Scaling-up the Resources for a Freely Available S wedish VADER (sv VADER ) (2023)

Abstract: With widespread commercial applications in various domains, sentiment analysis has become a success story for Natural Language Processing (NLP). Still, although sentiment analysis has rapidly progressed during the last years, mainly due to the application of modern AI technologies, many approaches apply knowledge-based strategies, such as lexicon-based, to the task. This is particularly true for analyzing short social media content, e.g., tweets. Moreover, lexicon-based sentiment analysis approaches are usually preferred over learning-based methods when training data is unavailable or insufficient. Therefore, our main goal is to scale-up and apply a lexicon-based approach which can be used as a baseline to Swedish sentiment analysis. All scaled-up resources are made available, while the performance of this enhanced tool is evaluated on two short datasets, achieving adequate results.

## introduction sentiment analysis is the computational study of people's opinions, sentiments, emotions, and attitudes towards entities such as products and services, and their attributes. sentiment analysis allows tracking of the public's mood about a particular entity to create actionable knowledge @xcite and has found numerous applications, ranging from digital humanities @xcite to gaining insight into customers' feedback about commercial products and services @xcite . sentiment analysis can occur at the document, sentence, or word level, while the sentiment types usually assigned are very positive, positive, neutral, negative or very negative. e.g., the sentiment for the sentence att känna stödet från publiken och folket är väldigt smickrande 'to feel the support of the audience and the people is very flattering' will be usually assigned a positive sentiment while the sentence föräldrar i chock efter bluffen i basketlaget 'parents in shock after the hoax in the basketball team' will be assigned a negative one. in this paper we discuss an enhancement of a popular off-the-shelf (unsupervised) dictionarybased approach to sentiment analysis using vader (valence aware dictionary and sentiment reasoner). vader is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media @xcite . vader is fully open-sourced, available e.g., from the nltk package @xcite , which can be applied directly to unlabeled text data. furthermore, vader can efficiently handle large vocabularies, including the use of degree modifiers and emoticons. these qualities make vader a good fit for use on social media input for rapid sentiment text analysis. as such, the need for previous training as in machine or deep learning models, is eliminated. our main aim of this work is to make vader a useful baseline for swedish sentiment analysis, by rapidly scaling-up and improving the coverage of the already translated to swedish resources (lexicons and processing tools). we further evaluate the coverage by applying and comparing the original vader translation with the enhanced version on two small datasets, one with swedish tweets and one sample from the absabank-imm 1 , an annotated swedish corpus for aspect-based se-ntiment analysis @xcite . ## vader the valence aware dictionary for sentiment reasoning (vader foot_1 ) is a parsimonious rulebased model for sentiment analysis of specifically social media text @xcite . since its release vader has been extensively used in various applications and domains; from the analysis stock news headlines @xcite ; to the assessment of sentiments expressed in customers' e-mails @xcite ; and further to the analysis of tweets on covid-19 vaccine hesitancy @xcite . ## application scenario: swedish tweets about mrna vaccines and flashback posts on immigration as an application scenario for the evaluation of svvader we selected two small datasets. the first one consists of swedish tweets posted in 2022 that discuss vaccine skepticism, and particularly, anxiety about possible side effects and concerns related to novel vaccine technologies, such as the messenger rna (mrna) which has be used as a reason for not receiving (the covid-19) vaccine @xcite . the extracted swedish tweets were collected with the keywords m-?rna.* ('?' the preceding character is optional.; '.*': ≥ 0 characters) or the hashtag #mrna and lang:sv (swedish content). from the extracted tweets (ca 1,800), a random selection of 200 tweets was selected for the svvader evaluation. the second dataset originates from the absabank-imm (where absa stands for "aspect-based sentiment analysis" and imm for "immigration", a subset of the swedish absabank) annotated dataset 7 motivated by the fact that there is a growing interest to analyzed social media with health-related content. in absabank-imm, texts and paragraphs are manually labelled according to the sentiment (on 1-5 scale) that the author expresses towards immigration in sweden (a task also known as stance analysis). the 315 posts come from the flashback forum 9 , a popular swedish discussion platform. for simplicity, the extracted posts consisted of posts with 1-2 sentences; posts that consisted of 3 or more sentences were excluded. moreover, the selected posts were labelled as positive if their manually assigned score in absa was 5.0 (very positive) or 4.0 (positive) and negative if their manually assigned score was 1.0 (very negative) or 2.0 (negative). posts that lied in the middle scale with ratio 3.0 were labelled as neutral. thus, for practical reasons, we collapsed the scores 5.0 and 4.0 to positive sentiment and 1.0 and 2.0 to negative. ## conclusions and future work vader offers a simple process for sentiment classification with a design focus on social media texts, where no training data is required, and can be used as a baseline method to evaluate and compare other methods. in this paper we outlined the scaling-up process for a dictionary approach to https://huggingface.co/recordedfutur swedish sentiment analysis using the vader, a less resource-consuming lexicon and rule-based sentiment analysis tool that consumes fewer resources as compared to learning models as there is no need for vast amounts of training data. as such, vader can serve as a good starting point to sentiment analysis before diving into more advanced machine learning (e.g., transfer learning; prottasha et al., 2022); semiautomatic lexicon based @xcite or deep learning models foot_6 which stand out in terms of usage the last years, and compare their results @xcite . for higher level of accuracy, it may be worth evaluating alternatives @xcite or even better a combination of alternative models using the vader's sentiment scores as input feature to ensemble learning @xcite . the performance of svvader was further evaluated on two, rather small, but characteristic swedish social media datasets. one that contains 200 tweets and one with 200 single-sentenced posts from flashback and the achieved results were adequate. e.g., compared to gervader: f1-@xmath0,42% on german human labelled tweets and vader-it, gynaecology reviews, with f1-@xmath1.47%. we have also shown several ways to augment and expand the resources, and there is a strong indication in which mwes can slightly contribute to the improvement of the results (semantic orientation) of the texts. perhaps evaluation on much larger and varied datasets could achieve better performance.