scholarly journals IMPROVED SENTIMENT ANALYSIS FOR DRAVIDIAN LANGUAGE-KANNADA USING DICISION TREE ALGORITHM WITH EFFICIENT DATA DICTIONARY

2021 ◽  
Vol 1123 (1) ◽  
pp. 012039
Author(s):  
P Ranjitha ◽  
K N Bhanu
Keyword(s):  
Sentiment Analysis ◽  
Data Dictionary ◽  
Tree Algorithm ◽  
Efficient Data ◽  
Dravidian Language

52A Efficient data modeling with a device independent data dictionary

1994 ◽  
Vol 15 (3) ◽  
pp. 68
Author(s):  
R. Brandmaier ◽  
J. Becher ◽  
T. Kapsner
Keyword(s):  
Data Modeling ◽  
Data Dictionary ◽  
Independent Data ◽  
Efficient Data

scholarly journals SENTIMENT ANALYSIS ON TWITTER OF PSBB EFFECT USING MACHINE LEARNING

2020 ◽  
Vol 17 (2) ◽  
pp. 143-150
Author(s):  
Irwansyah Saputra ◽  
Jose Andrean Halomoan ◽  
Adam Bagusmugi Raharjo ◽  
Cyra Rezky Ananda Syavira
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Sentiment Analysis ◽  
Bayes Classifier ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Processing Data ◽  
Twitter Users ◽  
Using Data

A collection of tweets from Twitter users about PSBB can be used as sentiment analysis. The data obtained is processed using data mining techniques (data mining), in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier to find the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study were the Decision Tree algorithm with an accuracy of 83.3%, precision 79%, and recall 87.17%.

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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