Sökning: "Text classification"
Visar resultat 1 - 5 av 128 uppsatser innehållade orden Text classification.
- Kandidat-uppsats, Mälardalens högskola/Akademin för innovation, design och teknik
Sammanfattning : Emotion detection, also known as Facial expression recognition, is the art of mapping an emotion to some sort of input data taken from a human. This is a powerful tool to extract valuable information from individuals which can be used as data for many different purposes, ranging from medical conditions such as depression to customer feedback. LÄS MER
- Master-uppsats, Linköpings universitet/Interaktiva och kognitiva system
Sammanfattning : Categorizing books and literature of any genre and subject area is a vital task for publishers which seek to distribute their books to the appropriate audiences. It is common that different countries use different subject categorization schemes, which makes international book trading more difficult due to the need to categorize books from scratch once they reach another country. LÄS MER
3. Exploit Unlabeled Data with Language Model for Text Classification. Comparison of four unsupervised learning modelsMaster-uppsats, Göteborgs universitet/Institutionen för filosofi, lingvistikoch vetenskapsteori
Sammanfattning : Within a situation where Semi-Supervised Learning (SSL) is available to exploit unlabeled data, this paper shows that Language Model (LM) outperforms the three models in text classification, which three models are based on Term-Frequency Inverse Document Frequency (Tf-idf) and two pre-trained word vectors. The experimental results show that the LM outperforms the other three unsupervised learning models whether the task is easy or difficult, which the difficult task consists of imbalanced data. LÄS MER
4. Identifying Hateful Text on Social Media with Machine Learning Classifiers and Normalization Methods - Using Support Vector Machines and Naive Bayes AlgorithmKandidat-uppsats, Umeå universitet/Institutionen för datavetenskap
Sammanfattning : Hateful content on social media is a growing problem. In this thesis, machine learning algorithms and pre-processing methods have been combined in order to train classifiers in identifying hateful text on social media. LÄS MER
- Master-uppsats, KTH/Matematisk statistik
Sammanfattning : With more and more digital text-valued data available, the need to be able to cluster, classify and study them arises. We develop in this thesis statistical tools to perform null hypothesis testing and clustering or classification on text-valued data in the framework of Object-Oriented Data Analysis. LÄS MER