Sentiment Analysis of Multilingual Mixed-Code, Twitter Data Using Machine Learning Approach

2021 ◽  
pp. 683-697
Author(s):  
Sowmya Swamy ◽  
Jyoti Kundale ◽  
Dipti Jadhav
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Approach ◽  
Twitter Data ◽  
Machine Learning Approach ◽  
Mixed Code

scholarly journals Twitter Sentiment Analysis on Coronavirus: Machine Learning Approach

2021 ◽  
Vol 1828 (1) ◽  
pp. 012104
Author(s):  
Cristian R. Machuca ◽  
Cristian Gallardo ◽  
Renato M. Toasa
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Approach ◽  
Machine Learning 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 Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

2021 ◽  
Vol 1 (1) ◽  
pp. 57-70
Author(s):  
Ranjan Raj Aryal ◽  
Ankit Bhattarai
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Other ◽  
Learning Approach ◽  
Asian Countries ◽  
Vaccination Programs ◽  
Machine Learning Approach ◽  
Positive Sentiment

Social media is one platform where people share their opinions and views on different topics, services, or behaviors that happen around them. Since the COVID19 pandemic that started at the end of 2019, it has been a topic on which people express their sentiments. Recently, the COVID19 vaccination programs have got a lot of responses. In this paper, we have proposed two models: one based on the machine learning approach: Naive Bayes & the other based on deep learning: LSTM, whose goal is to know the sentiment of Asian region tweets towards the vaccine through sentiment analysis. The data were extracted with the help of Twitter API from March 23, 2021, till April 2, 2021. The extraction approach contains keywords with geocoding of some of the Asian countries, especially Nepal, India and Singapore. After collecting data, some preprocessing such as removing numbers, non-English & stop words, removing special characters, and hyperlinks were done. The polarity of tweets was assigned using the Text blob library. The tweets were classified into one of the three: positive, negative, or neutral. Now the data were preprocessed with the splitting of tweets into training & testing sets. Both the models were trained & tested using 10767 unique tweets. This experiment shows that a number of people in these three countries (Nepal, India and Singapore) have positive sentiment towards the vaccine and are taking the first dose of Covid19 vaccine. At last, the accuracy of the LSTM model was found to be 7% greater than that of the Naive Bayes-based model.

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