. "[02136EC87B13]" . "288619400029" . . . "Automatic Sentiment Analysis Using the Textual Pattern Content Similarity in Natural Language" . . . "Automatic Sentiment Analysis Using the Textual Pattern Content Similarity in Natural Language"@en . "2"^^ . "6231" . "0302-9743" . "The paper investigates a problem connected with automatic analysis of sentiment (opinion) in textual natural-language documents. The initial situation works on the assumption that a user has many documents centered around a certain topic with different opinions of it. The user wants to pick out only relevant documents that represent a certain sentiment -- for example, only positive reviews of a certain subject. Having not too many typical patterns of the desired document type, the user needs a tool that can collect documents which are similar to the patterns. The suggested procedure is based on computing the similarity degree between patterns and unlabeled documents, which are then ranked according to their similarity to the patterns. The similarity is calculated as a distance between patterns and unlabeled items. The results are shown for publicly accessible downloaded real-world data in two languages, English and Czech." . "RIV/62156489:43110/10:00159734!RIV11-MSM-43110___" . . . . "248201" . "2"^^ . "1" . . . "Da\u0159ena, Franti\u0161ek" . . "Z(MSM6215648904)" . "\u017Di\u017Eka, Jan" . . "RIV/62156489:43110/10:00159734" . "Automatic Sentiment Analysis Using the Textual Pattern Content Similarity in Natural Language"@en . . "The paper investigates a problem connected with automatic analysis of sentiment (opinion) in textual natural-language documents. The initial situation works on the assumption that a user has many documents centered around a certain topic with different opinions of it. The user wants to pick out only relevant documents that represent a certain sentiment -- for example, only positive reviews of a certain subject. Having not too many typical patterns of the desired document type, the user needs a tool that can collect documents which are similar to the patterns. The suggested procedure is based on computing the similarity degree between patterns and unlabeled documents, which are then ranked according to their similarity to the patterns. The similarity is calculated as a distance between patterns and unlabeled items. The results are shown for publicly accessible downloaded real-world data in two languages, English and Czech."@en . . "DE - Spolkov\u00E1 republika N\u011Bmecko" . "Automatic Sentiment Analysis Using the Textual Pattern Content Similarity in Natural Language" . "Lecture Notes in Artificial Intelligence" . "textual patterns; natural language; textual document similarity; similarity ranking; sentiment/opinion analysis"@en . "43110" . . . . "8"^^ . .