Sentiment Analysis Model Research Articles

OSINT (OPEN SOURCE INTELLIGENCE) Exploring the Power of Open Source Intelligence in Modern Decision-Making

Open Source Intelligence (OSINT) has emerged as a powerful tool in the information age, offering valuable insights to individuals, organizations, and governments. This paper explores the significance of OSINT in contemporary decision-making processes, highlighting its role in providing timely, relevant, and actionable information from publicly available sources. The first section elucidates the concept of OSINT, delineating its scope, sources, and methodologies. OSINT encompasses a wide array of publicly accessible information, including social media posts, news articles, government reports, and academic publications. Leveraging advanced data mining, web scraping, and analytical techniques, OSINT practitioners sift through this vast trove of data to extract pertinent insights. The subsequent section delves into the multifaceted applications of OSINT across various domains. In the realm of national security, OSINT aids in threat assessment, geopolitical analysis, and monitoring of adversarial activities. Law enforcement agencies utilize OSINT for criminal investigations, intelligence gathering, and identifying emerging trends. Moreover, businesses employ OSINT for competitive intelligence, market research, and brand monitoring, gaining a competitive edge in dynamic markets. The paper also examines the ethical and privacy implications inherent in OSINT practices. While OSINT offers unprecedented access to information, it raises concerns regarding privacy infringement, misinformation propagation, and algorithmic biases. Safeguarding individual privacy rights and ensuring data accuracy are imperative considerations in the ethical utilization of OSINT. Furthermore, the paper discusses the evolving landscape of OSINT technologies and methodologies. Advancements in artificial intelligence, natural language processing, and machine learning have revolutionized OSINT capabilities, enabling automated data collection, sentiment analysis, and predictive modeling. However, these technological advancements also pose challenges in terms of information overload, data veracity, and algorithmic transparency.

Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework

The recent development of communication technologies made it possible for people to share opinions on various social media platforms. The opinion of the people is converted into small-sized textual data. Aspect Based Sentiment Analysis (ABSA) is a process used by businesses and other organizations to assess these textual data in order to comprehend people’s opinions about the services or products offered by them. The majority of earlier Sentiment Analysis (SA) research uses lexicons, word frequencies, or black box techniques to obtain the sentiment in the text. It should be highlighted that these methods disregard the relationships and interdependence between words in terms of semantics. Hence, an efficient ABSA framework to determine the sentiment from the textual reviews of the customers is developed in this work. Initially, the raw text review data is collected from the standard benchmark datasets. The gathered text reviews undergo text pre-processing to neglect the unwanted words and characters from the input text document. The pre-processed data is directly provided to the feature extraction phase in which the seq2seq network and transformer network are employed. Further, the optimal features from the two resultant features are chosen by utilizing the proposed Modified Bird Swarm-Ladybug Beetle Optimization (MBS-LBO). After obtaining optimal features, these features are fused together and given to the final detection model. Consequently, the Optimized Cascaded Long Short Term Memory (OCas-LSTM) is proposed for predicting the sentiments from the given review by the users. Here, the parameters are tuned optimally by the MBS-LBO algorithm, and also it is utilized for enhancing the performance rate. The experimental evaluation is made to reveal the excellent performance of the developed SA model by contrasting it with conventional models.

The public attitude towards ChatGPT on reddit: A study based on unsupervised learning from sentiment analysis and topic modeling.

ChatGPT has demonstrated impressive abilities and impacted various aspects of human society since its creation, gaining widespread attention from different social spheres. This study aims to comprehensively assess public perception of ChatGPT on Reddit. The dataset was collected via Reddit, a social media platform, and includes 23,733 posts and comments related to ChatGPT. Firstly, to examine public attitudes, this study conducts content analysis utilizing topic modeling with the Latent Dirichlet Allocation (LDA) algorithm to extract pertinent topics. Furthermore, sentiment analysis categorizes user posts and comments as positive, negative, or neutral using Textblob and Vader in natural language processing. The result of topic modeling shows that seven topics regarding ChatGPT are identified, which can be grouped into three themes: user perception, technical methods, and impacts on society. Results from the sentiment analysis show that 61.6% of the posts and comments hold favorable opinions on ChatGPT. They emphasize ChatGPT's ability to prompt and engage in natural conversations with users, without relying on complex natural language processing. It provides suggestions for ChatGPT developers to enhance its usability design and functionality. Meanwhile, stakeholders, including users, should comprehend the advantages and disadvantages of ChatGPT in human society to promote ethical and regulated implementation of the system.

A Multimodal Sentiment Analysis Approach Based on a Joint Chained Interactive Attention Mechanism

The objective of multimodal sentiment analysis is to extract and integrate feature information from text, image, and audio data accurately, in order to identify the emotional state of the speaker. While multimodal fusion schemes have made some progress in this research field, previous studies still lack adequate approaches for handling inter-modal information consistency and the fusion of different categorical features within a single modality. This study aims to effectively extract sentiment coherence information among video, audio, and text and consequently proposes a multimodal sentiment analysis method named joint chain interactive attention (VAE-JCIA, Video Audio Essay–Joint Chain Interactive Attention). In this approach, a 3D CNN is employed for extracting facial features from video, a Conformer is employed for extracting audio features, and a Funnel-Transformer is employed for extracting text features. Furthermore, the joint attention mechanism is utilized to identify key regions where sentiment information remains consistent across video, audio, and text. This process acquires reinforcing features that encapsulate information regarding consistency among the other two modalities. Inter-modal feature interactions are addressed through chained interactive attention, and multimodal feature fusion is employed to efficiently perform emotion classification. The method is experimentally validated on the CMU-MOSEI dataset and the IEMOCAP dataset. The experimental results demonstrate that the proposed method significantly enhances the performance of the multimodal sentiment analysis model.

Differing Content and Language Based on Poster-Patient Relationships on the Chinese Social Media Platform Weibo: Text Classification, Sentiment Analysis, and Topic Modeling of Posts on Breast Cancer.

Breast cancer affects the lives of not only those diagnosed but also the people around them. Many of those affected share their experiences on social media. However, these narratives may differ according to who the poster is and what their relationship with the patient is; a patient posting about their experiences may post different content from someone whose friends or family has breast cancer. Weibo is 1 of the most popular social media platforms in China, and breast cancer-related posts are frequently found there. With the goal of understanding the different experiences of those affected by breast cancer in China, we aimed to explore how content and language used in relevant posts differ according to who the poster is and what their relationship with the patient is and whether there are differences in emotional expression and topic content if the patient is the poster themselves or a friend, family member, relative, or acquaintance. We used Weibo as a resource to examine how posts differ according to the different poster-patient relationships. We collected a total of 10,322 relevant Weibo posts. Using a 2-step analysis method, we fine-tuned 2 Chinese Robustly Optimized Bidirectional Encoder Representations from Transformers (BERT) Pretraining Approach models on this data set with annotated poster-patient relationships. These models were lined in sequence, first a binary classifier (no_patient or patient) and then a multiclass classifier (post_user, family_members, friends_relatives, acquaintances, heard_relation), to classify poster-patient relationships. Next, we used the Linguistic Inquiry and Word Count lexicon to conduct sentiment analysis from 5 emotion categories (positive and negative emotions, anger, sadness, and anxiety), followed by topic modeling (BERTopic). Our binary model (F1-score=0.92) and multiclass model (F1-score=0.83) were largely able to classify poster-patient relationships accurately. Subsequent sentiment analysis showed significant differences in emotion categories across all poster-patient relationships. Notably, negative emotions and anger were higher for the "no_patient" class, but sadness and anxiety were higher for the "family_members" class. Focusing on the top 30 topics, we also noted that topics on fears and anger toward cancer were higher in the "no_patient" class, but topics on cancer treatment were higher in the "family_members" class. Chinese users post different types of content, depending on the poster- poster-patient relationships. If the patient is family, posts are sadder and more anxious but also contain more content on treatments. However, if no patient is detected, posts show higher levels of anger. We think that these may stem from rants from posters, which may help with emotion regulation and gathering social support.

Tweeting about fatphobia and body shaming: A retrospective infodemiological study

BackgroundWe aimed to analyze reactions to tweets that employed weight-based discrimination terms and to compare them to others posted by the same users on X (formerly Twitter). MethodsWe collected tweets featuring weight-based discrimination terms (the study group) and created a control group by randomly selecting up to five tweets from each account that did not mention any such terms. Descriptive statistics, sentiment analysis, and logistic regression modeling were used to compare the numbers of likes and retweets of the study and control groups, and to understand the emotions associated with these tweets. ResultsOur analysis included 22,075 study group tweets and 50,341 control group tweets. Sentiment analysis was conducted on 41,403 (57.2%) tweets, with 65.7% of the study group tweets being found to contain negative sentiments. The study group had a higher median of likes (1 [0–4]) and retweets (0 [0–0]) than the control group (1 [0–2] and 0 [0–0], respectively, with the study group obtaining higher mean ranks in both comparisons, P < 0.001). Multivariable logistic regression analysis revealed that tweets using weight-based discrimination terms gained more likes (OR = 1.22; 95% CI: 1.16–1.28) and retweets (OR = 1.61; 95% CI: 1.49–1.74), independent of, for example, verification status, follower count, year and season of the tweet, and emotional expression of the tweet. ConclusionsTweets concerning fatphobia, body shaming, and similar terms gain more reactions than others posted by the same accounts.

Analytics of motivational factors of educational video games: LDA topic modeling and the 6 C’s learning motivation model

AbstractThis research studies the motivational factors used in educational video games through the lens of 6 C’s learning motivation model with text mining of the players’ reviews and comments. This research seeks to offer insight for game producers and educational institutions to investigate the effectiveness of these motivators for increasing player motivations and thus improving the quality of learning. Sentiment analysis and LDA topic modeling were used to analyze reviews of five selected video games on the Steam platform. The 6 C’s Learning Motivation Model guided text mining to analyze the motivational factors used in the games and how they contribute to user learning. The effectiveness of these motivational factors was discussed in conjunction with categorized text mining. Results show that the major motivation factors of educational games are ‘construction meaning’, ‘challenge’, and ‘control’ in the 6 C’s learning motivation model. Among them, users focus on whether the game’s content meets their interests and the construction of the educational meaning of the game. The advantage of control, a high degree of motivational factor in video games, may turn out to be a factor that leads to user churning when the game is not interesting or attractive enough. Previous educational game research seldom involved a large sample size for generalizable findings. In addition, this research extends the application of the 6 C’s learning motivation model to the digital educational gaming arena, providing a novel player-centric perspective. Based on the results, we provide recommendations and design considerations for educational game developers to enhance players’ experience and motivations.

Similar modality completion-based multimodal sentiment analysis under uncertain missing modalities

Recently, uncertain missing modalities in multimodal sentiment analysis (MSA) brings a new challenge for sentiment analysis. However, existing research cannot accurately complete the missing modalities, and fail to explore the advantages of the text modality in MSA. For the above problems, this work develops a Similar Modality Completion based-MSA model under uncertain missing modalities (termed as SMCMSA). Firstly, we construct the full modalities samples database (FMSD) by screening out the full modality samples from the whole multimodal dataset, and then predicting and marking the sentiment labels of each modality of the samples with three pre-trained unimodal sentiment analysis model (PTUSA). Next, for completing the uncertain missing modalities, we propose a set of missing modalities completion strategies based on the similar modalities selected from FMSD. For the completed multimodal data, we first encode the text, video and audio modality using the encoder of transformer, then we fuse the representation of text into the representations of video and audio under the guidance of a pre-trained model, thereby improving the quality of video and audio. Finally, we conduct sentiment classification based on the representations of text, video and audio with the softmax function respectively, and get the final decision with the decision-level fusion method. Based on benchmark datasets CMU-MOSI and IEMOCAP, extensive experiments have been conducted to verify that our proposed model SMCMSA has better performance than that of the state-of-the-art baseline models.The codes of our model are available at https://github.com/Astro2Sun/SMCMSA.

Characteristics of Post-pandemic International Chinese Language Learning: Insights from X-Data

This study uses sentiment analysis and topic modeling to analyze X-data on Chinese language learning both before and after the pandemic. Our analysis reveals shifts in sentiment and topics, as well as their underlying causes. We observe a notable increase in online learning as the primary mode of international Chinese language learning, accompanied by an escalated demand for high quality online learning resources. There is also a discernible polarization of learning sentiments in the post-pandemic era. In addition, the former emphasis on cultural exchange in language learning has shifted to economic and trade interactions, highlighting the commercial facets of international Chinese education.

A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data

Social media platforms, such as X (Twitter), contain enormous amounts of data, and collecting valuable information from that data assists in making more informed decisions. In recent years, governments and institutions have begun to explore the possibilities of utilizing social networks as a platform to supply and enhance the quality of their services. Consequently, there is an increased demand to estimate people’s satisfaction with Turkish Universities written in Turkish wherever it is challenging and requires more effort to cope with its rich morphological structure to perform a Sentiment Analysis task. This study proposes a Turkish text sentiment analysis methodology for estimating people’s satisfaction with Turkish Universities by employing nine conventional machine learning models, deep learning techniques, and BERT-based transformers upon an original manually annotated dataset consisting of 17,793 tweets in Turkish. An innovative hybrid architecture, named BERT-BiLSTM-CNN, integrates Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Long Short-Term Memory (BiLSTM), and a triple parallel Convolutional Neural Network (CNN) branch, achieving exceptional accuracy in sentiment analysis of Turkish tweets. During testing, the proposed architecture revealed an impressive accuracy rate of over 0.9101, an F1 Score of 0.8801, and a Receiver Operating Characteristic (ROC) of 0.9632 for analyzing sentiment, demonstrating that the model outperformed the state-of-the-art models, and it is capable of coping with the linguistic complexities of Turkish sentiment analysis. This work provides new insights into sentiment analysis by proposing a hybrid model that combines several computational methodologies to improve the understanding of attitudes.

Consumer Feedback Analysis Using LDA Approach in Cross-border E-commerce

Purpose - The objective of this research is to gain insight into consumer feedback regarding cross-border e-commerce, which is a pivotal avenue for companies venturing into global markets. It seeks to investigate the variety of themes present in consumer feedback in order to gain a more profound understanding of their preferences and concerns, ultimately influencing future market strategies. Design/Methodology – The present study utilizes an extensive collection of textual online evaluations and a text-mining LDA framework to analyze and classify potential themes in consumer feedback pertaining to cross-border e-commerce platforms. By quantifying the sentiment tendencies in consumer-related comments, sentiment analysis models that rely on emotion dictionaries as a foundation identify the polarity and intensity of emotions. Findings – The present study examined 1,500 consumer evaluations pertaining to Korean cosmetic products, sourced from a cross-border platform in Southeast Asia. The analysis unveiled five key subjects frequently referenced in consumer feedback. A subsequent sentiment analysis revealed that a significant proportion of consumer sentiments were positive. Nevertheless, consumers identified certain concerns in the minority of negative reviews, including packaging and delivery, product quality, and the absence of freebies. Originality/value - In a practical sense, this provides significant ramifications for the optimization of consumer experiences and the development of successful market strategies in the ever-changing global marketplace. This is especially advantageous for organizations striving for success in the fiercely competitive cross-border e-commerce industry. This study exceeds conventional survey methods in its methodology by offering more comprehensive and datacentric understandings of consumer attitudes and behaviors.

SentiGEN: Synthetic Data Generator for Sentiment Analysis

Obtaining high-quality, diverse, accurate datasets for sentiment analysis has always been a significant challenge. Traditional approaches include annotators, which may introduce bias to datasets and are also time-consuming and expensive. These types of datasets may also not represent the variety needed to train robust and generalizable sentiment analysis models. This study introduces a novel combination of techniques to approach the problem with a novel solution. The proposed system, SentiGEN includes the use of a transformer, T5, fine-tuned and optimized using an evolutionary algorithm to generate high-quality, diverse, accurate data for sentiment analysis. The generated data is validated using XLNet to ensure high sentiment accuracy. This combination of technologies has proven successful based on the results derived from evaluating multiple models. From complex transformers such as BERT to more straightforward approaches like KNN, those trained using synthetic data demonstrated superior performance compared to their counterparts trained on real data. This enhancement in predictive accuracy was observed when evaluated on benchmark datasets such as SST-2 and Yelp. SentiGEN can generate high-quality, diverse, accurate, realistic data for sentiment analysis and successfully increased the performance of models trained on synthetic data compared to the same model trained on real data.

Sentiment Analysis of the Sheikh Zayed Grand Mosque’s Visitor Reviews on Google Maps Using the VADER Method

The Sheikh Zayed Grand Mosque in Solo is a replica of the Zayed Grand Mosque in Abu Dhabi. Many people have provided reviews on Google Maps after visiting the mosque. This research aims to determine the sentiment results regarding visitors’ reviews by developing a sentiment analysis model using a combination of the Valance Aware Dictionary for Sentiment Reasoning (VADER) and Deep Translator methods. This research was conducted in two phases. The first phase proposed a sentiment analysis model using VADER and Deep-Translator with public datasets. Later, the resulting sentiment analysis model was applied in the second phase to analyze the dataset of mosque visitor reviews and determine public perceptions. This research compares two preprocessing models (PPTV1 and PPTV2) and continues with the translation and sentiment prediction processes. The evaluation results show the proposed model (PPTV2) achieved the best average accuracy values of 72%, precision of 83%, recall of 72%, and F1-Score of 75% for the three examined datasets. The results of visitor review sentiment obtained showed 83.3% positive, 9.5% neutral, and 7.2% negative. The analysis findings show that people are amazed by the beauty and majesty of the mosque. However, some people provide negative reviews of the mosque’s facilities.

A syntactic features and interactive learning model for aspect-based sentiment analysis

The aspect-based sentiment analysis (ABSA) consists of two subtasks: aspect term extraction (AE) and aspect term sentiment classification (ASC). Previous research on the AE task has not adequately leveraged syntactic information and has overlooked the issue of multi-word aspect terms in text. Current researchers tend to focus on one of the two subtasks, neglecting the connection between the AE and ASC tasks. Moreover, the problem of error propagation easily occurs between two independent subtasks when performing the complete ABSA task. To address these issues, we present a unified ABSA model based on syntactic features and interactive learning. The proposed model is called syntactic interactive learning based aspect term sentiment classification model (SIASC). To overcome the problem of extracting multi-word aspect terms, the model utilizes part-of-speech features, words features, and dependency features as textual information. Meanwhile, we designs a unified ABSA structure based on the end-to-end framework, reducing the impact of error propagation issues. Interaction learning in the model can establish a connection between the AE task and the ASC task. The information from interactive learning contributes to improving the model’s performance on the ASC task. We conducted an extensive array of experiments on the Laptop14, Restaurant14, and Twitter datasets. The experimental results show that the SIASC model achieved average accuracy of 84.11%, 86.65%, and 78.42% on the AE task, respectively. Acquiring average accuracy of 81.35%, 86.71% and 76.56% on the ASC task, respectively. The SIASC model demonstrates superior performance compared to the baseline model.

A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM

Sentiment analysis is an essential task in natural language processing that involves identifying a text’s polarity, whether it expresses positive, negative, or neutral sentiments. With the growth of social media and the Internet, sentiment analysis has become increasingly important in various fields, such as marketing, politics, and customer service. However, sentiment analysis becomes challenging when dealing with foreign languages, particularly without labelled data for training models. In this study, we propose an ensemble model of transformers and a large language model (LLM) that leverages sentiment analysis of foreign languages by translating them into a base language, English. We used four languages, Arabic, Chinese, French, and Italian, and translated them using two neural machine translation models: LibreTranslate and Google Translate. Sentences were then analyzed for sentiment using an ensemble of pre-trained sentiment analysis models: Twitter-Roberta-Base-Sentiment-Latest, bert-base-multilingual-uncased-sentiment, and GPT-3, which is an LLM from OpenAI. Our experimental results showed that the accuracy of sentiment analysis on translated sentences was over 86% using the proposed model, indicating that foreign language sentiment analysis is possible through translation to English, and the proposed ensemble model works better than the independent pre-trained models and LLM.

Aspect-Level Sentiment Analysis of Customer Reviews Based on Neural Multi-task Learning

In the era of big data, major e-commerce platforms are facing the challenge of an exponential growth in the number of user comments. Effectively utilizing these comments has become an urgent issue. Traditional manual statistical methods are no longer able to meet the demands for accuracy and real-time analysis. The rise of artificial intelligence-based text mining techniques provides a new approach to address this problem. By building deep learning analysis models, it is possible to uncover user preferences and product characteristics, helping businesses proactively adjust sales strategies, improve product and service quality, and achieve precise marketing. Text sentiment analysis is one of the important research directions in the field of text mining. Its basic idea is to transform subjective texts with emotional color into structured data, and then use machine learning, deep learning, and other artificial intelligence technologies to extract emotional features and discover knowledge patterns. This article proposes a deep learning-based sentiment analysis model, which includes a shared sentiment prediction layer used to transfer emotional knowledge between different aspect categories and alleviate the problem of insufficient data. The model consists of two parts: the Aspect Category Detection (ACD) classifier based on attention mechanism and the Aspect Category Sentiment Analysis (ACSA) classifier. The ACD part generates word weights using attention mechanism, while the ACSA part predicts the sentiment of words and combines weights to determine the sentiment of aspect categories within sentences.

Enhancing image sentiment analysis: A user-centered approach through user emotions and visual features

To bridge the gap between user understanding of emotions and image representation, this paper amalgamated a variety of user features to formulate an image sentiment analysis model. Through controlled user experiments, participants were engaged in discerning sentiment from abstract painting images. User visual features were derived from eye-tracking data collected during the experiment. This paper employed the FFM (Five Factor Model) and POMS (Profile of Mood States) methodologies to gather personality and mood data, which were utilized as user emotional features. Additionally, fundamental components of abstract painting were captured as basic image features. By synergizing features across these three dimensions, the study introduced the "User-Feature-Enhanced-Image-Sentiment-Model" (UFE-ISM) for image sentiment recognition. The research findings underscore the model's superior performance in sentiment classification on abstract painting. Notably, both basic image features and user visual features exhibited significant contributions to the image sentiment classification model.

Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art

The emergence of AI Art has ignited extensive debates on social media platforms. Various online communities have expressed their opinions on different facets of AI Art and participated in discussions with other users, leading to the generation of a substantial volume of data. Analyzing these data can provide useful insights into the public’s opinions on AI Art, enable the investigation of the origins of conflicts in online debates, and contribute to the sustainable development of AI Art. This paper presents a deep learning-driven framework for analyzing the characteristics of public opinion on the Weibo topic of AI Art. To classify the sentiments users expressed in Weibo posts, the linguistic feature-enhanced pre-training model (LERT) was employed to improve text representation via the fusion of syntactic features, followed by a bidirectional Simple Recurrent Unit (SRU) embedded with a soft attention module (BiSRU++) for capturing the long-range dependencies in text features, thus improving the sentiment classification performance. Furthermore, a text clustering analysis was performed across sentiments to capture the nuanced opinions expressed by Weibo users, hence providing useful insights about different online communities. The results indicate that the proposed sentiment analysis model outperforms common baseline models in terms of classification metrics and time efficiency, and the clustering analysis has provided valuable insights for in-depth analyses of AI Art.

ВЗАЄМОДІЯ АНАЛІЗУ ДИСКУРСУ ТА АНАЛІЗУ НАСТРОЇВ ДЛЯ ІДЕНТИФІКАЦІЇ ЕМОЦІЙНОГО СТАНУ В ТЕКСТОВОМУ СПІЛКУВАННІ

This paper is devoted to the study of two fundamental tasks of neuro-linguistic programming: discourse analysis and sentiment analysis, which are two fundamental tasks of natural language processing. The paper examined some models and algorithms for improving data processing in the process of textual communication between users. The study showed that the use of a joint model of discourse analysis and sentiment analysis is mutually beneficial. The results show that the information obtained from the discourse can help in determining the mood, and sentiment analysis and knowledge of two text fragments can help determine the discourse relations between them.

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

This article presents a comprehensive evaluation of traditional machine learning and deep learning models in analyzing sentiment trends within the SENT-COVID Twitter corpus, curated during the COVID-19 pandemic. The corpus, filtered by COVID-19 related keywords and manually annotated for polarity, is a pivotal resource for conducting sentiment analysis experiments. Our study investigates various approaches, including classic vector-based systems such as word2vec, doc2vec, and diverse phrase modeling techniques, alongside Spanish pre-trained BERT models. We assess the performance of readily available sentiment analysis libraries for Python users, including TextBlob, VADER, and Pysentimiento. Additionally, we implement and evaluate traditional classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, and simple neural networks like Multilayer Perceptron. Throughout the research, we explore different dimensionality reduction techniques. This methodology enables a precise comparison among classification methods, with BETO-uncased achieving the highest accuracy of 0.73 on the test set. Our findings underscore the efficacy and applicability of traditional machine learning and deep learning models in analyzing sentiment trends within the context of low-resource Spanish language scenarios and emerging topics like COVID-19.