Grammatical Relations Identification of Korean Parsed Texts Using Support Vector Machines

Lee, Songwook; Seo, Jungyun

doi:10.1007/978-3-540-30120-2_16

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…Expression (3.1) can be positive or negative depending on the context in which it is used. In the case of expression (4.1), each of the contextual words, "elyep" ("difficult") and "himtul" ("hard"), functions as a polarity shifter 6) although they themselves are sentiment words. One of the interesting findings in Example (3) is the difference between the expressions (3.2) and (3.3).…”

Section: Resultant Lexiconmentioning

confidence: 99%

“…Example (4) presents a list of expressions that include "mit" ("believe" or "trust"), which has positive polarity. In the case of expression (4.1), each of the contextual words, "elyep" ("difficult") and "himtul" ("hard"), functions as a polarity shifter 6) although they themselves are sentiment words. The expression (4.2), a proverb extracted from the corpus, expresses negative sentiment without an additional sentiment word or a polarity shifter.…”

Section: Resultant Lexiconmentioning

confidence: 99%

“…Additional analysis is conducted using a Korean dependency parser [6], [8] to identify relations between the words in a sentence. The posted text also contains slang, jargon, argot, taboo expressions, expletives, and misused words.…”

Section: A Preprocessingmentioning

confidence: 99%

“…With the tagging process completed, each sentence in the review has undergone morphological analysis to find the stem of each word. Additional analysis is conducted using a Korean dependency parser [6], [8] to identify relations between the words in a sentence.…”

Section: A Preprocessingmentioning

confidence: 99%

See 3 more Smart Citations

Extracting Multiword Sentiment Expressions by Using a Domain-Specific Corpus and a Seed Lexicon

Lee¹

2013

ETRI J

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This paper presents a novel approach to automatically generate Korean multiword sentiment expressions by using a seed sentiment lexicon and a large‐scale domain‐specific corpus. A multiword sentiment expression consists of a seed sentiment word and its contextual words occurring adjacent to the seed word. The multiword sentiment expressions that are the focus of our study have a different polarity from that of the seed sentiment word. The automatically extracted multiword sentiment expressions show that 1) the contextual words should be defined as a part of a multiword sentiment expression in addition to their corresponding seed sentiment word, 2) the identified multiword sentiment expressions contain various indicators for polarity shift that have rarely been recognized before, and 3) the newly recognized shifters contribute to assigning a more accurate polarity value. The empirical result shows that the proposed approach achieves improved performance of the sentiment analysis system that uses an automatically generated lexicon.

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Section: Resultant Lexiconmentioning

confidence: 99%

Section: Resultant Lexiconmentioning

confidence: 99%