Semi-supervised opinion mining : learning sentiment towards vaccination on Dutch tweets

Nowadays, online social media platforms like Twitter or Facebook provide a valuable framework for individuals to share and discuss ideas and opinions regarding various topics, including health related issues. Platforms like Twitter allow for an exhaustive aggregation and analysis of usergenerated content which facilitates monitoring of public opinions and sentiment over time towards certain topic of interest. Keeping track of these developments can be crucial for health professionals in order to understand and address the public opinion and behaviour with regard to health related topics. One such topic is vaccination. The goal of this thesis is to perform sentiment analysis towards vaccination on Dutch tweets referring to vaccination. I will present results for using a framework named co-training in order to iteratively label unlabeled instances while adding those labeled with high confidence to the training set. The experiments demonstrate that the co-training framework can be successfully applied to improve sentiment analysis towards vaccination on Dutch Twitter messages. In total a best F1 score of 0.72 on the negative class and 0.70 on the non-negative class has been reached with the best performing co-training classification system. Nicole Walasek s4629310 1. The importance of online opinion mining for vaccination Nowadays, online social media platforms like Twitter or Facebook provide a valuable framework for individuals to share and discuss ideas and opinions regarding various topics, including health related issues. Within the past decade these kinds of platforms have spread globally and witnessed a rapid growth in the number of users, reaching people from various demographic groups, ethnicities and occupations (Perrin 2015). Twitter, a popular news and social networking service, where messages (“tweets”) are restricted to 140 characters, has reported 317 million monthly and 100 million daily active users in January 2017 with a total of 500 million tweets sent per day (Aslam 2017). These characteristics of platforms like Twitter allow for an exhaustive aggregation and analysis of usergenerated content which facilitates monitoring of public opinions and sentiment over time towards certain topics of interest. Keeping track of these developments can be crucial for health professionals in order to understand and address the public opinion and behaviour with regard to health related topics. One such topic is vaccination. Outbreaks of vaccine preventable diseases are a serious public health issue. Its likelihood is reported to increase either for an overall decline in vaccine uptake (Jansen et al. 2003), or with an increase in the number and size of communities with very low vaccination rates. These low vaccination rates are often considered to be the result of so-called opinion clustering (Salathé and Bonhoeffer 2008, Omer et al. 2009). Opinion clusters are clusters of (unvaccinated) individuals who share the same sentiment concerning vaccination. Recognizing and addressing public concerns regarding vaccination is important for evolving successful immunization strategies in order to control the spread of infectious diseases. The structure of the Twitter network allows not only to study sentiment towards vaccination but also to make inferences about how these sentiment and behavioral patterns cluster along temporal, geographic and demographic dimensions (Campbell and Salathé 2012, Huang et al. 2017). This way Public Healthcare Organizations try to detect social discussion communities that are identified to influence vaccination decision-making in order to adapt immunization strategies to avoid new outbreaks of infectious diseases (Bello-Orgaz et al. 2017). Furthermore, Signorini et al. (2011) have demonstrated in the past that Twitter can be successfully used not only to track users’ interest in and concern about H1N1 influenza, but also to predict disease outbreaks in real time. The goal of this thesis is to perform sentiment analysis towards vaccination on Dutch tweets referring to vaccination. The task poses several challenges: 1. Identify whether a tweet is relevant, i.e. addressing vaccination 2. Disentangle general sentiment from sentiment towards vaccination 3. Train a (multiple) classifier(s) on only a small number of labeled instances each of them being at most 140 characters long The first challenge can be overcome by checking whether a tweet contains keywords related to vaccination, such as “vaccination” or “immunization”. However, there are also tweets which contain these keywords and are not related to vaccination, as for instance “A vaccine against hypocrisy would also be nice”. Concerning the second challenge some tweets might be about vaccination but express a sentiment that is not related to vaccination. Take for instance “I hate rain. Later on heading to a doctor’s appointment for vaccination. ”. Although this tweet conveys negative sentiment (“I hate rain”) and refers to the topic of vaccination, the expressed sentiment is not related to vaccination. 1. Tweets contain specific markers like # or @ to denote topics or user references. Moreover, tweets hold information about the geographic location of a user, as well as other user information inferred from his Twitter profile. Linking all of these information allows to build graphs visualizing different aspects of the Twitter network. 2. Note, that the English example tweets are artificial and only added to illustrate the challenges.