Feeling the Void: Lack of Support for Isolation and Sleep Difficulties in Pregnant Women during the COVID-19 Pandemic Revealed by Twitter Data Analysis

Pregnant women face many physical and psychological changes during their pregnancy. It is known that stress, caused by many factors and life events such as the COVID-19 pandemic, can negatively impact the health of mothers and offspring. It is the first time social media, such as Twitter, are available and commonly used during a global pandemic; this allows access to a rich set of data. The objective of this study was to characterize the content of an international sample of tweets related to pregnancy and mental health during the first wave of COVID-19, from March to June 2020. Tweets were collected using GetOldTweets3. Sentiment analysis was performed using the VADER sentiment analysis tool, and a thematic analysis was performed. In total, 192 tweets were analyzed: 51 were from individuals, 37 from companies, 56 from non-profit organizations, and 48 from health professionals/researchers. Findings showed discrepancies between individual and non-individual tweets. Women expressed anxiety, depressive symptoms, sleeping problems, and distress related to isolation. Alarmingly, there was a discrepancy between distress expressed by women with isolation and sleep difficulties compared to support offered by non-individuals. Concrete efforts should be made to acknowledge these issues on Twitter while maintaining the current support offered.

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Sentiment Analysis of Movies on Social Media using R Studio

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8586.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 2171-2175

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Media Analysis ◽

Literature Study ◽

Social Media Analysis ◽

Customer Feedback ◽

Twitter Data ◽

User Data ◽

Market Trends ◽

Twitter Data Analysis

This paper presents sentiment analysis of twitter data on movies using R-studio. Twitter is one of the largest social media that shares user opinion about a thing or event that happens all around the world. Recently social media analysis gained importance in digital marketing. User tweets about a product or event, person, movie, etc., are analyzed to know market trends and customer feedback. In this paper, first we have performed literature study on various methods used in twitter data analysis. Second, we have discussed about the steps involved in accessing twitter data. Finally, we have performed sentiment analysis on tweeter data for the movies titled kabali, Bharath Ane Nenu Mersal, and Dangal. User data for the movies are classified into positive, neutral and negative based on DBM and SVM. Sentiment scores are used as evaluation metrics. Results shows DBM is effective in classifying sentiments and produced better sentiment scores compared to SVM. Results are helpful in identifying popularity of the movies and audience feedback about the movies.

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Literature review on Real-time Location-Based Sentiment Analysis on Twitter

Advances in Technology ◽

10.31357/ait.v1i2.4936 ◽

2021 ◽

Vol 1 (2) ◽

Author(s):

Dilmini Rathnayaka ◽

Pubudu K.P.N Jayasena ◽

Iraj Ratnayake

Keyword(s):

Social Media ◽

Data Analysis ◽

Sentiment Analysis ◽

Real Time ◽

Analysis Data ◽

Time Data ◽

Twitter Data ◽

Analysis Methods ◽

Real Time Data ◽

Twitter Data Analysis

Sentiment analysis mainly supports sorting out the polarity and provides valuable information with the use of raw data in social media platforms. Many fields like health, business, and security require real-time data analysis for instant decision-making situations.Since Twitter is considered a popular social media platform to collect data easily, this paper is considering data analysis methods of Twitter data, real-time Twitter data analysis based on geo-location. Twitter data classification and analysis can be done with the use of diverse algorithms and deciding the most appropriate algorithm for data analysis, can be accomplished by implementing and testing these diverse algorithms.This paper is discussing the major description of sentiment analysis, data collection methods, data pre-processing, feature extraction, and sentiment analysis methods related to Twitter data. Real-time data analysis arises as a major method of analyzing the data available online and the real-time Twitter data analysis process is described throughout this paper. Several methods of classifying the polarized Twitter data are discussed within the paper while depicting a proposed method of Twitter data analyzing algorithm. Location-based Twitter data analysis is another crucial aspect of sentiment analyses, that enables data sorting according to geo-location, and this paper describes the way of analyzing Twitter data based on geo-location. Further, a comparison about several sentiment analysis algorithms used by previous researchers has been reported and finally, a conclusion has been provided.

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TWITTER LOCATION-BASED DATA: EVALUATING THE METHODS OF DATA COLLECTION PROVIDED BY TWITTER API

International Journal of Computing ◽

10.47839/ijc.19.4.1992 ◽

2020 ◽

pp. 583-589

Author(s):

Noor Ahmed Qarabash ◽

Haneen Ahmed Qarabash

Keyword(s):

Data Analysis ◽

Data Collection ◽

Sentiment Analysis ◽

Disaster Response ◽

Representative Data ◽

Data Collection Methods ◽

Twitter Data ◽

Twitter Analysis ◽

Twitter Data Analysis ◽

Collection Methods

Twitter data analysis is an emerging field of research that utilizes data collected from Twitter to address many issues such as disaster response, sentiment analysis, and demographic studies. The success of data analysis relies on collecting accurate and representative data of the studied group or phenomena to get the best results. Various twitter analysis applications rely on collecting the locations of the users sending the tweets, but this information is not always available. There are several attempts at estimating location based aspects of a tweet. However, there is a lack of attempts on investigating the data collection methods that are focused on location. In this paper, we investigate the two methods for obtaining location-based data provided by Twitter API, Twitter places and Geocode parameters. We studied these methods to determine their accuracy and their suitability for research. The study concludes that the places method is the more accurate, but it excludes a lot of the data, while the geocode method provides us with more data, but special attention needs to be paid to outliers.

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Tools of Opinion Mining

Advances in Data Mining and Database Management - Extracting Knowledge From Opinion Mining ◽

10.4018/978-1-5225-6117-0.ch009 ◽

2019 ◽

pp. 179-203

Author(s):

Neha Gupta ◽

Siddharth Verma

Keyword(s):

Sentiment Analysis ◽

Opinion Mining ◽

Semantic Analysis ◽

Analysis Tool ◽

Search Results ◽

Twitter Data

Today's generation express their views and opinions publicly. For any organization or for individuals, this feedback is very crucial to improve their products and services. This huge volume of reviews can be analyzed by opinion mining (also known as semantic analysis). It is an emerging field for researchers that aims to distinguish the emotions expressed within the reviews, classifying them into positive or negative opinions, and summarizing it into a form that is easily understood by users. The idea of opinion mining and sentiment analysis tool is to process a set of search results for a given item based on the quality and features. Research has been conducted to mine opinions in form of document, sentence, and feature level sentiment analysis. This chapter examines how opinion mining is moving to the sentimental reviews of Twitter data, comments used in Facebook on pictures, videos, or Facebook statuses. Thus, this chapter discusses an overview of opinion mining in detail with the techniques and tools.

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