Bangla text document categorization using Stochastic Gradient Descent (SGD) classifier

Kabir, Fasihul; Siddique, Sabbir; Kotwal, Mohammed Rokibul Alam; Huda, Mohammad Nurul

doi:10.1109/ccip.2015.7100687

Cited by 84 publications

(27 citation statements)

References 15 publications

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“…This dataset has regular level details from 2019-nCoV on the number of infected cases, deaths and recovery. The dataset has 1086 cases with nineteen (19) The dataset also contained a hashtags.CSV file which contained 26,833,314 hashtags collected over the period starting on March 13th 2020 and ending on March 28 th 2020. There were two fields status_id and the hashtag.…”

Section: Resultsmentioning

confidence: 99%

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Categorizing 2019-n-CoV Twitter Hashtag Data by Clustering

Khan¹,

Ramsahai²

2020

IJAIA

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Unsupervised machine learning techniques such as clustering are widely gaining use with the recent increase in social communication platforms like Twitter and Facebook. Clustering enables the finding of patterns in these unstructured datasets. We collected tweets matching hashtags linked to COVID-19 from a Kaggle dataset. We compared the performance of nine clustering algorithms using this dataset. We evaluated the generalizability of these algorithms using a supervised learning model. Finally, using a selected unsupervised learning algorithm we categorized the clusters. The top five categories are Safety, Crime, Products, Countries and Health. This can prove helpful for bodies using large amount of Twitter data needing to quickly find key points in the data before going into further classification.

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Section: Resultsmentioning

confidence: 99%

“…The cost function gradient of a single sample is calculated at each iteration. The Stochastic Gradient Descent (SGD) classifier [19] essentially incorporates a simple SGD learning routine. Accuracy is simply a ratio of correctly predicted observation to the total observations (Accuracy = TP+TN/TP+FP+FN+TN).…”

Section: Methodsmentioning

confidence: 99%

Categorizing 2019-n-CoV Twitter Hashtag Data by Clustering

Khan¹,

Ramsahai²

2020

IJAIA

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“…The results were improved by incorporating reduced features to SGD. Another application for SGD in literature is for the categorization of Bangla text [34] The results for Bangla text classification was improved using SGD.…”

Section: Stochastic Gradient (Sgd)mentioning

confidence: 99%

Extending the Classifier Algorithms in Machine Learning to Improve the Performance in Spoken Language Understanding Systems Under Deficient Training Data

Jagdale¹,

Shah²

2020

Adv. sci. technol. eng. syst. j.

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One of the open domain challenges for Spoken Dialogue System (SDS) is to maintain a natural conversation for rarely visited domain i.e. domain with fewer data. Spoken Language Understanding (SLU) is a component of SDS that converts user utterance into a semantic form that a computer can understand. If we scale SDS open domain challenge to SLU then it should be able to convert user utterance to a semantic form even if less data is available for the rarest visited domain. The SLU reported in literature incorporate classifiers for the task of identifying the domain of user utterance, understanding the intent of the user, and filling slots-value pair. Thus, to address open domain challenges, classifiers in SLU must be robust to scarce training data. This paper presents investigations to improve the performance of SLU to convert user utterance into semantic form even if less training data is available. Eleven classification algorithms from machine learning have experimented under deficient data. The evaluation matrices used are accuracy, f-score, and inter crossentropy. Comprehensive experimentation is carried out on the two publicly available datasets DSTC2 and DSTC3 were carried out.The accuracy for Support Vector Machine (SVM) , Stochastic Gradient Descent (SGD) and Decision tree are 0.940, 0.960 , 0.955 for DSTC2 and 0.916, 0.900, 0.901 for DSTC3 database respectively. The F-score for SVM, SGD and Decision tree are 0.855, 0.868, 0.849 for DSTC2 dataset and 0.725, 0.715, 0.700 for DSTC3 database, respectively. The ICE for SVM and SGD are 1.191,1.100 for DSTC2 dataset and 3.180,2.999 for DSTC3 database, respectively. The performance of SLU based on SVM and SGD was found to be the best among all. The worst performance in terms of all three evaluation metrics was displayed by SLU incorporating Automatic Relevance Determination (ARD) and Relevance Vector Machine (RVC) classifier.

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“…In our research it is used to predict the value of target variable based on given predictor variables [12]. [14]. It evaluates and updates the coefficients every iteration to minimize the error of a model on the training data.…”

Section: Linear Regressionmentioning

confidence: 99%

Feature-Based Mobile Phone Rating Using Sentiment Analysis and Machine Learning Approaches

Kafi

Alam

Hossain

et al. 2019

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)

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This project proposes a model of sentiment analysis of different features of different company's mobile sets and rating them overall. Customers before buying a phone check reviews to get a better understanding of the device and this project derives an optimum solution for this. In this model, every feature of a mobile phone is rated based on public opinion and an overall rating for every type. Amazon is one of the largest internet retailer, which makes way for most public reviews on their products and so we collected data for sentiment analysis from amazon. We pre-processed the gathered data to a supervised form and chose the most common features from train data. In our model, Naïve Bayes, Support Vector Machine, Logistic Regression, Stochastic Gradient Descent and Random Forest algorithms were used to compare performance. These classification algorithms were trained with the training data and tested with test dataset to determine the accuracy of the classifiers. Our model provides an average polarity of each features and an average polarity of the mobile phone which will give a rating of the device, thus assisting the customers to choose the best according to their desire. This project can work as an assistant for the customers to determine their device following the opinion of the other users of the device. ACKNOWLEDGEMENT Firstly, we would like to thank our Almighty for enabling us to conduct our research, give our best efforts and conclude it. Secondly, we would like to thank our supervisor Hossain Arif Sir for his feedback, support, guidance and contribution in conducting the research and preparation of the report. He encouraged us to conduct the research, guided us and always was present to offer any help we could ask for. We are grateful to him for his excellent supervision and effective guidance to successfully conduct our research. We extend our gratitude to our parents and friends, who helped us with kindness and inspiration and with their suggestions. We would also like to acknowledge our fellow researchers whose informative suggestions and numerous resources aided us to achieve our goal. Last but not the least, we thank BRAC University for providing us the opportunity of conducting this research and for giving us the chance to complete our Bachelor degree.

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Bangla text document categorization using Stochastic Gradient Descent (SGD) classifier

Cited by 84 publications

References 15 publications

Categorizing 2019-n-CoV Twitter Hashtag Data by Clustering

Categorizing 2019-n-CoV Twitter Hashtag Data by Clustering

Extending the Classifier Algorithms in Machine Learning to Improve the Performance in Spoken Language Understanding Systems Under Deficient Training Data

Feature-Based Mobile Phone Rating Using Sentiment Analysis and Machine Learning Approaches

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