Detection of Sentiment in Luxembourgish User Comments

Sentiment is all around us in everyday life. It can be found in blog posts, social media comments, text messages and many other places where people express themselves. Sentiment analysis is the task of automatically detecting those sentiments, attitudes or opinions in written text. In this research, the first sentiment analysis solution for the low-resource language, Luxembourgish, is conducted using a large corpus of user comments published on the RTL Luxembourg website www.rtl.lu. Various resources were created for this purpose to set the foundation for further sentiment research in Luxembourgish. A Luxembourgish sentiment lexicon and an annotation tool were built as external resources that can be used for collecting and enlarging training data for sentiment analysis tasks. Additionally, a corpus of mainly sentences of user comments was annotated with negative, neutral and positive labels. This corpus was furthermore automatically translated to English and German. Afterwards, diverse text representations such as word2vec, tf-idf and one-hot encoding were used on the three versions of the corpus of labeled sentences for training different machine learning models. Furthermore, one part of the experimental setup leveraged linguistic features for the classification process in order to study their impact on sentiment expressions. By following such a broad strategy, this thesis not only sets the basis for sentiment analysis with Luxembourgish texts but also intends to give recommendations for conducting sentiment detection research for other low-resource languages. It is demonstrated that creating new resources for a low-resource language is an intensive task and should be carefully planned in order to outperform working with translations where the target language is a high-resource language such as English and German

Component Analysis of Adjectives in Luxembourgish for Detecting Sentiments

The aim of this paper is to investigate the role of Luxembourgish adjectives in expressing sentiments in user comments written at the web presence of rtl.lu (RTL is the abbreviation for Radio Television Lëtzebuerg). Alongside many textual features or representations, adjectives could be used in order to detect sentiment, even on a sentence or comment level. In fact, they are also by themselves one of the best ways to describe a sentiment, despite the fact that other word classes such as nouns, verbs, adverbs or conjunctions can also be utilized for this purpose. The empirical part of this study focuses on a list of adjectives that were extracted from an annotated corpus. The corpus contains the part of speech tags of individual words and sentiment annotation on the adjective, sentence, and comment level. Suffixes of Luxembourgish adjectives like -esch, -eg, -lech, -al, -el, -iv, -ent, -los, -bar and the prefix on- were explicitly investigated, especially by paying attention to their role in regards to building a model by applying classical machine learning techniques. We also considered the interaction of adjectives with other grammatical means, especially other part of speeches, e.g. negations, which can completely reverse the meaning, thus the sentiment of an utterance

An Annotation Framework for Luxembourgish Sentiment Analysis

The aim of this paper is to present a framework developed for crowdsourcing sentiment annotation for the low-resource language Luxembourgish. Our tool is easily accessible through a web interface and facilitates sentence-level annotation of several annotators in parallel. In the heart of our framework is an XML database, which serves as central part linking several components. The corpus in the database consists of news articles and user comments. One of the components is LuNa, a tool for linguistic preprocessing of the data set. It tokenizes the text, splits it into sentences and assigns POS-tags to the tokens. After that, the preprocessed text is stored in XML format into the database. The Sentiment Annotation Tool, which is a browser-based tool, then enables the annotation of split sentences from the database. The Sentiment Engine, a separate module, is trained with this material in order to annotate the whole data set and analyze the sentiment of the comments over time and in relationship to the news articles. The gained knowledge can again be used to improve the sentiment classification on the one hand and on the other hand to understand the sentiment phenomenon from the linguistic point of view