Climate Change Discourse Over Time: A Topic-Sentiment Perspective (2025)

Abstract: The present paper focuses on the study of opinion dynamics and opinion shifts in social media in the context of climate change discourse in terms of the quantitative NLP analysis, supported by a linguistic outlook. The research draws on two comparable collections of climate-related social media data from different time periods, each based on trending climate-related hashtags and annotated for relevant sentiment values. The quantitative computer-based research methodology has been supported by a language-based perspective in the pragma-linguistic form. The research shows that the latter data source, for the majority of identified topics, exhibits a significant reduction in negative sentiment and a dominance of positive sentiment, i.e., a potential temporal evolution in public sentiment toward climate change. To achieve this, we used a BERT-based clustering approach to identify dominant themes within a combined dataset of tweets from both periods. Subsequently, a unified sentiment classification framework using a Large Language Model (LLM) was applied to reclassify all tweets, ensuring consistent and climate-specific sentiment analysis across both datasets. This methodology allowed for a coherent comparison of public attitudes and their evolution in different time periods and thematic structures.

## introduction understanding human communication in its full richness requires delving beyond the mere factual content of utterances. it requires an exploration of the underlying layers of opinion, sentiment, and attitude @xcite that imbue discourse with meaning, intention, and interpersonal resonance. these three interconnected yet distinct concepts form the background of subjective expression, shaping how individuals perceive, interpret, and react to the world around them. the manifestation and interplay of opinions, sentiments, and attitudes are ubiquitous, fundamentally influencing the dynamics of human interaction and the propagation of ideas. the study of opinion, sentiment, and attitude in discourse has emerged as an increasingly critical area within diverse academic disciplines, including linguistics, computational linguistics, and others. this interdisciplinary focus reflects the impact these objective and subjective elements have on information processing, decisionmaking, and social cohesion. opinion can be broadly defined as a belief or judgment held by an individual about a particular subject. it represents a cognitive stance, intermixed with existential, epistemological and moral stances @xcite , perceived in terms of propositional structures, or else as proposed in lewandowska-tomaszczyk et al. @xcite perceived in terms of a whole opinion event. the topic of this study undertakes research in the dynamicity of opinions. opinions are dynamic and can evolve over time as individuals and communities acquire new information, engage in critical reflection, or are exposed to diverse perspectives. in discourse, opinions are frequently expressed through explicit statements, but can also be inferred from argumentative structures, rhetorical devices, patterns of reasoning in terms of figurative, indirect or implicit structures @xcite . sentiment refers to the emotional tone or feeling conveyed by a piece of text or speech. it encapsulates the affective dimension of discourse, reflecting positive, negative, or neutral emotional or, more generally, affective, states @xcite . for example, saying "i love this new policy" expresses a positive sentiment, while "i'm frustrated with the current situation" conveys a negative one. sentiment is often, though not always, expressed through emotionally charged vocabulary (e.g., "amazing," "terrible," "joy," "anger"), but can also be conveyed through intonation, facial expressions, or linguistic cues like intensifiers or hedges. the granularity of sentiment analysis can vary, from broad polarity (positive/negative/neutral) to more fine-grained emotions (e.g., joy, sadness, anger, fear, surprise, disgust) and affective states such as curosity, reserve, boredom @xcite . the prevalence of sentiment in online reviews, social media posts, and customer feedback has made sentiment analysis a cornerstone of natural language processing (nlp), enabling businesses to gauge public perception, track brand reputation, and identify emerging trends. attitude represents a more stable and enduring predisposition towards a person, object, idea, or issue @xcite . it is a psychological construct that reflects an individual's overall evaluative stance, encompassing cognitive, affective, and behavioral components. attitudes are often seen as the underlying drivers of opinions and sentiments. for example, a pro-environmental attitude might lead to the opinion that renewable energy is essential and positive sentiments toward green initiatives. attitudes are more deeply ingrained than sentiments or even situation-specific opinions; they shape an individual's worldview and influence their long-term behaviors and decision-making. in discourse, attitudes are often less explicitly stated than opinions or sentiments, but can be inferred from consistent patterns of expression, recurring themes, and the overall evaluative orientation of an individual's communication. analyzing attitudes in discourse involves a more holistic and interpretive approach, often drawing on psychological theories and qualitative methodologies. the relationship between these three concepts is hierarchical and interdependent. attitudes form the broadest and most stable foundation, influencing the opinions an individual holds, which in turn are often expressed with a particular sentiment. for example, a deeply ingrained attitude of skepticism toward government intervention might lead to the opinion that a new social welfare program is flawed, which could then be articulated with sentiment of anger or frustration. however, this relationship is not always linear or one-directional. a particularly strong sentiment or a newly formed opinion, especially when reinforced by social interaction, can sometimes contribute to the formation or modification of an attitude. the dynamic interplay between these layers makes their individual and collective analysis crucial for a comprehensive understanding of human communication. to sum up, opinions tend to be more rooted in cognitive processes -they are often the result of consideration, evaluation, and assessment. they can be can be logical, informed, or even biased and can be strongly held or loosely formed. sentiment, in this context, refers to the underlying emotional tone or feeling associated with an opinion, statement, or topic. it is the affective component of an opinion. thus, it is most often a complex combination of affect (emotions, feelings or moods) and a cognitive process of thinking. ## influence of external events the dynamism between opinions, emotions, and attitudes with respect to climate change, could be influenced externally both by external weather conditions and by political events. the period between 2015 and 2019 from which we draw our data stands as a pivotal time in the evolution of global climate consciousness, as it was during these five years that the tangible impacts of a warming planet became increasingly undeniable and visible to the broader public due to a series of major climate-related events, coupled with significant policy milestones, served as stark reminders of the escalating crisis, effectively shifting public perception from abstract threat to immediate reality. one of the most significant overarching trends during this period was the consistent shattering of temperature records and the increasingly evident link between these extreme events and human-induced climate change became a central theme in public discourse. the political landscape also played a crucial role in shaping climate consciousness during this period. the paris agreement on climate change foot_0 , adopted in december 2015, marked a landmark moment. furthermore, the intergovernmental panel on climate change (ipcc) special report on global warming of 1.5°c, released in october 2018, delivered a stark warning. on the other hand, donald trump's presidency (which began in january 2017) was marked by a significant departure from previous u.s. climate policy, actively seeking to dismantle environmental regulations and withdraw from international climate agreements foot_1 . paradoxically though, trump's actions and controversy also exerted impact on climate change awareness. and it was then, in august 2018, that greta thunberg 4 emerged as an unparalleled global icon for climate action during the latter part of this period, dramatically amplifying climate consciousness, especially among young people. ## computational and linguistic approaches the rise of computational approaches, particularly in natural language processing (nlp) and machine learning, has revolutionized the study of opinion, sentiment, and attitude in discourse. the sheer volume of digitally available text data -from social media feeds and online forums to news articles and customer reviews -has created an unprecedented opportunity to analyze subjective language at scale. early efforts in sentiment analysis focused primarily on the lexicons of positive and negative words, but more sophisticated techniques now employ machine learning algorithms, deep learning models, and contextual embeddings to capture the nuances of human emotion and subjective expression. these computational tools enable researchers and practitioners to automatically detect sentiment @xcite , identify opinion holders, track opinion evolution, and even infer underlying attitudes from large corpora of text. however, the computational analysis of opinion, sentiment, and attitude is not without its challenges. the inherent subjectivity and context-dependency of human language pose significant hurdles. sarcasm, irony, negation, and implicit expressions of sentiment can easily mislead automated systems. for example, "this movie was so good i fell asleep" expresses negative sentiment despite the positive word "good." furthermore, differentiating between factual statements, expressions of opinion, and expressions of sentiment can be complex, as these often intertwine in natural discourse. the development of robust and accurate computational models requires sophisticated linguistic knowledge, large annotated datasets, and continuous refinement to account for the complexities of human communication. beyond computational approaches, traditional linguistic and discourse analytic methodologies remain indispensable for a deeper qualitative understanding. discourse analysis and rhetorical strategies, for example, examines how opinions, sentiments, and attitudes are constructed, negotiated, and contested within specific communicative contexts. semantics and pragmatics provide frameworks for understanding the meaning and intended effect of subjective language, considering factors such as the intention of the speaker, the shared knowledge, and the social norms. by integrating computational and qualitative approaches, researchers can achieve a more comprehensive and nuanced understanding of these multifaceted phenomena and uncover stances @xcite @xcite towards climate change. ## aims of the analysis the aims of our analysis are to uncover the social media users' attitudes, embracing sentiment, towards the climate change in two social media datasets from different periods of time and investigate to what extent the public opinions and attitudes towards this issue change within that period of time. as individuals categorize information based on their existing beliefs and attitudes, new information is evaluated in relation to these existing attitudes, leading to either the new information assimilation (acceptance) or contrast (rejection) @xcite . a general theoretical approach adopted here refers to the identification of the users' opinions and attitudes via the sentiments expressed with reference to the opinions and argumentation persuasive effects, also in terms of aristotle's persuasive appeals. an important part of this theory is played by three main factors -logic, emotion and trustbased types of persuasion. logos is the appeal to the logic or reasoning by referring to evidence. pathos is the appeal to the emotions of the audience. trust-based ethos appeal invokes the credibility or trustworthiness of individuals considered the source of information. sentiment marking in datasets can refer to any of the three appeals while opinions have specific content orientation @xcite . that is why some opinionated utterances can be ambiguous between sentence polarity and opinion polarity or between literal and nonliteral (eg ironic) utterances such as e.g., you'd rather drown in these floods than admit global warming. previous attempts at opinion sentiment analysis are multiple and varied. in their (2024) review paper mao et al. @xcite critically assess available systems and highlight their challenges and limitations. to minimize the limitation of these methodologies, two types of sentiment analysis are used in the present study, one based on human marking, which can be more reliable but time-consuming and hence inefficient, and the other -on automatic lexicon-based labels -which is fast but can provide erroneous results with regard to the results of the opinion sentiment marking due to the above mentioned possible distinction between appeal polarity and opinion stance sentiments @xcite . as the results obtained were not fully consistent, we used llms to assist in the clarification of the distinction and to scrutinize a possible statistically significant sentiment flow between the earlier and later public opinion expression. ## literature review the topic of automated sentiment analysis is not new in computational linguistics. the reference to and basing the identification on emotion word dictionaries is typically the first method to think of. however, the emotion sentiment values are often ambiguous outside context. prediction of sentiment data with the use of machinelearning algorithms required heavy pre-processing preparations but it is machine learning that is regarded as being one of the latest and most prevalent techniques in sentiment analysis @xcite . another related problem refers to an unreflexive use of lexicon sentiment analysis for opinion sentiment which could lead to erroneous and untrusthworthy results due to considering sentiment identification synonymous with opinion polarity marking. the most recent model of sentiment analysis and opinion mining is developed by maruthupandi et al. @xcite . this framework (semai) analyzes opinion sentiment by applying ai-based semantic analysis to assess and classify user views from social data. the authors use the degree of correlation network model (dcnm) to extract the subset of basic features from social network data with the meta-heuristics model -heap based optimization (hbo), to reduce the dimensionality of retrieved features. finally, the unique classification algorithm, self-attention based deep analyzing network (sa-dan), is used to identify and categorize attitudes into positive, neutral, and negative. the semai model achieves particularly good results for accuracy, precision, as well as f1-scores in a variety of social data sets. our approach is an attempt to reconcile the lexicon-and text-based sentiment value approaches via contextual bert text embeddings, their clustering and topic labeling and interpretation to observe the dynamicity of opinion shift in public opinion sentiment values related to climate change. ## discussion and conclusions the main aim of this study was to investigate how public opinions expressed in terms of sentiment toward climate change evolved over time. as is known sentiment analysis can be performed at various granularities, each offering different insights, such as the word level, document-, sentence-or aspect-levels. the closest brief description of our methodology employed in the study is a hybrid approach, i.e., a combination of some of these levels. we first specified particular dominant climate-related topics that evolved over time in the two datasets we explored by employing a bert-based clustering approach to extract semantically coherent topics from unstructured texts. each tweet was encoded into a bert-related vector, capturing contextually relevant semantic information. the problem with the two datasets we used was that they were not identical in scope and in the contextual information annotation. some of the sentiment values were assigned not to general topics or main -climate change -targets, but to some lexical entities, peripheral from the point of view of a particular text opinion sentiment. the bert transformer advanced the study, more effectively capturing long-range dependencies and contextual relationships. the analysis revealed dynamic patterns in climate change discourse, frequently characterized by polarization and shifts in dom-inant narratives. a notable observation was how public expressions of sentiment modulates, often reflecting concern, anxiety, or fear, probably intensified during extreme weather events or following significant climate-related news. this suggests a direct link between real-world occurrences and shifts in public opinion, demonstrating the dynamic, responsive nature of public sentiment and highlighting the importance of real-time monitoring for climate communication strategies. an important part of our study was a distinction between lexical emotion sentiment and more general, opinion thematic sentiments that might often contradict. this layering of opinion via the opinion sentiment targets might be an important site of opinion and attitude sentiment distinction in our further studies. there are new proposals to extend the effectiveness of sentiment identification in data such as e.g., semantic analysis based on ai (semai) for assessing and categorizing user views from social data mentioned in the literature review secton here, which applies algorithms in the stages of feature extraction, optimization, and sentiment prediction @xcite with the degree of correlation network model (dcnm) and the heap based optimization model, supported by self-attention based deep analyzing network (sa-dan). and yet, despite significant advancements, opinion-related sentiment analysis continues to face several challenges that would be undertaken in the future studies. they are primarily related to implicit meanings such as figurative language or sarcasm and irony detection. another hurdle, often requiring deep contextual and commonsense understanding, is handling the scope of negation and intensification. as is also encountered in our analyzed data, there are still problems with context and domain dependency as the opinion sentiment can change across contexts and domains as well as target orientation that can vary depending on whether we identify sentiments towards particular entities expressed in lexical units or whether the sentiment of the whole entity (e.g., post or document). an important methodological asset of our study lies in the integration of lexicon-based tools, human annotation, and llm-powered reclassification within a unified framework for sentiment analysis. the use of bert embeddings allowed for a semantically nuanced clustering of topics beyond surface-level lexical similarity, while llms enabled a more contextualized and climate-specific reinterpretation of sentiment. this computational pipeline ensured interpretability and adaptability across datasets with divergent structures. comparing source 1 and source 2 climate change twitter data, some of the opinion sentiment marking agrees with human contextual sentiment recognition (see however, several challenges remain: sentiment models, especially lexicon-based ones like vader, are sensitive to contextual ambiguity, figurative language, and sarcasm. both vader and even llms, despite their contextual strength, may struggle with stance disambiguation when multiple sentiment targets (e.g., politicians vs. climate change) co-occur. an example of such opinion sentiment recognition problems can be encountered in the following case: @tiniebeany climate change is an interesting hustle as it was global warming but the planet stopped warming for 15 yes while the suv boom (dataset 1) such and similar posts are indirect and are interpreted as denying the reality of global climate change, based on the uninformed or disinformed types of opinion. the opinion sentiment is negative, even though the positive lexicon sentiment lexis-"interesting" or "stopped warming"-is used. such contextual clues may be misleading and cause llm neutral marking and positive by vader. problems with llm-generated correct identification of implicit, as well as some of the indirectly conveyed opinions, were first reported in liebeskind and lewandowska-tomaszczyk @xcite . in our present paper more nuanced opinion recognition problems are noted. neither vader nor llm recognized the anti-climate change opinion sentiment, although the lexical form "hustle" may function as an interpretative indicator. another example (dataset 2) also presents questionable climate change sentiment recognition: the people on #climatestrike in dc are marching to confront the banks funding the climate crisis! #firedrillfriday #shut-downdc vader marks the post sentiment as negative, and llm as positive, while the post is immersed in a more complex outside context-#climatestrike confronting bank funding of investments which accelerate climate change. in other words, the post includes two opinion sentiment stances: climate change recognition (positive) and opposition to the institutions contributing to the process (negative). thus, while our methodology enables a richer analysis of opinion dynamics, it also underscores the need for further refinement in handling subtle pragmatic cues and layered sentiment targets. at the same time, this research unequivocally demonstrates that universal opinion is not solely shaped by one mode of persuasion, but rather by a dynamic interplay of appeals. while logos-based arguments provide the foundational framework of factual evidence and logical reasoning that underpins informed perspectives, they are often insufficient on their own to influence widespread belief. we found that emotional appeals are equally critical, tapping into shared human experiences, values, and sentiments to foster resonance and engagement. furthermore, the ethos of the source-its credibility, authority, and perceived trustworthiness-plays a pivotal role in determining the receptiveness of an audience to any given message such as eg. in the case of greta thunberg. our analysis, while comprehensive within its defined parameters, may be limited by its focus on specific social media platforms (twitter) and a fairly short time frame. this can affect the generalizability of findings across more diverse textual sources (e.g. news articles, forums), different cultural contexts, or linguistic variation. one can also indicate a general lack of consideration for user demographics (inaccessible in social media data) and entirely external factors that shape opinions in climate change discourse, a limitation that may also apply to aspects of this study. yet, in our attempt to use topic cluster modeling and fairly intensive scrutiny, some of these threats may have been overcome. there are also specific linguistic-pragmatic features that signal subtle shifts in meaning or stance @xcite . they are hedging devices (e.g., "perhaps," "it seems") and modal verbs (e.g., "may," "might," "could"), which signal caution, uncertainty, or indirectness in expressing claims, first-person pronouns, indicating a subjective stance and authorial presence, nominalizations (such as "global warming") and passive voice constructions (e.g., "it was observed"), which contribute to informational density and objectivity, often favored in factual or scientific discourse. furthermore, the pragmatic analysis can delve more deeply into the contextual and consequential dimensions of language (presupposition, implicature, speaker's intent) and, finally, implicit meanings detection (figurative language, irony, sarcasm). needless to say, our analysis mirrored to a large extent all those important political events that took place in the period between 2015 -2019. the generated thematic clusters contained items such as trump and greta as their main headwords. hence, future investigations should also extend research towards more effective modeling of real-world debates on complex societal issues. conducting comprehensive cross-cultural and cross-linguistic analyses of climate narratives and opinion change is also vital, exploring how language, cultural norms, and communication styles influence opinion dynamics globally. incorporating more realistic social network structures into opinion dynamics simulations will better reflect how opinions propagate and change within diverse social groups. furthermore, a significant leap from merely detecting opinion change to understanding how to influence it responsibly involves investigating causal relationships between narratives, linguistic shifts, and real-world outcomes, moving beyond mere correlation to understand how specific narratives directly influence behavior and societal change. future research might also prioritize refining llms by fine-tuning them with more diverse and real-world human discourse data, including content that reflects polarized or fact-resistant opinions. this is crucial to overcome current llm biases, negatvely influencing e.g., word embeddings, thereby enabling more accurate simulations and analyses of complex opinion dynamics. further exploration of hybrid computational-linguistic models is warranted, combining the strengths of advanced llms with explicit linguistic and pragmatic theories to enhance accuracy, interpretability, and detection of subtle opinion shifts. developing more nuanced and multidimensional representations of opinion, moving beyond scalar sentiment or stance to capture the complexity of human beliefs and attitudes, is also a key direction. following this line of enquiry, our further research in opinionrelated sentiment analysis is thus likely to use a hybrid linguistic nlp research methodology and focus on more robust contextual understanding that can better capture implicit and nuanced language. furthermore, it goes without saying, that incorporating deeper aspects of common sense reasoning would improve opinion sentiment understanding. for future work, exploring alternative topic modeling approaches beyond bert-based clustering, such as latent dirichlet allocation (lda), could offer complementary insights into the underlying thematic structures of climate change discourse. while bert excels in capturing semantic nuances through contextual embeddings, probabilistic topic models like lda could provide a different perspective on topic composition by identifying latent topics based on word cooccurrence patterns, potentially revealing distinct and interpretable themes. this comparative analysis would enrich our understanding of topic dynamics and validate the robustness of identified themes across different methodological paradigms.