Automatic Sentiment Analysis Using the Textual Pattern Content Similarity in Natural Language

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F10%3A00159734" target="_blank" >RIV/62156489:43110/10:00159734 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Sentiment Analysis Using the Textual Pattern Content Similarity in Natural Language

Popis výsledku v původním jazyce

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 thatcan 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 iscalculated 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.

Název v anglickém jazyce

Automatic Sentiment Analysis Using the Textual Pattern Content Similarity in Natural Language

Popis výsledku anglicky

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 thatcan 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 iscalculated 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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Lecture Notes in Artificial Intelligence

ISSN

0302-9743

e-ISSN

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Svazek periodika

6231

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

8

Strana od-do

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Kód UT WoS článku

288619400029

EID výsledku v databázi Scopus

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