Gustavo de Azevedo Reis; William Barbosa

DOI: 10.22167/2675-6528-202604416

Article derived from a Final Course Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by the Pecege Institute for textual synthesis and organization.

Summary

This study applied Natural Language Processing techniques to analyze the sentiment of Brazilian consumers regarding the launch of the Samsung Galaxy S25. The objective was to analyze consumer sentiment, based on 1,320 comments in Portuguese collected on the Reddit platform, from January 2024 to December 2025. The BERTimbau model, pre-trained for Portuguese, was used, fine-tuned for ternary sentiment classification (positive, negative, and neutral), achieving an accuracy of 78.26% and an F1 Macro of 78.21%. The temporal analysis, structured in three phases (rumors and speculations, launch, and post-launch), revealed that negative sentiment predominated in the rumors phase (42.7%), decreased during the launch (25.9%), and stabilized in the post-launch phase (35.7%). The embedded artificial intelligence (Galaxy AI) was the most discussed topic (588 mentions) and predominantly negative (46.9%). The battery was the attribute with the highest dissatisfaction (55.9% negative), being the only one whose perception deteriorated after real use. The camera stood out as the attribute with the best relative evaluation. The results indicated that incremental innovation finds limits in its capacity to generate sufficient perceived value to sustain the product’s premium positioning in the Brazilian market.

Keywords: Sentiment analysis.; BERTimbau; Natural language processing; Reddit; Smartphones.

1. Introduction

In recent decades, the global smartphone market has consolidated as one of the most competitive and dynamic segments in the technology industry. In 2023, the sector generated approximately 410 billion dollars worldwide, with the sale of over 1.2 billion devices (IDC, 2024). In Brazil, around 44 million smartphones were sold in the same year, with a notable growth of over 20% in the premium segment. This movement reinforces the strategic relevance of premium lines for manufacturers and investors, due to their higher average ticket price and direct impact on margins and competitive positioning (IDC, 2024).

In this highly competitive context, consumer perception plays a central role in the formulation of pricing, marketing, brand positioning, and demand forecasting strategies. With the increased use of social media and online review platforms, user opinions have come to play a determining role in the purchase decision process. According to BrightLocal (2023), 76% of consumers state that they consult online reviews when researching products and services. Complementarily, a survey conducted by PowerReviews (2023) indicates that over 90% of consumers consider online reviews a decisive factor in the purchasing process.

Studies such as those by Chevalier and Mayzlin (2006), Cui et al. (2012), and Floyd et al. (2014) show that online reviews have a significant impact on consumer behavior, especially in markets for durable and high-value goods. These studies empirically demonstrate that both the volume and valence of online reviews influence consumers’ quality perception and, consequently, sales. Negative reviews tend to receive more attention, functioning as an uncertainty reduction mechanism, while positive reviews act as a purchase decision confirmation mechanism (Floyd et al., 2014).

In this scenario, Natural Language Processing (NLP) techniques have been widely applied to deepen the analysis of these opinions in digital environments. Lucini et al. (2020), for example, investigated customer satisfaction in the airline sector based on online textual reviews, using sentiment analysis and topic identification. Deep learning-based models, such as those based on transformer-type architectures, show superior performance in sentiment analysis in short and informal texts, characteristic of social media (Araci, 2019). The aggregated sentiment expressed on social media also exerts a relevant influence on corporate reputation and companies’ financial performance (Islam et al., 2025).

In the premium smartphone segment, Samsung Electronics stands out as one of the world’s largest technology conglomerates, with the Galaxy S line representing one of its main revenue sources and positioning itself among the leaders in the global market. The company competes directly with Apple and Xiaomi, which adopt distinct strategies in terms of portfolio, pricing, and positioning. In 2024, Samsung registered approximately 223 million units shipped, with a market share of about 18% (Counterpoint Research, 2025).

The launch of the Samsung Galaxy S25, announced in January 2025, generated a high volume of comments on the digital platform Reddit, especially in the period between the pre-launch, the official announcement, and the weeks following the product’s arrival on the market. Despite the abundance of available data, there is a gap in the literature regarding the systematic analysis of the temporal evolution of consumer sentiment in premium smartphone launches, particularly in the Brazilian context. Understanding this dynamic is crucial for formulating effective strategies in an increasingly competitive market.

This study aims to fill this gap, justified by the need to understand consumer perception to support strategic decisions of companies in the sector. The objective of this study is to apply Natural Language Processing techniques, through the BERTimbau model – an adaptation of BERT pre-trained for the Portuguese language –, to analyze consumer sentiment, based on comments in Portuguese published on the Reddit platform, regarding the launch of the Samsung Galaxy S25.

2. Material and Methods

The research was conducted as an observational study, of an applied nature, based on the analysis of secondary and unstructured data. A quantitative and exploratory approach was adopted, using Data Science and Natural Language Processing (NLP) techniques to investigate the sentiment expressed in social media texts. This methodology allowed for the understanding of consumer perception in a dynamic digital context.

The empirical object of the study consisted of comments in Portuguese published on the digital platform Reddit, specifically related to the launch of the Samsung Galaxy S25 smartphone. The choice of Reddit was justified by its widespread use in disseminating spontaneous opinions about technology products, as observed in opinion mining studies (Lucini et al., 2020).

The temporal scope of data collection covered the period from January 2024 to December 2025. This interval was segmented into three distinct phases to allow for the analysis of consumer sentiment evolution: rumors and speculations (January 2024 to January 21, 2025), launch (January 22 to February 28, 2025), and post-launch (March to December 2025), following the relevance of the temporal dimension in online reviews (Cui et al., 2012; Floyd et al., 2014).

Data collection was performed through the public JSON “endpoint” of the Reddit platform. Specific keywords related to the product were used, including ‘Galaxy S25’, ‘S25 Ultra’, ‘Samsung Galaxy S25’, ‘Samsung S25’, and ‘S25 Plus’. The search was directed to Brazilian subreddits, such as r/brasil, r/brasilivre, and r/tecnologia, and global technology subreddits, such as r/samsung, r/GalaxyS25, and r/Android.

After the initial collection, filtering procedures were applied to ensure that only texts in Portuguese were included in the corpus. Additionally, duplicate messages, automated content, promotional publications, and texts not directly related to the product were excluded, in accordance with methodologies employed in similar research (Cui et al., 2012; Floyd et al., 2014).

The preprocessing of the texts aimed to reduce noise and standardize the corpus for the application of NLP models. The steps included converting all texts to lowercase, removing URLs, user mentions (u/) and subreddit references (r/), Reddit Markdown formatting, emojis, punctuation, and special characters.

Complementarily, “stopwords” from the Portuguese language were eliminated and lemmatization was applied to reduce words to their canonical forms. These procedures are consolidated practices in the literature of opinion mining and sentiment analysis, essential for improving the performance of textual classification models (Hu and Liu, 2004; Araci, 2019; Lucini et al., 2020).

Sentiment analysis was conducted using the BERTimbau model, an adaptation of the BERT (Bidirectional Encoder Representations from Transformers) model pre-trained for the Portuguese language (Souza, Nogueira and Lotufo, 2020). The choice of BERTimbau was justified by the composition of the corpus in Portuguese and the superior performance of “transformers”-based architectures in sentiment analysis tasks (Araci, 2019).

The BERTimbau model is based on the “transformer” architecture (Vaswani et al., 2017), which employs self-attention mechanisms to process textual sequences in parallel and capture long-range dependencies. BERT, in turn, exclusively uses “encoder” blocks from the “transformer” for bidirectional modeling of textual context (Devlin et al., 2019).

The BERTimbau model was fine-tuned for the task of classifying comment polarity into three categories: positive, negative, and neutral. The final layers of the model were adapted in a supervised manner, allowing the contextual representations learned during pre-training to be adjusted to the specific domain of smartphone comments.

After classifying the comments, the data were temporally aggregated to analyze the evolution of consumer sentiment over the studied period. This aggregation allowed for the comparison of sentiment between the rumor and speculation, launch, and post-launch phases of the product, according to the importance of dynamic analyses (Cui et al., 2012).

Additionally, exploratory analyses of comments classified as positive and negative were performed. The objective was to identify recurring themes and product attributes most frequently associated with consumer reviews. The frequency and occurrence of terms related to aspects such as performance, price, design, battery, and functionalities were examined (Hu and Liu, 2004; Floyd et al., 2014).

Regarding ethical considerations, the research exclusively utilized publicly accessible secondary data, with no direct interaction with the authors of the analyzed publications. No personal information or sensitive data were collected, ensuring user anonymity. The study complied with the ethical guidelines applicable to research based on public data (Cui et al., 2012; Lucini et al., 2020).

3. Results and Discussion

The sentiment analysis of Brazilian consumers regarding the launch of the Samsung Galaxy S25 was based on a corpus of 1,320 texts in Portuguese, collected from the Reddit platform between January 2024 and December 2025. Of this total, 1,279 texts, representing 96.9% of the corpus, consisted of comments, while 41 (3.1%) were posts initiating discussions. The average text length was 211.5 characters, with a median of 110 characters, reflecting the concise and informal nature of social media interactions. This predominance of comments over posts is a reflection of Reddit’s dynamics, where discussions develop from a limited number of initial topics.

The temporal distribution of the corpus revealed two periods of intense discussion activity. The first occurred in the first semester of 2024, with notable peaks in February, registering 154 texts, and in April, with 172 texts. These peaks coincided with phases of leaks and speculation about the characteristics of the Galaxy S25. After a period of lower volume between July and November 2024, the last quarter of 2025 showed a significant increase in discussions, with 125 texts in October, 106 in November, and 188 in December. This temporal pattern suggests a maturation of the product’s user experience and a possible increase in discussions driven by year-end promotions or comparisons with competing models launched throughout the year.

The evaluation of the BERTimbau model, after the fine-tuning process for ternary sentiment classification, demonstrated robust performance. The model achieved an accuracy of 78.26% and a Macro F1 of 78.21% on the test set. The negative class obtained the best individual performance, with a Precision of 82.51%, Recall of 78.65%, and F1-Score of 80.53%. The positive class presented the highest Recall, at 82.41%, indicating that most positive texts were correctly identified, although with a Precision of 74.17%, suggesting some misclassifications of neutral texts as positive. The neutral class, in turn, registered a balanced performance, with a Precision of 76.67%, Recall of 75.41%, and F1-Score of 76.03%.

The confusion matrix analysis revealed that the model’s main source of error was concentrated on the boundary between the negative and neutral classes. Of the 192 negative samples in the test set, 151 were classified correctly, with 29 confused with the neutral class and 12 with the positive. Of the 183 neutral samples, 138 were correctly classified, 26 assigned to the negative class, and 19 to the positive. Of the 108 positive samples, 89 were classified correctly, 13 confused with the neutral class, and only 6 with the negative. This behavior is expected in informal social media texts, where the critical and analytical tone frequently transitions between these categories, making clear distinction difficult.

The model’s average confidence in its predictions was 96.1%, with the probability distribution assigned to each class exhibiting bimodal behavior. This indicates that the model tends to classify texts with a high degree of certainty, without a significant concentration of cases in the ambiguity zone. The analysis of the confidence distribution by predicted class confirmed this robustness, with the three categories showing medians above 0.97, and a reduced proportion of low-confidence outliers, which reinforces the reliability of the classifications made by the BERTimbau model.

After applying the model to the complete corpus, the general distribution of sentiments revealed a predominance of neutral comments, which represented 39.8% (526 texts), closely followed by negative ones, with 38.3% (505 texts), and positive ones, with 21.9% (289 texts). This pattern is consistent with the literature on sentiment analysis in social media, which frequently observes a higher frequency and detail in consumer dissatisfaction expressions. This trend can be interpreted as an uncertainty reduction mechanism in the decision-making process, where negative reviews receive greater attention (Floyd et al., 2014).

A complementary analysis by content type showed an expressive difference between posts and comments. Posts that initiate discussions presented a negative sentiment in 48.8% of cases and positive in only 4.9%. In contrast, comments exhibited a more balanced distribution, with 37.9% negative sentiment and 22.4% positive. This finding suggests that the motivation to initiate a new discussion on Reddit is strongly associated with dissatisfaction, while positive opinions tend to emerge reactively, as contributions to ongoing conversations. This asymmetry has methodological implications, indicating that metrics based exclusively on publication volume may overestimate negative sentiment.

Engagement analysis, measured by the number of “upvotes”, revealed no statistically significant correlation between comment sentiment and engagement received (Spearman p = 0.031; p = 0.259). Although positive texts showed a slightly higher average score (9.69) compared to negative (7.55) and neutral (3.63) ones, the medians were equal to 1.0 for all three categories. This result indicates that comment visibility on Reddit is not significantly influenced by sentiment polarity, granting balanced representativeness to the analyzed corpus and reducing the risk of exposure bias in the results.

The temporal sentiment analysis, structured in three phases of the product lifecycle, revealed distinct dynamics. In the rumors and speculation phase, which covered 585 texts (44.3% of the corpus), the highest proportion of negative sentiment was observed, at 42.7%, accompanied by 34.5% neutral and 22.7% positive sentiment, resulting in an average sentiment of 0.80. This pattern suggests that leaks and speculation about incremental changes from the previous model fueled negative expectations, a finding consistent with studies highlighting the role of early reviews in shaping pre-launch perceptions (Cui et al., 2012).

In the launch phase, corresponding to the Galaxy Unpacked event and the product’s arrival in the Brazilian market, 58 texts were analyzed. Negative sentiment receded to 25.9%, neutral sentiment rose to 50.0%, and positive sentiment reached 24.1%, elevating the average sentiment to 0.98, the highest among the three phases. This reconfiguration indicates that direct contact with the official specifications and the first “reviews” balanced consumer perception, partially correcting the negative bias formed in the previous phase. This behavior is in line with literature that points to the role of positive reviews as a confirmation mechanism for purchase decisions (Chevalier and Mayzlin, 2006; Elwalda and Lü, 2016). However, the reduced volume of texts in this phase imposes caution in generalization, although the trend is consistent.

In the post-launch phase, the most voluminous with 673 texts (51.0% of the corpus), the sentiment stabilized at a higher equilibrium level, with 43.2% neutral, 35.7% negative, and 21.1% positive, and an average sentiment of 0.85. The reduction in negative sentiment compared to the rumor phase (from 42.7% to 35.7%) indicates that, although criticism persisted, the actual usage experience contributed to a less polarized perception. The time series with a seven-day moving average allowed observation of the daily evolution of sentiment, predominantly oscillating between 0.6 and 1.0 in the rumor phase, reflecting skepticism. A sharp peak of positive sentiment was observed in early February 2025, coinciding with the first impressions and usage analyses of the product.

Post-launch, oscillations became more frequent, with occasional positive peaks, such as in December 2025, and recurring negative troughs. This indicates that consumer perception is continuously influenced by contextual factors, such as software updates, price variations, and competitor launches. Attribute-based sentiment analysis allowed for the identification of specific dimensions that guide consumer perception, revealing the complexity of user opinions.

Embedded artificial intelligence, named Galaxy AI, stood out as the most discussed attribute, with 588 mentions. Despite the high volume of mentions, the associated sentiment was predominantly negative, at 46.9%, resulting in a “sentiment score” of 0.73, with 32.7% neutral and 20.4% positive. The high volume of mentions confirms the success of Samsung’s strategy in positioning AI at the center of discussions. However, the predominance of negative sentiment indicates that Brazilian consumers did not perceive sufficient tangible value in the offered functionalities to justify the device’s premium positioning.

The battery emerged as the attribute with the highest proportion of negative sentiment, reaching 55.9% (102 mentions), and the lowest “sentiment score” of 0.66. This result highlights that maintaining the same 5,000 mAh capacity as the previous model generated significant dissatisfaction. The software, specifically the One UI 7 interface, presented 53.6% negative sentiment (207 mentions), suggesting that aspects related to the system’s user experience, such as stability and interface changes, constitute relevant sources of user dissatisfaction.

The device’s design registered 53.5% negativity (114 mentions), indicating that the perception of visual innovation across generations was considered insufficient by consumers. In contrast, the camera proved to be the attribute with the best relative evaluation, with 25.9% positive sentiment and 41.4% negativity, and a “sentiment score” of 0.85, the highest among attributes with a significant volume of mentions. This result is consistent with the significant update made by Samsung to the ultrawide sensor, which went from 12 to 50 megapixels, representing a noticeable and valued improvement by consumers. The literature on opinion mining supports that highly relevant functional attributes exert a disproportionate influence on the overall product perception, especially when consumer expectations are met or exceeded (Hu and Liu, 2004).

Comparisons with competitors showed distinct patterns. Mentions involving Apple registered 52.5% negative sentiment (198 mentions), reflecting the rivalry between brands and consumers’ tendency to use comparison as a tool for criticism. Comparisons with Xiaomi, although in lower volume (62 mentions), registered the lowest negativity index among comparisons (40.3%) and the highest percentage of neutral sentiment (37.1%), with a “sentiment score” of 0.82. This suggests that consumers perceive the Chinese brand as an alternative that offers better value for money. The comparison with the previous model, the Galaxy S24, registered 46.4% negative sentiment (112 mentions), evidencing that a significant portion of consumers did not perceive sufficient evolution between generations to justify the upgrade. Price, the second most discussed attribute (225 mentions), presented 42.7% negative sentiment and a “sentiment score” of 0.81, reflecting the recurring perception that Samsung’s premium pricing in the Brazilian market faces significant resistance.

The intersection between attributes and temporal phases revealed dynamics particularly relevant for understanding the evolution of consumer perception throughout the product lifecycle. The battery, for example, showed a continuous deterioration trajectory, with negative sentiment increasing from 51.5% in rumors to 64.7% post-launch, and the “sentiment score” declining from 0.71 to 0.56. This was the only attribute whose perception significantly worsened after real and prolonged use of the device, suggesting that autonomy expectations were not met by the daily usage experience and that battery capacity stagnation constitutes a growing competitive vulnerability throughout the product lifecycle.

Conversely, the camera and price showed a significant improvement in the launch phase, with positive sentiment reaching 50.0% and 42.9% respectively. This indicates that the initial user impressions validated expectations regarding photographic quality and that any launch offers mitigated resistance to the price. Artificial intelligence, on the other hand, maintained a relatively stable behavior, with negative sentiment oscillating between 47.5% in rumors and 43.7% post-launch, indicating that neither the actual user experience nor the perceived value of AI features substantially altered consumer skepticism. Performance showed the highest negativity indices in all phases, with 60.2% in rumors and 59.8% post-launch, suggesting that, despite the Snapdragon 8 Elite processor, consumer expectations regarding performance remained high and partially unmet. The design, in turn, showed a relevant improvement between the rumor phase (59.3% negative) and post-launch (44.7% negative), indicating that the tactile and visual experience of the product mitigated the initial criticisms formulated based on images and leaks.

In summary, the results demonstrate that consumer perception is not static, but evolves in a differentiated way according to the attribute analyzed and the product life cycle phase. While the camera, price, and design showed significant improvement after direct contact with the product, the battery stood out as a notable exception, with a trajectory of continuous deterioration. Artificial intelligence and performance maintained relatively stable negative patterns, suggesting that the user experience was not sufficient to alter the perceptions formed during the speculation phase. These findings reinforce the importance of multidimensional analyses that integrate the temporal dimension and product attributes for a deeper understanding of the dynamics of consumer sentiment in premium smartphone launches, highlighting that incremental innovation finds limits in its capacity to generate sufficient perceived value to sustain the product’s premium positioning in the Brazilian market.

4. Conclusion

This study aimed to analyze the sentiment of Brazilian consumers regarding the launch of the Samsung Galaxy S25, using Natural Language Processing techniques on comments in Portuguese collected from the Reddit platform. The successful application of the BERTimbau model, fine-tuned for ternary sentiment classification, was verified, achieving an accuracy of 78.26% and an F1 Macro of 78.21%. Temporal analysis revealed that negative sentiment predominated during the rumor and speculation phase, decreased during the launch, and stabilized in the post-launch period. On-device artificial intelligence (Galaxy AI) was identified as the most discussed feature, but with predominantly negative sentiment, while the battery emerged as the attribute with the greatest dissatisfaction, being the only one whose perception deteriorated after actual device use. In contrast, the camera stood out with the best relative evaluation. The findings indicated that incremental innovation faces limits in its capacity to generate sufficient perceived value to sustain the product’s premium positioning in the Brazilian market.

The main contribution of this study lies in demonstrating the feasibility and relevance of applying NLP techniques for a multidimensional analysis of consumer perception in premium smartphone launches in the Brazilian market. The results offer practical insights for business strategies, highlighting that communication about artificial intelligence, while effective in generating visibility, did not translate into a positive perception of value, suggesting the need to focus on practical use cases. The deterioration of perception regarding the battery, the only attribute to worsen after actual use, points to a competitive vulnerability and the need for investments in its capacity. It was observed that the reduced volume of texts in the launch phase imposes caution in generalizing some results. For the future, companies can consider expectation management strategies during the rumor period to mitigate initial negative bias, and analyses that complement the volume of publications with sentiment distribution to avoid overestimating negativity.

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Article originating from the Final Course Work of the Specialization in Data Science and Analytics of the MBA USP/Esalq

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