Hybrid Approach to Sentiment Analysis based on Syntactic Analy- sis and Machine Learning

2010 ◽  
Vol 14 (2) ◽  
pp. 159-181
Author(s):  
MUNPYO HONG ◽  
MIYOUNG SHIN ◽  
Shinhye Park ◽  
Hyungmin Lee
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Hybrid Approach

scholarly journals Sentiment Analysis Using Hybrid Approach

2021 ◽  
Vol 9 (12) ◽  
pp. 282-285
Author(s):  
Ganesh K. Shinde
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Algorithm ◽  
Hybrid Approach ◽  
Online Review ◽  
Second Step ◽  
Learning Approach ◽  
Machine Learning Classifiers ◽  
Machine Learning Approach ◽  
Mining Methods

Abstract: Most important part of information gathering is to focus on how people think. There are so many opinion resources such as online review sites and personal blogs are available. In this paper we focused on the Twitter. Twitter allow user to express his opinion on variety of entities. We performed sentiment analysis on tweets using Text Mining methods such as Lexicon and Machine Learning Approach. We performed Sentiment Analysis in two steps, first by searching the polarity words from the pool of words that are already predefined in lexicon dictionary and in Second step training the machine learning algorithm using polarities given in the first step. Keywords: Sentiment analysis, Social Media, Twitter, Lexicon Dictionary, Machine Learning Classifiers, SVM.

scholarly journals Word Embedding and Feature Reduction for Sentiment Analysis Using GA

2020 ◽  
pp. 111-117
Author(s):  
Prajakta P. Shelke ◽  
Ankita N. Korde
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Opinion Mining ◽  
Hybrid Approach ◽  
Feature Reduction ◽  
Word Embedding ◽  
Support Vector ◽  
Svm Classifier

Sentiment analysis (SA), also called as opinion mining is the technique for the removal of opinions of a specific entity or feature from reviews dataset. The opinions of other users help in decision making process of people. This paper studies different methods that are aimed at SA. These approaches vary from semantic based methods, machine learning, neural networks, syntactical methods with each having its own strength. Although hybrid approach also exists where the idea is to combine strengths of two or more methods to increase the accuracy. A framework in which sentiment analysis is done by using word embedding and feature reduction techniques is also proposed. Word embedding is a technique in which low-dimensional vector representation of words is provided. Feature reduction method is used with Support Vector Machine (SVM) classifier. The framework will perform sentiment analysis of user opinions by using a machine learning approach and provides a recommendation system for the ease of decision making for users. The proposed system in this paper has solved the scalability problem and improved the accuracy.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

POLITICAL SENTIMENT ANALYSIS ON DELHI USING MACHINE LEARNING

2020 ◽  
Vol 9 (3) ◽  
pp. 1239-1250
Author(s):  
D. Yadav ◽  
A. Sharma ◽  
S. Ahmad ◽  
U. Chandra
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis
Sign in / Sign up

Export Citation Format

Share Document