scholarly journals Sentiment Analysis of Roman-Urdu Tweets about Covid-19 Using Machine Learning Approach: A Systematic Literature Review

2021 ◽  
Vol 10 (2) ◽  
pp. 1112-1120
Keyword(s):  
Sentiment Analysis ◽  
English Language ◽  
Human Life ◽  
Morphological Structure ◽  
Learning Approach ◽  
The Past ◽  
Twitter Data ◽  
Machine Learning Approach ◽  
Effective Decision ◽  
Effective Decision Making

From the past decade, growth of social media encourages researchers to perform several academic studies on the user generated data. Sentiment Analysis is tool used to generate summary of public opinion about a topic, which helps in effective decision making. Most of the work in sentiment analysis has been performed on English Language Dataset. Many Languages such as Urdu, Hindi, and Italian faced lack of attention due to their complex morphological structure and unavailability of resources. COVID-19 has affected human life around the world. Many researchers have performed their studies using twitter data for knowing the sentiments of people throughout the time of pandemic. In this Paper we are giving a general overview about the process of Sentiment Analysis of Roman-Urdu Tweets. For the dataset query we have used the data generated by user about COVID19.

An historical analysis of species references in American English

Corpora ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. 327-349
Author(s):  
Craig Frayne
Keyword(s):  
Environmental Change ◽  
Sentiment Analysis ◽  
Quantitative Methods ◽  
English Language ◽  
Language Use ◽  
American English ◽  
Historical Analysis ◽  
The Past ◽  
Corpus Studies ◽  
Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

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 for Twitter Data

2020 ◽  
Vol 9 (7S) ◽  
pp. 98-101
Keyword(s):  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Daily Basis ◽  
Support Vector ◽  
Twitter Data ◽  
Internet Users ◽  
Political Views ◽  
Large Numbers ◽  
Machine Learning Approach

With the advancements in web technology and its growth, there's an incredible volume of information present everywhere on the net for internet users and plenty more data is generated on a daily basis. Internet emerged as place for exchanging ideas, sharing opinions, online learning and political views. Social networking sites such as Facebook, Twitter, are rapidly growing as the users are allowed to post and revel their views on various topics, and can discussion with different groups and communities, or post messages across the world. In the area of sentiment analysis large numbers of researchers are working. The main focus is on twitter data for sentiment analysis, that's helpful to research the info within the tweets,where opinions are heterogeneous, highly unstructured, and are either positive,or negative, or neutral.in many cases. In this paper, we provide a study and comparative analysis of existing techniques used for opinion mining through machine learning approach. Naive Bayes & Support Vector Machine, we provide research on twitter data.

scholarly journals Rain Prediction Using Rule-Based Machine Learning Approach

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Muchamad Taufiq Anwar ◽  
Saptono Nugrohadi ◽  
Vita Tantriyati ◽  
Vikky Aprelia Windarni
Keyword(s):  
Machine Learning ◽  
Natural Disasters ◽  
Human Life ◽  
Meteorological Data ◽  
Training Model ◽  
Weather Data ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
The World

Rain prediction is an important topic that continues to gain attention throughout the world. The rain has a big impact on various aspects of human life both socially and economically, for example in agriculture, health, transportation, etc. Rain also affects natural disasters such as landslides and floods. The various impact of rain on human life prompts us to build a model to understand and predict rain to provide early warning in various fields/needs such as agriculture, transportation, etc. This research aims to build a rain prediction model using a rule-based Machine Learning approach by utilizing historical meteorological data. The experiment using the J48 method resulted in up to 77.8% accuracy in the training model and gave accurate prediction results of 86% when tested against actual weather data in 2020.

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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