Bagheri et al, 2013) and is useful for market prediction (Lassen et al, 2014;Li and Li, 2013;Milea et al, 2012;Nassirtoussi et al, 2014;Zhang et al, 2009), assists in diagnosing patients' suicidal levels (Desmet and Hoste, 2013;Pestian et al, 2010a;2010b) and allows the related parties to gauge public perception towards events (Loia and Senatore, 2013;Moreo et al, 2012). The advancements in affective computing allow applications to sense and deliver services tailored to customer needs, but issues such as privacy need to be observed.…”

“…They found that the presence of unique words in subjective text is higher than in an objective document. Desmet and Hoste (2013) utilised lexical features, such as length of sentence, for the classification of feedback surveys. Lexical style markers (words per message and words per sentence) were used in Cambria et al (2011) to analyse web blogs.…”