Topic modelling of Finnish Internet discussion forums as a tool for trend identification and marketing applications

The increasing availability of public discussion text data on the Internet motivates to study methods to identify current themes and trends. Being able to extract and summarize relevant information from public data in real time gives rise to competitive advantage and applications in the marketing actions of a company. This thesis presents a method of topic modelling and trend identification to extract information from Finnish Internet discussion forums. The development of text analytics, and especially topic modelling techniques, is reviewed and suitable methods are identified from the literature. The Latent Dirichlet Allocation topic model and the Dynamic Topic Model are applied in finding underlying topics from the Internet discussion forum data. The discussion data collection with web scarping and text data preprocessing methods are presented. Trends are identified with a method derived from outlier detection. Real world events, such as the news about Finnish army vegetarian meal day and the Helsinki summit of presidents Trump and Putin, were identified in an unsupervised manner. Applications for marketing are considered, e.g. automatic search engine advert keyword generation and website content recommendation. Future prospects for further improving the developed topical trend identification method are proposed. This includes the use of more complex topic models, extensive framework for tuning trend identification parameters and studying the use of more domain specific text data sources such as blogs, social media feeds or customer feedback

A mathematical analysis of the information system domain

Theoretical concepts underlying information systems are analyzed. The study is in general oriented towards computer-based, large capacity IS. An attempt has been made to identify the distinguishible elements of IS domain and establish interrelationships between them. Some current IS theories are evaluated

Unsupervised learning for text-to-speech synthesis

This thesis introduces a general method for incorporating the distributional analysis of textual and linguistic objects into text-to-speech (TTS) conversion systems. Conventional TTS conversion uses intermediate layers of representation to bridge the gap between text and speech. Collecting the annotated data needed to produce these intermediate layers is a far from trivial task, possibly prohibitively so for languages in which no such resources are in existence. Distributional analysis, in contrast, proceeds in an unsupervised manner, and so enables the creation of systems using textual data that are not annotated. The method therefore aids the building of systems for languages in which conventional linguistic resources are scarce, but is not restricted to these languages. The distributional analysis proposed here places the textual objects analysed in a continuous-valued space, rather than specifying a hard categorisation of those objects. This space is then partitioned during the training of acoustic models for synthesis, so that the models generalise over objects' surface forms in a way that is acoustically relevant. The method is applied to three levels of textual analysis: to the characterisation of sub-syllabic units, word units and utterances. Entire systems for three languages (English, Finnish and Romanian) are built with no reliance on manually labelled data or language-specific expertise. Results of a subjective evaluation are presented