Supervised classification by thresholds: Application to automated text categorization and opinion mining

2021 ◽  
Author(s):  
Walid Cherif ◽  
Abdellah Madani ◽  
Mohamed Kissi
Keyword(s):  
Supervised Classification ◽  
Text Categorization ◽  
Opinion Mining

A Text Categorization Method Based on SVM and Improved K-Means

2013 ◽  
Vol 427-429 ◽  
pp. 2449-2453
Author(s):  
Rong Ze Xia ◽  
Yan Jia ◽  
Hu Li
Keyword(s):  
Support Vector Machine ◽  
Vector Space ◽  
High Performance ◽  
Supervised Classification ◽  
Text Categorization ◽  
Clustering Algorithm ◽  
Vector Space Model ◽  
Classification Method ◽  
Support Vector ◽  
Space Model

Traditional supervised classification method such as support vector machine (SVM) could achieve high performance in text categorization. However, we should first hand-labeled the samples before classifying. Its a time-consuming task. Unsupervised method such as k-means could also be used for handling the text categorization problem. However, Traditional k-means could easily be affected by several isolated observations. In this paper, we proposed a new text categorization method. First we improved the traditional k-means clustering algorithm. The improved k-means is used for clustering vectors in our vector space model. After that, we use the SVM to categorize vectors which are preprocessed by improved k-means. The experiments show that our algorithm could out-perform the traditional SVM text categorization method.

scholarly journals Performance based Machine Learning Algorithm for Topic Oriented Text Categorization

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3501-3506
Keyword(s):  
Machine Learning ◽  
Text Categorization ◽  
Opinion Mining ◽  
Learning Algorithm ◽  
Data Set ◽  
The Social ◽  
Mining Methods ◽  
Newspaper Data ◽  
F Measure ◽  
The Web

With the growth of societal news on the web, public opinions are given major importance in decision-making. Researchers of text-based mining have made number of evaluations and were diversified using different data mining methods so as to make the conclusions positive, negative and neutral. So, opinions of people are considered to mine the social information as people give superfluous interest to the reports. In this paper the newspaper data set is considered to find the opinion mining to evaluate the sentiment. Sentiment Analysis is used to compute the opinions of people before they judge on a particular issue. Machine Learning is one of the important approaches for analysis of sentiments. Different methods like Naïve Bayes, SVM, Maximum entropy and SLDA are used for classifying the sentiments. Predictions based on precision, f-measure, recall are done to determine which method best suits the classification.

A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization

2014 ◽  
Vol 65 (10) ◽  
pp. 1964-1987 ◽  
Author(s):  
Yindalon Aphinyanaphongs ◽  
Lawrence D. Fu ◽  
Zhiguo Li ◽  
Eric R. Peskin ◽  
Efstratios Efstathiadis ◽  
...  
Keyword(s):  
Feature Selection ◽  
Supervised Classification ◽  
Text Categorization ◽  
Selection Methods ◽  
Empirical Comparison

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

PEMANFAATAN DATA PENGINDERAAN JAUH DAN TEKNOLOGI SIG UNTUK MENGIDENTIFIKASI BUILT UP AREA DAN KAITANNYA DENGAN RTRW DI KABUPATEN KENDAL

2019 ◽  
Vol 3 ◽  
pp. 521
Author(s):  
Mailendra Mailendra
Keyword(s):  
Supervised Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm

Integrasi data penginderaan jauh dengan sistem informasi geografis telah banyak dikembangkan, dan salah satunya dalam melihat perkembangan lahan terbangun. Tujuan penelitian ini adalah untuk melihat perkembangan lahan terbangun dan kesesuaiannya dengan Rencana Pola Ruang Kabupaten Kendal. Kemudian metode yang digunakan yaitu metode supervised classification dengan memanfaatkan data citra landsat 5 TM dan landsat 8 OLI yang selanjutnya dihitung luas dari masing lahan terbangun berdasarkan data temporal tahun 1990, tahun 2015 dan tahun 2017. Setelah diketahui luas lahan terbangun selanjutnya dioverlay dengan peta rencana pola ruang Kabupaten Kendal untuk melihat sesuai atau tidaknya penempatan lahan terbangun tersebut. Adapun hasil penelitiannya yaitu setiap tahunnya lahan terbangun terus meningkat di Kabupaten Kendal, terjadi peningkatan yang cukup signifikan dalam dua tahun terakhir yaitu tahun 2015 hingga tahun 2017. Selanjutnya diperkirakan 88 % lahan terbangun tersebut telah sesuai dengan RTRW karena sudah berada pada kawasan budidaya.

PEMETAAN HABITAT BENTIK INTERTIDAL: Informasi Awal Perencanaan Pengelolaan Pesisir Laut Secara Spasial di Bulak Setra dan Batu Karas, Kabupaten Pangandaran

2018 ◽  
Vol 2 ◽  
pp. 105
Author(s):  
Rendra Pranata
Keyword(s):  
Dissolved Oxygen ◽  
Supervised Classification ◽  
Landsat 7 ◽  
Avicennia Alba

<p>Ekosistem pesisir Kabupaten Pangandaran memiliki biodiversitas yang cukup tinggi, namun pasca-tsunami tahun 2006 terjadi penurunan kerapatan ekosistem mangrove akibat rusaknya daerah pesisir dan wilayah permukiman sepanjang 28 km. Penelitian ini bertujuan untuk mengetahui kondisi habitat bentik di kawasan intertidal seperti mangrove dan makrozoobentos, serta mengukur parameter kualitas air. Metode yang digunakan yaitu interpretasi citra Landsat 7 tahun 2017 dengan melakukan <em>masking</em> dan <em>supervised classification</em> untuk mengetahui daerah tutupan mangrove di Bulak Setra dan Batu Karas, kemudian dilakukan identifikasi mangrove dengan transek kuadran 10x10 meter sepanjang 50 meter ke arah laut pada 7 plot di Bulak Setra dan 14 plot di Batu Karas untuk validasi data citra satelit. Selain itu juga dilakukan pengukuran parameter kualitas air serta identifikasi makrozoobentos. Hasil penelitian menunjukkan bahwa mangrove di Bulak Setra didominasi oleh <em>Scyphiphora hydrophyllacea</em> dari 8 spesies lain yang ditemukan dengan Indeks Nilai Penting (INP) 94,41%, sedangkan di Batu Karas didominasi oleh <em>Avicennia alba</em> dari 8 spesies lain yang ditemukan dengan INP 157%. Nilai rata-rata parameter kualitas air di Bulak Setra dan Batu Karas berturut-turut yaitu suhu 30<sup>o</sup>C dan 29,41<sup>o</sup>C, salinitas 5,56 psu dan 27,23 psu, pH 7,48 dan 6,86 serta konsentrasi <em>Dissolved Oxygen</em> (DO) 5,2 dan 6,5 mg/L. Makrozoobentos didominasi oleh kelas <em>G</em><em>astropoda</em>. Faktor sosial ekonomi masyarakat juga disajikan sebagai informasi sumber daya manusia yang akan berperan menjadi komponen pembangunan pengelolaan pesisir. Diharapkan penelitian ini dapat menjadi informasi awal dalam pengelolaan perencanaan wilayah pesisir di Bulak Setra dan Batu Karas.<strong></strong></p><p><strong>Kata kunci</strong>: bentik, intertidal, mangrove</p>

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