Attitydanalys är ett delfält av språkteknologi (NLP) som försöker analysera känslan av skriven text. Detta är ett komplext problem som medför många utmaningar. Av denna anledning har det studerats i stor utsträckning. Under de senaste åren har traditionella maskininlärningsalgoritmer eller handgjord metodik använts och givit utmärkta resultat. Men den senaste renässansen för djupinlärning har växlat om intresse till end to end deep learning-modeller.Å ena sidan resulterar detta i mer kraftfulla modeller men å andra sidansaknas klart matematiskt resonemang eller intuition för dessa modeller. På grund av detta görs ett försök i denna avhandling med att kasta ljus på nyligen föreslagna deep learning-arkitekturer för attitydklassificering. En studie av deras olika skillnader utförs och ger empiriska resultat för hur ändringar i strukturen eller kapacitet hos modellen kan påverka exaktheten och sättet den representerar och ''förstår'' meningarna. / Sentiment analysis is a subfield of natural language processing (NLP) that attempts to analyze the sentiment of written text.It is is a complex problem that entails different challenges. For this reason, it has been studied extensively. In the past years traditional machine learning algorithms or handcrafted methodologies used to provide state of the art results. However, the recent deep learning renaissance shifted interest towards end to end deep learning models. On the one hand this resulted into more powerful models but on the other hand clear mathematical reasoning or intuition behind distinct models is still lacking. As a result, in this thesis, an attempt to shed some light on recently proposed deep learning architectures for sentiment classification is made.A study of their differences is performed as well as provide empirical results on how changes in the structure or capacity of a model can affect its accuracy and the way it represents and ''comprehends'' sentences.

This thesis explores the methods of representing sentence representations for semantic text similarity using word embeddings and benchmarks them against sentence based evaluation test sets. Two methods were used to evaluate the representations: STS Benchmark and STS Benchmark converted to a binary similarity task. Results showed that preprocessing of the word vectors could significantly boost performance in both tasks and conclude that word embed-dings still provide an acceptable solution for specific applications. The study also concluded that the dataset used might not be ideal for this type of evalua-tion, as the sentence pairs in general had a high lexical overlap. To tackle this, the study suggests that a paraphrasing dataset could act as a complement but that further investigation would be needed. / Denna avhandling undersöker metoder för att representera meningar i vektor-form för semantisk textlikhet och jämför dem med meningsbaserade testmäng-der. För att utvärdera representationerna användes två metoder: STS Bench-mark, en vedertagen metod för att utvärdera språkmodellers förmåga att ut-värdera semantisk likhet, och STS Benchmark konverterad till en binär lik-hetsuppgift. Resultaten visade att förbehandling av texten och ordvektorerna kunde ge en signifikant ökning i resultatet för dessa uppgifter. Studien konklu-derade även att datamängden som användes kanske inte är ideal för denna typ av utvärdering, då meningsparen i stort hade ett högt lexikalt överlapp. Som komplement föreslår studien en parafrasdatamängd, något som skulle kräva ytterligare studier.

When issuing surveys, having the option for free-text answer fields is only feasible where the number of respondents is small, as the work to summarize the answers becomes unmanageable with a large number of responses. Using NLP techniques to cluster these answers and summarize them would allow a greater range of survey creators to incorporate free-text answers in their survey, without making their workload too large. Academic work in this domain is sparse, especially for smaller languages such as Swedish. The Swedish company iMatrics is regularly hired to do this kind of summarizing, specifically for workplace-related surveys. Their method of clustering has been semiautomatic, where both manual preprocessing and postprocessing have been necessary to accomplish this task. This thesis aims to explore if using more advanced, unsupervised NLP text representation methods, namely SentenceBERT and Sent2Vec, can improve upon these results and reduce the manual work needed for this task. Specifically, three questions are to be answered. Firstly, do the methods show good results? Secondly, can they remove the time-consuming postprocessing step of combining a large number of clusters into a smaller number? Lastly, can a model where unsupervised learning metrics can be shown to correlate to the real-world usability of the model, thus indicating that these metrics can be used to optimize the model for new data? To answer these questions, several models are trained, employed, and then compared using both internal and external metrics: Sent2Vec, SentenceBERT, and traditional baseline models. A manual evaluation procedure is performed to assess the real-world usability of the clusterings looks like, to see how well the models perform as well as to see if there is any correlation between this result and the internal metrics for the clustering. The results indicate that improving the text representation step is not sufficient for fully automating this task. Some of the models show promise in the results of human evaluation, but given the unsupervised nature of the problem and the large variance between models, it is difficult to predict the performance of new data. Thus, the models can serve as an improvement to the workflow, but the need for manual work remains.