An Ensemble Multi-Layered Sentiment Analysis Model (EMLSA) for Classifying the Complex Datasets

MLSA - A Multi-layered Reference Corpus for German Sentiment Analysis

In this paper, we describe MLSA, a publicly available multi-layered reference corpus for German-language sentiment analysis. The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity. The sentence-layer annotation, as the most coarse-grained annotation, focuses on aspects of objectivity, subjectivity and t...

A Pre-Trained Ensemble Model for Breast Cancer Grade Detection Based on Small Datasets

Background and Purpose: Nowadays, breast cancer is reported as one of the most common cancers amongst women. Early detection of the cancer type is essential to aid in informing subsequent treatments. The newest proposed breast cancer detectors are based on deep learning. Most of these works focus on large-datasets and are not developed for small datasets. Although the large datasets might lead ...

Ensemble classifier for Twitter sentiment analysis

In this paper, we present a combination of different types of sentiment analysis approaches in order to improve the individual performance of them. These ones consist of (I) ranking algorithms for scoring sentiment features as bi-grams and skip-grams extracted from annotated corpora; (II) a polarity classifier based on a deep learning algorithm; and (III) a semi-supervised system founded on the...

Sentiment analysis: Bayesian Ensemble Learning

Webis: An Ensemble for Twitter Sentiment Detection

We reproduce four Twitter sentiment classification approaches that participated in previous SemEval editions with diverse feature sets. The reproduced approaches are combined in an ensemble, averaging the individual classifiers’ confidence scores for the three classes (positive, neutral, negative) and deciding sentiment polarity based on these averages. The experimental evaluation on SemEval da...