Classification of sentiment reviews using n-gram machine learning approach

Channel:
Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
Subscribers:
22,300
Published on ● Video Link: https://www.youtube.com/watch?v=nrIMRGkrMQg


Category:
Review
Duration: 5:14
3,466 views
271

5-min ML Paper Challenge

Classification of sentiment reviews using n-gram machine learning approach
https://www.sciencedirect.com/science/article/pii/S095741741630118X

With the ever increasing social networking and online marketing sites, the reviews and blogs obtained from those, act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like classification or clustering to provide a meaningful information for future uses. These reviews and blogs may be classified into different polarity groups such as positive, negative, and neutral in order to extract information from the input dataset. Supervised machine learning methods help to classify these reviews. In this paper, four different machine learning algorithms such as Naive Bayes (NB), Maximum Entropy (ME), Stochastic Gradient Descent (SGD), and Support Vector Machine (SVM) have been considered for classification of human sentiments. The accuracy of different methods are critically examined in order to access their performance on the basis of parameters such as precision, recall, f-measure, and accuracy.



Other Videos By LLMs Explained - Aggregate Intellect - AI.SCIENCE


2019-06-05State of Natural Language Processing in 2019 | AISC
2019-06-03Superposition of Many Models into One | AISC
2019-05-27Learning Discrete Structures for Graph Neural Networks | AISC
2019-05-23Near-optimal Evasion of Randomized Convex-inducing Classifiers in Adversarial Environments | AISC
2019-05-21Comparative Document Summarisation via Classification | AISC
2019-05-13Sparse Transformers and MuseNet | AISC
2019-05-09Content Tree Word Embedding for document representation | AISC
2019-05-06Deep Temporal Logistic Bag-of-Features For Forecasting High Frequency Limit Order Book Time Series
2019-05-02A Web-scale system for scientific knowledge exploration | AISC
2019-05-02Convolutional Neural Networks for processing EEG signals
2019-05-02Classification of sentiment reviews using n-gram machine learning approach
2019-05-02Introduction to the Conditional GAN - A General Framework for Pixel2Pixel Translation
2019-05-02A Style-Based Generator Architecture for Generative Adversarial Networks
2019-05-02A Framework for Developing Deep Learning Classification Models
2019-05-02Revolutionizing Diet and Health with CNN's and the Microbiome
2019-05-02Efficient implementation of a neural network on hardware using compression techniques
2019-05-02Supercharging AI with high performance distributed computing
2019-05-02Combining Satellite Imagery and machine learning to predict poverty
2019-05-02Revolutionary Deep Learning Method to Denoise EEG Brainwaves
2019-04-25[LISA] Linguistically-Informed Self-Attention for Semantic Role Labeling | AISC
2019-04-23How goodness metrics lead to undesired recommendations


Tags:
deep learning
machine learning
Sentiment Analysis
Machine learning
NLP
Data Science
Movie data-set
Prediction
Accuracy.
support vector machine
N gram technique
count vectorization