Microblog has become a central platform where people express their thoughts and opinions toward public events in China. With the sudden outbreak of coronavirus, the posts related to coronavirus are usually followed by a burst immediately in microblog volume, which provides a great opportunity to explore public sentiment about the events. In this context, sentiment analysis is helpful to explore how coronavirus affects public opinions. Deep learning has become a very popular technique for sentiment analysis. This thesis uses Bidirectional Encoder Representations from Transformers (BERT), a pre-trained unsupervised language representation model based on deep learning, to generate initial token embeddings that are further tuned by a neural network model on a supervised corpus, a sentiment classifier is constructed. We utilize data recently made available by the government of Beijing which contains 1 million blog posts from January 1 to February 20, 2020. Also, the model developed in this thesis can be used to track the sentiment variation with Weibo microblog data in the future. At the final stage, the variation of public sentiment is analyzed and presented with visualization charts of preformed people sentiment variation with the development of coronavirus in China. Comparison of the results between labeled data and all data is performed in order to explore how thoughts and opinions evolve in time. The result shows a significant growth of the negative sentiment on January 20 when a lockdown started in Wuhan, and afterward the growth becomes slower. Around February 7 when doctor Wenliang Li died, the number of negative sentiments reached its peak.

Online store customers generate vast amounts of product and service information through reviews, which are an important source of feedback. This thesis deals with the creation of a system for the analysis of product and shop reviews in the czech language. It describes the current methods of sentiment analysis and builds on current solutions. The resulting system implements automatic data download and their indexing, subsequently sentiment analysis together with text summary in the form of clustering of similar sentences based on vector representation of the text. A graphical user interface in the form of a web page is also included. A review data set with a total of more than six million reviews was created during the semester along with an interface for easy data export.

We explore the use a Latent Dirichlet Allocation (LDA) imitating pseudo-topic-model, based on our original relevance metric, as a tool to facilitate distant annotation of short (often one to two sentence or less) documents. Our exploration manifests as annotating tweets for emotions, this being the current use-case of interest to us, but we believe the method could be extended to any multi-class labeling task of documents of similar length. Tweets are gathered via the Twitter API using "track" terms thought likely to capture tweets with a greater chance of exhibiting each emotional class, 3,000 tweets for each of 26 topics anticipated to elicit emotional discourse. Our pseudo-topic-model is used to produce relevance-ranked vocabularies for each corpus of tweets and these are used to distribute emotional annotations to those tweets not manually annotated, magnifying the number of annotated tweets by a factor of 29. The vector labels the annotators produce for the topics are cascaded out to the tweets via three different schemes which are compared for performance by proxy through the competition of bidirectional-LSMTs trained using the tweets labeled at a distance. An SVM and two emotionally annotated vocabularies are also tested on each task to provide context and comparison.

The sentiment analysis of a text is getting more focus nowadays from different entities for a variety of reasons. Emotions mining (sentiment analysis) is a very interesting subject to explore thus the research question is How can a module for sentiment analysis be designed to classify tweets about Covid-19. The dataset used for this project was taken from Kaggle and preprocessed with various methods such as Bag of Words and term frequency-inverse document frequency. The models are based on the following algorithms: KNN, SVM, DT, and NB. Some models are also based on the combination of ML and Lexicon. The outcome of the experiment showed that the lexicon method with an accuracy of 87% exceeded the machine learning methods implemented in this thesis and the experiments done by the ML community in Kaggle. This implies that the traditional lexicon approach is still considered a fit choice in the sentiment analysis field. / På senaste tiden har sentimentanalyser av text fått ett större fokus. Känsloutvinning (Emotions mining) är ett väldigt intressant ämne att utforska, Forskningsfrågan är då Hur kan man designa en modul inom sentimentanalys för att klassificera tweets om covid19. Datasetet som används är hämtat från Kaggle och sedan preprocesserat med hjälp av olika metoder såsom Bag of Words och term frequency-inverse document frequency. Modellerna är baserad på följande algoritmer: KNN, SVM, DT, och NB. Vissa modeller är baserad på en kombination of ML och Lexicon. Slutresultatet av experimentet visade sig vara att lexikon metoden med en prestanda av 87% översteg maskin inlärningsmetoderna som utfördes i denna uppsatsen och övriga experiment från ML gemensamhet i kaggle. Detta antyder att lexikon metoden är fortfarande ett bra val inom sentimentanalys området.

Sentiment analysis aims at figuring out the opinions of the users towards a certain service or product. In this research, the aim is at classifying the sentiments of users based on the comments they have posed on Douban movie website. In this thesis, I try two different ways to classify the sentiments: with the first one classifying comments into five classes of ratings from 1 to 5, and with the second one classifying comments into three classes of ratings: negative, neutral and positive. For the latter, the ratings of 1 and 2 are grouped as negative, the ratings of 3 neutral and the ratings of 4 and 5 positive. First, Term Frequency Inverse Document Frequency (TF-IDF) is used as the feature extraction technique for machine learning algorithms. Chi Square and Mutual Information are used for feature selection. The selected features are fed into different machine learning methods: Logistic Regression, Linear SVC, SGD classifier and Multinomial Naive Bayes. The performance of models with feature selection will be compared with the performance of models without feature selection for 5-class classification as well as 3-class classification. Also, fastText and Skip-Gram are used as embedding methods for deep learning algorithms LSTM and BILSTM. FastText will also be used for both embedding as well as being a classifier. The aim is to compare different machine learning and deep learning algorithms using different vectorization methods to see which model performs the best regarding both 5-class and 3-class classification. The two classification strategies will be compared with each other in terms of error analysis. The aim is to figure out the similarities and differences of misclassifications made by two different classification strategies.

Genre and domain influence an author\''s style of writing and therefore a text\''s characteristics. Natural language processing is prone to such variations in textual characteristics: it is said to be genre and domain dependent. This thesis investigates genre and domain dependencies in sentiment analysis. Its goal is to support the development of robust sentiment analysis approaches that work well and in a predictable manner under different conditions, i.e. for different genres and domains. Initially, we show that a prototypical approach to sentiment analysis -- viz. a supervised machine learning model based on word n-gram features -- performs differently on gold standards that originate from differing genres and domains, but performs similarly on gold standards that originate from resembling genres and domains. We show that these gold standards differ in certain textual characteristics, viz. their domain complexity. We find a strong linear relation between our approach\''s accuracy on a particular gold standard and its domain complexity, which we then use to estimate our approach\''s accuracy. Subsequently, we use certain textual characteristics -- viz. domain complexity, domain similarity, and readability -- in a variety of applications. Domain complexity and domain similarity measures are used to determine parameter settings in two tasks. Domain complexity guides us in model selection for in-domain polarity classification, viz. in decisions regarding word n-gram model order and word n-gram feature selection. Domain complexity and domain similarity guide us in domain adaptation. We propose a novel domain adaptation scheme and apply it to cross-domain polarity classification in semi- and unsupervised domain adaptation scenarios. Readability is used for feature engineering. We propose to adopt readability gradings, readability indicators as well as word and syntax distributions as features for subjectivity classification. Moreover, we generalize a framework for modeling and representing negation in machine learning-based sentiment analysis. This framework is applied to in-domain and cross-domain polarity classification. We investigate the relation between implicit and explicit negation modeling, the influence of negation scope detection methods, and the efficiency of the framework in different domains. Finally, we carry out a case study in which we transfer the core methods of our thesis -- viz. domain complexity-based accuracy estimation, domain complexity-based model selection, and negation modeling -- to a gold standard that originates from a genre and domain hitherto not used in this thesis.

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This thesis is devoted to the area of sentiment analysis. Its goal is to discuss and compare various methods applicable to sentiment classification of short texts. When analyzing the described techniques, we will orient ourselves towards the context of social networks. Recently, this type of media became the source of vast amounts of data and the demand for its automatic processing is high. Interesting results have been obtained for clustering used in combination with supervised learning and convolution, which is primarily used for image data.

Background. Stock market prediction is an active yet challenging research area. A lot of effort has been put in by both academia and practitioners to produce accurate stock market predictions models, in the attempt to maximize investment objectives. Tree-based ensemble machine learning methods such as XGBoost have proven successful in practice. At the same time, there is a growing trend to incorporate multiple data sources in prediction models, such as historical prices and text, in order to achieve superior forecasting performance. However, most applications and research have so far focused on the American or Asian stock markets, while the Swedish stock market has not been studied extensively from the perspective of hybrid models using both price and text derived features.  Objectives. The purpose of this thesis is to investigate whether augmenting a numerical dataset based on historical prices with sentiment features extracted from financial news improves classification performance when predicting the daily price trend of the Swedish stock market index, OMXS30. Methods. A dataset of 3,517 samples between 2006 - 2020 was collected from two sources, historical prices and financial news. XGBoost was used as classifier and four different metrics were employed for model performance comparison given three complementary datasets: the dataset which contains only the sentiment feature, the dataset with only price-derived features and finally, the dataset augmented with sentiment feature extracted from financial news.  Results. Results show that XGBoost has a good performance in classifying the daily trend of OMXS30 given historical price features, achieving an accuracy of 73% on the test set. A small improvement across all metrics is recorded on the test set when augmenting the numerical dataset with sentiment features extracted from financial news.  Conclusions. XGBoost is a powerful ensemble method for stock market prediction, reflected in a satisfactory classification performance of the daily movement direction of OMXS30. However, augmenting the numerical input set with sentiment features extracted from text did not have a powerful impact on classification performance in this case, as the improvements across all employed metrics were small.

The advent of Web 2.0 technologies which supports the creation and publishing of various social media content in a collaborative and participatory way by all users in the form of user generated content and social networks has led to the creation of vast amounts of structured, semi-structured and unstructured data. The sudden rise of social media has led to their wide adoption by organisations of various sizes worldwide in order to take advantage of this new way of communication and engaging with their stakeholders in ways that was unimaginable before. Data generated from social media is highly unstructured, which makes it challenging for most organisations which are normally used for handling and analysing structured data from business transactions. The research reported in this dissertation was carried out to investigate fast and efficient methods available for retrieving, storing and analysing unstructured data form social media in order to make crucial and informed business decisions on time. Sentiment analysis was conducted on Twitter data called tweets. Twitter, which is one of the most widely adopted social network service provides an API (Application Programming Interface), for researchers and software developers to connect and collect public data sets of Twitter data from the Twitter database. A Twitter application was created and used to collect streams of real-time public data via a Twitter source provided by Apache Flume and efficiently storing this data in Hadoop File System (HDFS). Apache Flume is a distributed, reliable, and available system which is used to efficiently collect, aggregate and move large amounts of log data from many different sources to a centralized data store such as HDFS. Apache Hadoop is an open source software library that runs on low-cost commodity hardware and has the ability to store, manage and analyse large amounts of both structured and unstructured data quickly, reliably, and flexibly at low-cost. A Lexicon based sentiment analysis approach was taken and the AFINN-111 lexicon was used for scoring. The Twitter data was analysed from the HDFS using a Java MapReduce implementation. MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster. The results demonstrate that it is fast, efficient and economical to use this approach to analyse unstructured data from social media in real time. / Dissertation (MSc)--University of Pretoria, 2019. / National Research Foundation (NRF) - Scarce skills / Computer Science / MSc / Unrestricted