Opinion to Emotion Mining: A Sentiment Analysis towards Super Typhoon Ompong

2021 ◽  
Author(s):  
Albert Vinluan ◽  
Mamerto Goneda ◽  
Francis Arlando L. Atienza ◽  
John Paul P. Miranda ◽  
Rolando R. Fajardo ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Super Typhoon ◽  
Emotion Mining

Sentiment Analysis, Emotion Mining & Authentication Methods in Hadoop: A Survey of Approaches

IJARCCE ◽  
2017 ◽  
Vol 6 (3) ◽  
pp. 308-313
Author(s):  
Sagar S. Patil ◽  
Pravin S. Game
Keyword(s):  
Sentiment Analysis ◽  
Emotion Mining

Emotion Mining and Sentiment Analysis in Software Engineering Domain

2018 ◽  
Author(s):  
Arvinder Kaur ◽  
Amrit Pal Singh ◽  
Guneet Singh Dhillon ◽  
Divesh Bisht
Keyword(s):  
Software Engineering ◽  
Sentiment Analysis ◽  
Emotion Mining

Current State of Text Sentiment Analysis from Opinion to Emotion Mining

2017 ◽  
Vol 50 (2) ◽  
pp. 1-33 ◽  
Author(s):  
Ali Yadollahi ◽  
Ameneh Gholipour Shahraki ◽  
Osmar R. Zaiane
Keyword(s):  
Sentiment Analysis ◽  
Current State ◽  
Text Sentiment Analysis ◽  
Emotion Mining

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.

A Contemporary Sentiment Analysis Approach: Algorithm-Based Analysis of News Items within the Direct Real Estate Market in the US

2017 ◽  
Author(s):  
Jessica Ruscheinsky ◽  
Jochen Hausler ◽  
Marcel Lang
Keyword(s):  
Real Estate ◽  
Sentiment Analysis ◽  
Real Estate Market ◽  
Analysis Approach ◽  
The Us
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