“I Tried to Breastfeed but…”: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis

Abstract

Social media is a growing platform for health-related discourse, opinion and experience sharing, including breastfeeding. For instance, nursing mothers share their personal experiences and opinions about breastfeeding on social networks, such as Facebook and Twitter. Unravelling the sentiments behind these experiences will promote adequate knowledge of many challenges, benefits, and factors influencing breastfeeding behaviours. To achieve this, we mine breastfeeding-related tweets and then perform sentiment analysis of the tweets using lexicon-based and machine learning (ML) techniques with the aim of detecting their sentiment polarity (i.e., positive or negative). Specifically, we implement and compare four lexicon-based sentiment classifiers, as well as five ML-based classifiers. Our results show that VADER-EXT (our extended version of VADER) performed best with an overall F1-score of 82.4%, compared to the other lexicon-based classifiers. On the other hand, Support Vector Machine (SVM) outperformed the other four ML-based classifiers with an overall F1-score of 73.7%. The overall best performing classifier is then used in determining the sentiment polarity of tweets. Next, we conduct thematic analysis of both positive and negative tweets to identify the factors influencing breastfeeding behaviours either positively or negatively. Our findings reveal various health-related factors (such as lactational issues, medical issues, and nutritional issues), social factors, psychological factors, and situational factors affecting breastfeeding behaviours negatively. Also, perceived benefits, maternal selfefficacy, social support, and education and training support emerged as the positive factors influencing breastfeeding behaviours. Finally, we reflect on our findings and recommend interventions that address the negative factors to promote positive breastfeeding behaviours.

Using Machine Learning to Establish the Concerns of Persons With HIV/AIDS During the COVID-19 Pandemic From Their Tweets

“…Lemmatization is more effective than stemming since it converts the word into its root word rather than removing the suffices. Stopwords and punctuations contribute to the overall polarity of texts in lexicon-based sentiment analysis [58], hence we maintained them during the sentiment analysis of the tweets. However, for the detection of topics, the data was converted into lowercase, and punctuations and stopwords were removed.…”

“…In this regard, this research sought to investigate the concerns of the PLWHA by analyzing their opinions expressed on social media. According to [25] and [42], social media platforms encourage all-inclusive participation and promote health-related discourse, opinion, and experience sharing. Due to the anonymity (pseudo name) feature of some of these platforms, PLWHA can be expressive without being overly concerned about victimization [42].…”

Customer sentiment analysis and prediction of halal restaurants using machine learning approaches

“…To perform SA, several lexicons are accessible Customer sentiment analysis (Preethi et al, 2015); lexicon-based techniques are simple to apply, and lexicon-based approaches have been used in many recent and similar studies (Machov a et al, 2020;Yang et al, 2020). Additionally, it was discovered that lexicon-based approaches have been used in a variety of fields to gain the score from the textual data and find top positive and negative sentences, including the tweets of a general user (Bhaumik and Yadav, 2021;Oyebode), tweets about online education during COVID-19 (Mujahid et al, 2021), stock market (Oliveira et al, 2016), news (Nemes and Kiss, 2021), e-mails (Borg and Boldt, 2020) and halal food (Mostafa, 2018;Mostafa, 2020), but not halal restaurants. Hence, in the current work, we used two lexicon-based approaches to find sentiment scores and top positive and negative reviews of halal restaurants.…”

Desmame precoce e depressão pós-parto: uma revisão sistemática

“…(Silva et al, 2017). (Oyebode et al, 2021) Nos seus estudos, Santana et al (2020) explica que o baixo nível de escolaridade e socioeconômico geralmente favorecem um início tardio do pré-natal, o que leva a menor acesso a informações acerca dos inúmeros benefícios do aleitamento materno. Em relação ao emprego, relata que em situações em que mães possuem um emprego que lhe proporciona licença materna além de horários no trabalho para que possam amamentar, este não interfere no AME e que desmame precoce é mais incidente em situações que as mães têm uma carga horária de mais do que 20 horas semanais.…”

“…Em relação ao Aleitamento Materno Exclusivo (AME), estudos mostram que sua prevalência é maior nos Brazilian Journal of Health Review, Curitiba, v. 5, n. 4, p. 14382-14394, jul./aug., 2022 países desenvolvidos quando se compara a países em desenvolvimento, 95% e 78,8% respectivamente. (OYEBODE et al, 2021).…”