XRCE: Hybrid Classification for Aspect-based Sentiment Analysis

2014 ◽  
Author(s):  
Caroline Brun ◽  
Diana Nicoleta Popa ◽  
Claude Roux
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Classification

scholarly journals Hybrid Classification Technique for Sentiment Analysis of the Twitter Data

2019 ◽  
Vol 8 (10) ◽  
pp. 1750-1753
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Research Work ◽  
Related Data ◽  
Twitter Data ◽  
Opinion Extraction ◽  
Social Media Platforms ◽  
Hybrid Classification ◽  
Media Platform

Sentiment can be described in the form of any type of approach, thought or verdict which results because of the occurrence of certain emotions. This approach is also known as opinion extraction. In this approach, emotions of different peoples with respect to meticulous rudiments are investigated. For the attainment of opinion related data, social media platforms are the best origins. Twitter may be recognized as a social media platform which is socially accessible to numerous followers. When these followers post some message on twitter, then this is recognized as tweet. The sentiment of twitter data can be analyzed with the feature extraction and classification approach. The hybrid classification is designed in this work which is the combination of KNN and random forest. The KNN classifier extract features of the dataset and random forest will classify data. The approach of hybrid classification is applied in this research work for the sentiment analysis. The performance of the proposed model is tested in terms of accuracy and execution time.

T-SAF: Twitter sentiment analysis framework using a hybrid classification scheme

2017 ◽  
Vol 35 (1) ◽  
pp. e12233 ◽  
Author(s):  
Muhammad Zubair Asghar ◽  
Fazal Masud Kundi ◽  
Shakeel Ahmad ◽  
Aurangzeb Khan ◽  
Furqan Khan
Keyword(s):  
Sentiment Analysis ◽  
Classification Scheme ◽  
Analysis Framework ◽  
Hybrid Classification

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

An historical analysis of species references in American English

Corpora ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. 327-349
Author(s):  
Craig Frayne
Keyword(s):  
Environmental Change ◽  
Sentiment Analysis ◽  
Quantitative Methods ◽  
English Language ◽  
Language Use ◽  
American English ◽  
Historical Analysis ◽  
The Past ◽  
Corpus Studies ◽  
Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

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