Understanding Public Opinion through Sentiment Analysis: A Case Study Using the Sentiment140 Dataset

An analysis of machine learning models for sentiment analysis of Tamil code-mixed data

Sentiment analysis of product reviews plays a crucial role in understanding consumer feedback, improving customer experience and making informed business decisions. This paper explores the application of machine learning and deep learning algorithms to effectively classify and analyse the sentiment of product reviews. Traditional machine learning techniques, such as Naïve Bayes, Support Vector Machines (SVM) and Random Forests are employed for sentiment classification based on manually engineered features. Simultaneously, deep learning approaches like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are leveraged to automatically learn complex representations from raw text data. The study compares the performance of these methods in terms of accuracy, precision, recall and F1-score. Additionally, pre-trained language models such as BERT are incorporated to enhance contextual understanding. Experimental results demonstrate that deep learning models particularly LSTM and BERT, outperform traditional machine learning techniques in capturing sentiments. This analysis provides valuable insights into the effectiveness of different algorithms in sentiment analysis tasks, paving the way for more advanced applications in natural language processing and customer sentiment evaluation.

Sentiment analysis of news headlines is an important factor that investors consider when making investing decisions. We claim that the sentiment analysis of financial news headlines impacts stock market values. Hence financial news headline data are collected along with the stock market investment data for a period of time. Using Valence Aware Dictionary and Sentiment Reasoning (VADER) for sentiment analysis, the correlation between the stock market values and sentiments in news headlines is established. In our experiments, the data on stock market prices are collected from Yahoo Finance and Kaggle. Financial news headlines are collected from the Wall Street Journal, Washington Post, and Business-Standard website. To cope with such a massive volume of data and extract useful information, various embedding methods, such as Bag-of-words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), are employed. These are then fed into machine learning models such as Naive Bayes and XGBoost as well as deep learning models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Various natural language processing, andmachine and deep learning algorithms are considered in our study to achieve the desired outcomes and to attain superior accuracy than the current state-of-the-art. Our experimental study has shown that CNN (80.86%) and LSTM (84%) are the best performing models in relation to machine learning models, such as Support Vector Machine (SVM) (50.3%), Random Forest (67.93%), and Naive Bayes (59.79%). Moreover, two novel methods, BERT and RoBERTa, were applied with the expectation of better performance than all the other models, and they did exceptionally well by achieving an accuracy of 90% and 88%, respectively.

Sentiment Analysis of Self Driving Car Dataset: A comparative study of Deep Learning approaches

In recent years, ride-hailing platforms such as Uber have significantly transformed the transportation industry by offering convenient, flexible, and technology-driven services. These platforms have made travel more accessible by enabling users to book rides instantly through mobile applications. However, customer satisfaction is not always consistent, as users frequently report issues related to pricing, driver behavior, service reliability, and application performance. Understanding these concerns is essential for improving service quality and enhancing user experience.This study focuses on analyzing customer reviews collected from Uber using sentiment analysis techniques. Since customer feedback is typically unstructured and large in volume,manual analysis is inefficient. Therefore, Natural Language Processing (NLP) techniques are used to process and interpret the textual data. The research applies both machine learning and deep learning models, including Logistic Regression, Naïve Bayes, Random Forest, and Long Short-Term Memory (LSTM), to classify reviews into positive, negative, and neutral sentiments. The dataset used in this study consists of approximately 12,000 customer reviews, which provide a diverse representation of user experiences. Data preprocessing techniques such as cleaning, tokenization, stopword removal, and stemming are applied to improve data quality. The experimental results demonstrate that deep learning models, particularly LSTM,outperform traditional machine learning approaches in terms of accuracy and contextual understanding. The proposed model achieved an overall accuracy of 90%, indicating its effectiveness in correctly classifying customer sentiments. The analysis also reveals key factors influencing customer satisfaction, including frequent ride cancellations, surge pricing, driver-related issues, and technical problems within the application. Overall, this research highlights the importance of sentiment analysis as a powerful tool for understanding customer opinions in ride-hailing platforms. The insights obtained from this study can help companies make data-driven decisions, improve service quality, and enhance customer satisfaction.

Small and Medium-sized Enterprises (SMEs) in Sri Lanka are increasingly turning to social media to gain insights into consumer preferences; however, they frequently lack sophisticated analytical tools to derive actionable insights. This research introduces a hybrid deep learning framework aimed at sentiment and emotion analysis specifically for the Sri Lankan cake sector. Customer reviews and social media comments were gathered and processed through various methods including text cleaning, emoji translation, stopword elimination, tokenization, and sequence padding. Several deep learning models— Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Units (GRU) were assessed, along with hybrid models that combined CNN and BiLSTM with GPT-2 embeddings. The CNN+GPT-2 model achieved the highest accuracy in sentiment classification at 96.14%, while BiLSTM performed best in emotion classification with an accuracy of 91.57%. In terms of trend forecasting, Long Short-Term Memory (LSTM) outshone both SARIMA and Prophet, yielding the lowest error metrics and the most reliable sentiment predictions. These results underscore the potential of hybrid deep learning and transformer-based models to effectively capture complex consumer sentiments and provide SMEs with practical, AI-driven insights. This study is among the first to implement such hybrid NLP models within a localized Sri Lankan context, presenting a scalable approach for business intelligence and informed decision-making.

The classification of genomic sequences is a crucial area of research in the field of virology. This is due to the increasing number of outbreaks we have faced in recent times. We have a vast repository of genomic sequences from various species, including humans, animals, plants, bacteria, and viruses, which tend to mutate and form new variants or strains. In the realm of machine learning, several models are employed for genome sequence classification. Among these are traditional algorithms such as Random Forest (RF), K-nearest neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB), each offering unique advantages in handling genetic data. Additionally, deep learning models like Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Bi-Directional LSTM networks are utilized for their robust capabilities in capturing complex patterns and dependencies within genomic sequences. In this study, we explored the application of Natural Language Processing (NLP) techniques to classify the genomic sequences. The focus of our research involves utilizing advanced large language models (LLMs) such as DNABERT, DNAGPT, and GENA LM, which are fine-tuned explicitly on the language of DNA. In this research, after a detailed analysis, we found that DNAGPT achieved an accuracy of 96%, which exceeds the performance of state-of-the-art machine learning and deep learning models.

The purpose of the study was to train and compare machine learning (ML) and deep learning (DL) models for long-term forecasting of minimum daily temperatures (frosts) - a key agrometeorological risk affecting crop productivity. The study was based on data from weather stations in St. Petersburg and the Leningrad region. To predict the year ahead, which aligns with the needs of operational agricultural production planning (sowing dates, harvest timing, protective measures), 8 methods were used: ForecasterAutoreg, Random Forest, Support Vector Regression (SVR), XGBoost, Convolutional Neural Network (CNN), SimpleRNN, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). The analysis was carried out on two datasets: for the periods 1936-2024 and 1881-1995. The quality of the models was assessed using the metrics MAE, MSE, RMSE, R2 and adjusted R2. The LSTM model showed the most accurate results on the main dataset (1936-2024): MAE 2.9, MSE 14.661, R2 0.789. The SVR method (MAE 3.461, R2 0.775) demonstrated the best metrics based on archived data (1881-1995). It has been established that DL models (LSTM, GRU, CNN) generally outperform classical ML methods based on modern data. The LSTM method is recognized as the most effective for integration into precision farming systems and agricultural monitoring in the region for the advance planning of agrotechnological measures to protect crops from frost, optimization of crop rotation systems, and minimization of crop loss risks.

Parts of Speech (pos) tagging corresponds to techniques of tagging accurate pos tags of each word of a sentence. It is a pivotal and indispensable part of sentiment analysis, synthesizing text to speech, analyzing textual data and many more Natural Language Processing (NLP) tasks. Though there are many researches done in the field of languages like English, Chinese etc., it is still comparatively unexplored for Bangla. An excellent pos tagger in Bangla language can accelerate NLP of Bangla textual data. In this study, we have developed a novel hybrid Deep Learning (DL) model with two parallel input networks with Convolutional Neural Network (CNN), and Bidirectional Long Short Term Memory (BiLSTM), and an output network of CNN. CNN works well regarding the local dependencies of words in sentences, and BiLSTM works well with the global dependencies of words regarding the context in a sentence. No other work has been done with local features of textual data along with long term word dependencies in Bangla pos tagging studies. The model can work with any external Word Embeddings. In this study, it is implemented with both Embedding Layer and Word2Vec layer. In the evaluation process, Mathews Correlation Coefficient (MCC), Precision-Recall curve are used along with accuracy and F1-score due to the dataset's imbalanced characteristic. Our proposed model accompanied by Word2Vec layer has outperformed Vanilla Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), BiLSTM networks, and also one recent study using the same dataset with the highest accuracy of 0.974, F1-score of 0.883, and MCC value of 0.936.

Customer sentiment analytics has emerged as a critical tool for retail businesses aiming to understand consumer perceptions, improve customer experience, and enhance decision-making. This paper explores the application of Natural Language Processing (NLP) and deep learning techniques in analyzing unstructured textual data such as customer reviews, social media posts, and feedback to extract actionable insights for the retail industry. Traditional sentiment analysis methods often rely on keyword-based approaches, which lack the contextual understanding necessary to interpret nuanced emotions and implicit meanings. In contrast, NLP combined with advanced deep learning models, such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Transformers (e.g., BERT), enables more accurate sentiment classification by capturing semantic relationships, context, and sentiment polarity. The study presents a comprehensive framework that leverages data preprocessing, tokenization, embedding layers (e.g., Word2Vec, GloVe), and model training pipelines to analyze large volumes of customer-generated content. Experimental results from real-world retail datasets demonstrate the superiority of deep learning models over traditional machine learning techniques in sentiment prediction accuracy, especially in multilingual and noisy data scenarios. Moreover, the paper discusses the integration of sentiment scores with customer segmentation and product performance metrics to drive personalized marketing, inventory adjustments, and service enhancements. Challenges such as data labeling, domain adaptation, and interpretability of deep models are also addressed, along with strategies to overcome them using transfer learning, attention mechanisms, and explainable AI tools. The research concludes that customer sentiment analytics using NLP and deep learning is not merely a technological advancement but a strategic enabler for data-driven retail transformation. By systematically capturing and interpreting customer voices, retail businesses can proactively respond to market demands, enhance loyalty, and gain a competitive edge in an increasingly digital consumer landscape.

Renewable energy (RE) plays a critical role in mitigating climate change and ensuring energy security globally. As the adoption of RE technologies continues to grow, accurate forecasting of renewable energy consumption has become increasingly important for grid planning and integration. This paper provides a comprehensive review of the key factors influencing renewable energy consumption and the application of machine learning and deep learning models for forecasting RE consumption. The study examines various socioeconomic, demographic, and environmental variables that have been found to impact the demand and usage of renewable energy sources such as solar, wind, and hydropower. Factors like GDP, population, energy prices, government policies, and weather conditions are discussed in detail for their influence on RE consumption patterns. The paper also analyzes how technological advancements, consumer behavior, and energy efficiency measures can shape renewable energy demand. Furthermore, the paper evaluates the performance of various machine learning techniques, which have been done in various research from 2020 to 2024, including artificial neural networks, support vector machines, random forests, gradient boosting, and deep learning models such as long short-term memory (LSTMs) and convolutional neural networks (CNNs), in forecasting renewable energy consumption. The study analyzes the strengths, limitations, and accuracy of these models based on case studies across residential, commercial, and industrial sectors. The findings indicate that deep learning models, particularly LSTMs and CNNs, have outperformed traditional machine learning techniques in renewable energy consumption forecasting. These deep learning models are able to capture the complex nonlinear relationships and temporal dependencies inherent in RE consumption data, leading to higher accuracy and better generalization performance.

Sequence Classification is one of the on-demand research projects in the field of Natural Language Processing (NLP). Classifying a set of images or text into an appropriate category or class is a complex task that a lot of Machine Learning (ML) models fail to accomplish accurately and end up under-fitting the given dataset. Some of the ML algorithms used in text classification are KNN, Naïve Bayes, Support Vector Machines, Convolutional Neural Networks (CNNs), Recursive CNNs, Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), etc. For this experimental study, LSTM and a few other algorithms were chosen for a more comparative study. The dataset used is the SMS Spam Collection Dataset from Kaggle and 150 more entries were additionally added from different sources. Two possible class labels for the data points are spam and ham. Each entry consists of the class label, a few sentences of text followed by a few useless features that are eliminated. After converting the text to the required format, the models are run and then evaluated using various metrics. In experimental studies, the LSTM gives much better classification accuracy than the other machine learning models. F1-Scores in the high nineties were achieved using LSTM for classifying the text. The other models showed very low F1-Scores and Cosine Similarities indicating that they had underperformed on the dataset. Another interesting observation is that the LSTM had reduced the number of false positives and false negatives than any other model.

Abstract. To Electronic health records (EHRs) are a treasure trove of patient information, including clinical notes. However, extracting meaningful insights from these unstructured narratives remains a challenge. This paper explores the potential of natural language processing (NLP) and deep learning to unlock the rich clinical narrative data and revolutionize healthcare. Clinical notes capture a wealth of information beyond structured data points. They document patient history, symptoms, treatment progress, and physician observations. Analyzing this narrative unlocks a deeper understanding of a patient's health journey, aiding in NLP can identify subtle patterns in language usage that might be indicative of specific diseases. This can assist clinicians in arriving at more accurate diagnoses, particularly for complex cases. Deep learning models can analyze narratives to identify patients at higher risk of developing certain conditions. This allows for proactive interventions and preventative measures. Extracting details about a patient's lifestyle, social context, and emotional well-being from narratives can facilitate the development of personalized treatment plans that cater to individual needs. NLP techniques like named entity recognition, sentiment analysis, and topic modeling enable the extraction of key clinical entities, emotions, and themes from narratives. Deep learning, with its ability to learn complex relationships in text data, empowers NLP tasks. Deep learning models can be trained on massive datasets of clinical notes, achieving superior performance in tasks like information extraction compared to traditional NLP methods. Deep learning models can handle the vast amount of text data generated in healthcare settings, enabling large- scale analysis of clinical narratives. By learning from diverse patient populations, deep learning models can improve their generalizability and adapt to variations in language usage within clinical documentation. Integrating NLP and deep learning into clinical workflows promises a future where the patient narrative is not just documented but actively analyzed to inform better healthcare decisions. This can lead to earlier diagnoses, more effective treatments, and ultimately, improved patient outcomes. By addressing the challenges and fostering responsible development, we can unlock the true potential of clinical narratives and empower a new era of data-driven healthcare.

Sentiment analysis has become an important part of managing brands and getting people to interact with them. It helps businesses figure out what the public thinks, make better experiences for customers, and make smart marketing choices. In this study, different machine learning (ML) models are tested to see how well they work for analyzing brand-related text data from customer polls, product reviews, and social media sites. Many different types of ML methods are compared, including support vector machines (SVM), Random Forest, and Logistic Regression. Also compared are deep learning (DL) structures like long short-term memory networks (LSTM), convolutional neural networks (CNN), and Transformer-based models. To make sure the quality and usefulness of the raw features, the data goes through a lot of strict preparation steps, such as cleaning, normalization, tokenization, stop word removal, and lemmatization. Our method focuses on using the best model training techniques and fine-tuning hyperparameters to get the best performance measures, like F1-score, accuracy, precision, and recall. The test results show that DL models, especially Transformer-based structures, are better than traditional methods at finding complex mood patterns and how they rely on the context of brand-related texts. We also look at the trade-offs between computer complexity and prediction power, giving us ideas that can be used in real-life brand management situations. You can use this thorough review as a help to choose the right mood analysis methods for your business needs. The results help to improve brand image management through data-driven insights, make proactive customer interaction easier, and improve automatic mood recognition systems.

Sentiment analysis (SA) detects people’s opinions from text engaging natural language processing (NLP) techniques. Recent research has shown that deep learning models, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer-based provide promising results for recognizing sentiment. Nonetheless, CNN has the advantage of extracting high-level features by using convolutional and max-pooling layers; it cannot efficiently learn a sequence of correlations. At the same time, Bidirectional RNN uses two RNN directions to improve extracting long-term dependencies. However, it cannot extract local features in parallel, and Transformer-based like Bidirectional Encoder Representations from Transformers (BERT) are the computational resources needed to fine-tune, facing an overfitting problem on small datasets. This paper proposes a novel attention-based model that utilizes CNNs with LSTM (named ACL-SA). First, it applies a preprocessor to enhance the data quality and employ term frequency-inverse document frequency (TF-IDF) feature weighting and pre-trained Glove word embedding approaches to extract meaningful information from textual data. In addition, it utilizes CNN’s max-pooling to extract contextual features and reduce feature dimensionality. Moreover, it uses an integrated bidirectional LSTM to capture long-term dependencies. Furthermore, it applies the attention mechanism at the CNN’s output layer to emphasize each word’s attention level. To avoid overfitting, the Guasiannoise and GuasianDroupout are adopted as regularization. The model’s robustness is evaluated on four English standard datasets, i.e., Sentiment140, US-airline, Sentiment140-MV, SA4A with various performance matrices, and compared efficiency with existing baseline models and approaches. The experiment results show that the proposed method significantly outperforms the state-of-the-art models.