Aspect Based Sentiment Analysis of F innish Neighborhoods: Insights from Suomi24 (2024)

Abstract: This study presents an approach to Aspect-Based Sentiment Analysis (ABSA) using Natural Language Processing (NLP) techniques to explore public sentiment across 12 suburban neighborhoods in Finland. We employed and compared a range of machine learning models for sentiment classification, with the RoBERTa model emerging as the best performer. Using RoBERTa, we conducted a comprehensive sentiment analysis(SA) on a manually annotated dataset and a predicted dataset comprising 32,183 data points to investigate sentiment trends over time in these areas. The results provide insights into fluctuations in public sentiment, highlighting both the robustness of the RoBERTa model and significant shifts in sentiment for specific neighborhoods over time. This research contributes to a deeper understanding of neighborhood sentiment dynamics in Finland, with potential implications for social research and urban development.

## introduction understanding public sentiment towards specific neighborhoods is crucial for urban planners, sociologists, and policymakers, as it offers insights into the social fabric of communities and the challenges they face. with the advent of social media and online forums, the internet has become a rich source of data reflecting public opinion. suomi24 1 , the largest finnish online forum, is an ideal platform for studying the sentiments of neighborhoods through user-generated content. hence, we used suomi24 as our data source driven by the following factors. first, social media has become a popular platform for expressing opinions, making it valuable for sa on topics such as urban issues, climate change, and healthcare @xcite . second, online forums provide an efficient way to share information and generate 1 https://www.suomi24.fi/ discussions, offering a wealth of informative comments that can be analyzed for sentiment @xcite . building on this, @xcite utilized the suomi24 data to create a manually annotated dataset foot_0 for the sa task, further highlighting the platform's potential for research in finnish sa. the field of urban design and planning increasingly utilizes sa to evaluate urban environments through crowdsourced data @xcite . platforms like point of interest (poi) databases, social media, and citizen engagement tools have proven valuable for large-scale urban assessments @xcite @xcite . there are several techniques in literature using nlp to extract sentiment from geo-located textual data. however, most models focus on identifying general positive or negative sentiments within a text, with limited efforts made to train models that can detect specific aspects and their associated sentiments @xcite . therefore, to expand the limited research on absa in finnish suburbs, we seek to explore the following research question (rq): how do sentiments toward suburbs in finland, expressed in public online discussions, vary over time, and how effectively can absa models capture and predict these sentiments? to address this research objective, we investigate public sentiment toward 12 different areas in finland by utilizing a manually annotated finnish-language dataset for the absa task. for this purpose, each neighborhood is treated as a distinct 'aspect,' allowing us to classify sentiments expressed toward these locations rather than identifying specific reasons for likes or dislikes associated with each neighborhood. this approach provides an overview of the sentiment trends for each neighborhood over time, using the neighborhood names as predefined aspects for classification. afterwards, we applied a combination of classical machine learning models such as support vector machines (svm) and naive bayes, alongside modern deep learning models like bert foot_1 (turkunlp/bert-base-finnish-cased-v1) @xcite and roberta (finnish-nlp/roberta-large-finnish-v2) 4 , to analyze sentiment towards these neighborhoods. our results show that roberta outperforms other models in accurately predicting sentiment across these neighborhoods. further, we expanded our analysis by using roberta to predict sentiment on a larger dataset of approximately 32,000 data points for the suburbs of interest in finland. this allowed us to track trends and shifts in sentiment over time, providing valuable insights into how public perceptions of different neighborhoods evolve. our findings contribute to the growing body of research on sa and urban studies, particularly in the finnish context. the results have practical implications for regional development and policy formulation, as understanding public sentiment is key to fostering better community relations and addressing regional disparities. ## sentiment analysis and social media data in the age of digitalization, the growth of social media has significantly transformed the global flow of information and the organization of social demands. platforms such as twitter, facebook, and instagram have emerged as vast repositories of realtime opinions from diverse groups @xcite . the vast volume of online-generated data makes manual analysis challenging and hinders the ability to identify trends in a timely manner. as a result, nlp techniques, particularly sa, have become widely used in recent literature for analyzing social media content and user feedback @xcite . sentiment analysis/classification is a specific type of text categorization, where the classification is based on the author's expressed attitude by understanding of the document and focusing on how the sentiment is conveyed throughout the text @xcite . sa is usually performed at three levels as follows: document level, sentence level, and aspect/category level. the objective of documentlevel sentiment analysis is to assess the overall sentiment of the entire document. sentence-level sentiment analysis evaluates the overall sentiment of a sentence as a single entity and predicting the opinion expressed within that sentence @xcite . whereas absa goes deeper by identifying specific opinion targets (aspects) within the text and determining the sentiment associated with each target @xcite . sa techniques are typically divided into supervised, unsupervised, and semi-supervised learning approaches. the supervised method applies labeled data for training and evaluation. the lexicon-based approach is an example of an unsupervised method, where the sentiment of a document is determined by assessing the semantic polarity of individual words by utilizing a pre-existing dictionary that assigns sentiment polarities to words or phrases. the overall sentiment of the text is then determined based on the predominant polarity of the words. @xcite . traditional machine learning techniques, including svm @xcite and naive bayes @xcite , have been extensively utilized in various sa applications. traditional machine learning methods often rely on bag-of-words (bow) or term frequency-inverse document frequency (tf-idf) for feature extraction. however, these approaches tend to generate sparse, high-dimensional feature vectors and have limited ability to capture the word order, syntactic structure, and semantic meaning of a sentence @xcite . recent advancements in deep learning models have surpassed the performance of traditional methods by addressing the limitations of traditional machine learning methods. bidirectional encoder representations from transformers (bert) (devlin, 2018) is a pre-trained model that utilizes a self-attention mechanism @xcite to weigh the importance of each word in a sentence, and captures complex word dependencies regardless of their position. unlike traditional models, bert processes text bidirectionally to understand the full context, enabling deeper comprehension of syntactic and semantic relationships. roberta (robustly optimized bert pretraining approach) @xcite is a variant of the bert model, that improves bert's performance by modifying its training process and showing better performance on natural language understanding(nlu) tasks @xcite . roberta removes bert's next sentence prediction task, increases the training data, and extends the training duration with larger mini-batches and more diverse data sources @xcite . ## methodology in this section, we outline our methodology for conducting absa using user-generated content. the objective is to analyze and understand people's sentiments toward 12 suburban neighborhoods in finland. by identifying sentiment changes over time for these areas, we aim to uncover patterns related to public perception and community sentiment changes. as depicted in figure 1 , our process begins with scraping data from suomi24, followed by data pro-cessing. in the feature extraction and aspect selection phase, we identify and tag the specific areas of interest within the text. the data is then sampled and annotated with corresponding sentiment labels, preparing it for absa. the output consists of sentiment polarities for each area, which we analyze to detect trends and shifts in public sentiment over time. this methodology helps in understanding how residents and visitors perceive these neighborhoods and track any significant changes in sentiment. ## discussion naturally occurring online discussions offer an opportunity to study the sentiments of the general public about neighborhoods form a linguistic point of view. understanding such sentiments is crucial for regional development, urban planning, and public policy. this study aimed to investigate sentiment toward 12 different neighborhoods in finland by leveraging user-generated content from suomi24. using a combination of classical machine learning models, such as svm and naive bayes, alongside state-of-the-art models like bert and roberta, we applied absa to analyze the sentiment associated with each neighborhood. the roberta model outperformed other methods, demonstrating its superior ability to predict sentiment accurately. we extended our research to analyze sentiment trends for a larger dataset of 32,183 data points, which offers insights into how public opinion evolves over time. our results contribute to the growing body of knowledge in sa, particularly in the context of regional perceptions, and have practical implications for urban planning and policy making. we conducted an error analysis using the bestperforming model, roberta, to investigate the misclassifications made on the test set. as previfigure 5: kontula sentiment change overtime ously mentioned, the lemmatized version of the text was used for both model training and evaluation. however, in this section, we present examples in their original form to enhance readability and provide clarity on the context. a significant number of misclassifications stemmed from ambiguous or multi-layered statements where the emotional tone was not immediately clear. the model tended to predict negative sentiment when it encountered critical or confrontational language, whereas human annotators perceived such content as neutral or factual. for instance, the sentence "2 kysymystä mitä sä sitten riehut. vai uhkaako teidän @xmath0. furthermore, the model struggled with indirect sentiments, such as rhetorical or sarcastic statements, which it frequently misinterpreted as negative when the intended sentiment was neutral or sarcastic. an example is the sentence "suomalaisen luonteen heikkous näkyy @xmath1. lastly, some factual statements were misclassified, likely because the model identified subtle emotional cues that the annotators did not prioritize. for instance, "nykyään rakennetaan hometaloja ! @xmath2. the phrase "rakennetaan hometaloja" (meaning "nowadays they build moldy houses") might imply dissatisfaction with construction practices through sarcasm. increasing the amount of training data could enhance the model's ability to handle these complexities more effectively. the analysis of sentiment trends across the 12 neighborhoods reveals a range of patterns, with most neighborhoods showing either stable or slightly declining sentiment over time. areas such as leppävaara, pihlajamäki, and uittamo exhibit a subtle but consistent decline in sentiment, with annotated data indicating a shift from neutral or slightly positive to more negative sentiment. matinkylä and maunula remain largely neutral, with minimal fluctuations in sentiment across both annotated and predicted datasets, indicating a stable public perception. myllypuro and runosmäki show a predominantly negative sentiment, with runosmäki demonstrating greater variability in the annotated data, while predicted data captures a more consistent negative trend. in contrast, soukka and suikkila display improvements in sentiment over time according to the annotated data, though the predicted data does not fully reflect this positive shift, suggesting a potential limitation in capturing more complex emotional changes. ## conclusion this study presents a manually annotated dataset for absa tasks for studying different areas in finland. as far as we know, there is no publicly available dataset for this specific domain in the finnish language. the dataset consists of 3183 sentences taken from suomi24. the dataset has been designed to perform various research tasks such as aspect extraction, and polarity detection. we believe this dataset would provide considerable significance in the research area of absa for social media text in the finnish language. the dataset will be published publicly to be used by researchers in finland. moreover, our research addresses a significant gap by using sa to understand public perceptions of different areas in finland. utilizing data from suomi24, one of the largest online forums in finland, allows for the collection of unfiltered opinions, offering an authentic insight into how residents perceive their living environments. by applying state-of-the-art absa models like roberta, our study enhances the analysis of regional sentiment. additionally, tracking sentiment trends over time using 32,183 data points can provide valuable information for urban planners, policymakers, and social scientists. this combination of largescale, time-sensitive data analysis and advanced machine learning techniques makes our research highly relevant for improving public policy and regional development. future research could explore the relationship between sentiment and real estate pricing in finland by analyzing how changes in public sentiment over time correlate with fluctuations in housing prices. by pairing sa with real estate data, it may be possible to predict price trends in specific areas or explain price shifts based on sentiment dynamics. a similar approach has been applied in other markets, such as the study by @xcite which analyzed the predictive power of sentiment on market indicators like price and transaction volume in the housing market. applying such a methodology to the finnish housing market could provide new insights into how local sentiment influences real estate trends, potentially offering predictive power for pricing fluctuations across different areas. finally, a limitation of this study is the potential impact of dataset imbalance and prediction errors, which can affect the detection of complex sentiment trends in the larger predicted dataset. an imbalanced training set, with a prevalence of neutral sentiments, may lead the model to favor stable or neutral predictions. thereby, smoothing out fluctuations that might otherwise reveal slight shifts in sentiment. addressing these challenges in future work by employing techniques to balance sentiment classes or reduce prediction errors could improve the model's sensitivity to evolving sentiment dynamics across neighborhoods. moreover, we acknowledge that the number of posts may have varied significantly across the years, particularly as internet and social media usage expanded in finland. however, while our focus was on examining sentiment trends rather than volume dynamics, future work could delve deeper into how this growth in user base and posting frequency influences sentiment patterns. incorporating controls for yearly post volumes could offer a clearer view of whether observed sentiment shifts are affected by posting frequency or reflect genuine changes in public sentiment.