Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer

al., Saqib et

doi:10.21833/ijaas.2019.08.004

Cited by 3 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many text classification approaches (Saqib et al, 2019;Wang et al, 2016;Thaoroijam, 2014;Aljaber et al, 2011;Fang et al, 2011;Hliaoutakis et al, 2009;Zhang et al, 2008;Li et al, 2007;Chen et al, 2006;Cohen, 2006;Couto et al, 2004;Kamruzzaman et al, 2005) were proposed for improving the results of classification accuracy and retrieving the relevant documents from database. However, the main problem while performing text classification is managing high dimensional data (Rizaldy and Santoso, 2017;Chandrashekar and Sahin, 2014;Khalid et al, 2014;Javed et al, 2012;Maji and Paul, 2011;Wei and Billings, 2007).…”

Section: Introductionmentioning

confidence: 99%

Review of feature extraction approaches on biomedical text classification

al.¹

2020

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

The overcoming volume of online biomedical literature causes congestion of data and difficulties in organizing these documents and also to retrieve the required documents from the database, especially in the Medline database. One of the solutions to surpass the overwhelming of documents is to apply classification. However, each document must be represented by a set of terminology or feature vectors. The identification of terminology or feature from biomedical literature is one of the most important and challenging tasks in text classification. This is due to a large number of new features and entities that appear in the biomedical domain. In addition, combining sets of features from different terminological resources leads to naming conflicts such as homonymous use of names and terminological ambiguities. Therefore, the purpose of this research is to investigate and evaluate the effective ways for extracting the relevant and meaningful features in order to increase the classification accuracy and improve the performance of web searches. Towards this effort, we conduct several classification experiments to evaluate and compare the effectiveness of feature extraction approaches for extracting the relevant and informative features from the biomedical literature. For our experiments, we use two different sets of features, which are a set of features that are extracted using the Genia tagger tool and set of features that are extracted by medical experts from Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM). The results show the performance of classification using features that are extracted by medical experts outperform the performance of classification using the Genia Tagger tool when applying feature selection method.

show abstract

Section: Introductionmentioning

confidence: 99%

Review of feature extraction approaches on biomedical text classification

al.¹

2020

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

show abstract

MobVGG: Ensemble technique for birds and drones prediction

Muhammad Saqib,

Mazhar,

Iqbal

et al. 2024

Heliyon

View full text Add to dashboard Cite

Evaluation of Deep Learning Approaches for Sentiment Analysis

Saqib,

Naeem,

Ahmad

et al. 2023

VAWKUM trans. comput. sci.

View full text Add to dashboard Cite

Due to the increasing popularity of posting evaluations, sentiment analysis has grown to be a crucial area of study. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and GRU (Gated Recurrent Unit). Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. To discover the optimal deep learning methodology for the given data, authors here proposed many deep learning methodologies for text data on sentiment analysis. A publicly available dataset including both positive and negative reviews on LSTM, CNN, RNN, and GRU was used in the experiments, and the findings showed that CNN had the highest accuracy compared to the other models. Based on the experimental results of CNN, it was found that prediction from the proposed work exhibited a significant improvement over existing work.

show abstract

Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer

Cited by 3 publications

References 29 publications

Review of feature extraction approaches on biomedical text classification

Review of feature extraction approaches on biomedical text classification

MobVGG: Ensemble technique for birds and drones prediction

Evaluation of Deep Learning Approaches for Sentiment Analysis

Contact Info

Product

Resources

About