國立虎尾科技大學 |

The Role of Approximate Negators in Modeling the Automatic Detection of Negation in Tweets.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	The Role of Approximate Negators in Modeling the Automatic Detection of Negation in Tweets./
作者:	Palomino, Norma.
面頁冊數:	1 online resource (203 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Contained By:	Dissertation Abstracts International79-11B(E).
標題:	Artificial intelligence. -
電子資源:	click for full text (PQDT)
ISBN:	9780438102101

The Role of Approximate Negators in Modeling the Automatic Detection of Negation in Tweets.
Palomino, Norma.

The Role of Approximate Negators in Modeling the Automatic Detection of Negation in Tweets. - 1 online resource (203 pages)

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.

Thesis (D.P.S.)--Syracuse University, 2018.

Includes bibliographical references

Although improvements have been made in the performance of sentiment analysis tools, the automatic detection of negated text (which affects negative sentiment prediction) still presents challenges. More research is needed on new forms of negation beyond prototypical negation cues such as "not" or "never." The present research reports findings on the role of a set of words called "approximate negators," namely "barely," "hardly," "rarely," "scarcely," and "seldom," which, in specific occasions (such as attached to a word from the non-affirmative adverb "any" family), can operationalize negation styles not yet explored. Using a corpus of 6,500 tweets, human annotation allowed for the identification of 17 recurrent usages of these words as negatives (such as "very seldom") which, along with findings from the literature, helped engineer specific features that guided a machine learning classifier in predicting negated tweets. The machine learning experiments also modeled negation scope (i.e. in which specific words are negated in the text) by employing lexical and dependency graph information. Promising results included F1 values for negation detection ranging from 0.71 to 0.89 and scope detection from 0.79 to 0.88. Future work will be directed to the application of these findings in automatic sentiment classification, further exploration of patterns in data (such as part-of-speech recurrences for these new types of negation), and the investigation of sarcasm, formal language, and exaggeration as themes that emerged from observations during corpus annotation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438102101Subjects--Topical Terms:

559380
Artificial intelligence.
Index Terms--Genre/Form:

554714
Electronic books.

The Role of Approximate Negators in Modeling the Automatic Detection of Negation in Tweets.
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