Sentiment Classification of Documents in Serbian: The Effects of Morphological Normalization and Word Embeddings
An open issue in the sentiment classification of texts written in Serbian is the effect of different forms of morphological normalization and the usefulness of leveraging large amounts of unlabeled texts. In this paper, we assess the impact of lemmatizers and stemmers for Serbian on classifiers trai...
Main Authors: | V. Batanović, B. Nikolić |
---|---|
Format: | Article |
Language: | English |
Published: |
Telecommunications Society, Academic Mind
2017-11-01
|
Series: | Telfor Journal |
Subjects: | |
Online Access: |
http://journal.telfor.rs/Published/Vol9No2/Vol9No2_A6.pdf
|
Similar Items
-
A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
by: Qizhi Li, et al.
Published: (2022-10-01) -
Enhancing Accuracy of Semantic Relatedness Measurement by Word Single-Meaning Embeddings
by: Xiaotao Li, et al.
Published: (2021-01-01) -
Refined Global Word Embeddings Based on Sentiment Concept for Sentiment Analysis
by: Yabing Wang, et al.
Published: (2021-01-01) -
Sentiment-Aware Word Embedding for Emotion Classification
by: Xingliang Mao, et al.
Published: (2019-03-01) -
Dual-Word Embedding Model Considering Syntactic Information for Cross-Domain Sentiment Classification
by: Zihao Lu, et al.
Published: (2022-12-01)