Identifying Correlation between Sentiment Analysis and Septic News Sentences Classification Tasks (2023)

Abstract: This research investigates the correlation between Sentiment and SEPSIS(SpEculation, oPinion, biaS, and twISt) characteristics in news sentences through an ablation study. Various Sentiment analysis models, including TextBlob, Vader, and RoBERTa, are examined to discern their impact on news sentences. Additionally, we explore the Logistic Regression(LR), Decision Trees(DT), Support Vector Machines(SVM) and Convolutional Neural Network (CNN) models for Septic sentence classification.

## introduction in an era dominated by information and rapid communication, news serves as an invaluable source of knowledge, shaping public opinion and influencing decision-making. the term 'septic' encapsulates four distinctive linguistic characteristics i.e., 'sepsis': speculation, opinion, bias, and twist @xcite . when sentences exhibit one or more of these sepsis properties, they are labelled as septic sentences. we can interpret a sentence as pure if it is not having any of these properties. purity is irrelevant to legal and correctness qualities, in contrast to dictionary meaning. purity, therefore, denotes goodness in general. we believe that the news articles in the e-papers we have chosen accurately present a certain truth. however, a newspaper article may contain one or more septic sentences. although the context is intimately connected to purity, each sentence's authenticity will be determined separately in this work. we regard a sentence as pure if it cannot be resolved without context. this research explores the fusion of machine learning methods namely logistic regression(lr), decision trees(dt), and support vector machines(svm) and deep learning techniques, specifically convolutional neural networks (cnn), with advanced sentiment analysis pretrained models to improve septic-pure sentence classification. we present a dataset of 6000 news sentences annotated with septic and pure labels, derived from reputable e-newspapers, such as the hindu and the telegraph. three annotators evaluated the dataset, indicating substantial inter-annotator agreement. our study focuses on how sentiment analysis tools, including textblob, the vader sentiment analyzer, and the roberta model, can be used to enhance septic-pure sentence classification. we demonstrate that roberta, a state-of-the-art transformer model, surpasses other sentiment analysis methods and is particularly well-suited to this task. our analysis also examines the impact of opinionated and non-opinionated sentences on septic and pure classification. we transform sentiment labels into two categories: 'opinionated(negative and positive sentiment)' and 'non-opinionated(neutral sentiment),'. it analyses the relationship between the sentiment expressed in sentences and their classification. ## related work numerous forms of misinformation and deceptive content are prevalent on the internet. these encompass fabricated news and deduced information that contributes to the identification of false reports @xcite @xcite , as well as the recognition of rumors @xcite , and the propagation of false information @xcite . in their study, @xcite conducted research into automatically recognizing and labeling phrases within distorted news articles. they utilized machine learning techniques and rule-based post-processing to establish the concept of legitimacy in news sentences. these sentences were categorized into two groups: pure and septic. additionally, @xcite introduced guidelines for annotating non-legitimacy in bengali, addressing both shallow and deep levels, while considering the semantic attributes of bengali words. according to @xcite , opinion refers to an assessment, an evaluation, or an individual perspective. @xcite describe an opinion mining system as one capable of analyzing search results for an item. @xcite define opinion as a quadruple (topic, holder, claim, sentiment). ## our task in the landscape of natural language processing and text analysis, the task at hand is an intersection of sentiment analysis and septic sentence classification within news text data. we examine how the sentiment of a sentence influences its classification as septic or pure, and conversely, how the presence of septic properties might affect sentiment labelling. to analysis this we have done the experiments with and without adding these extra feature for each cases. this ablation study proves that there is an influence of sentiment in the septic sentence, however the opposite is not correct. the importance of the septic sentence in the analysis of sentiment is not important. ## correlation between septic sentence and sentiment analysis understanding the correlation between septic sentences and sentiment analysis is crucial. we examine how the sentiment of a sentence (positive, negative and neutral) influences its classification as septic or pure, and conversely, how the presence of septic properties might affect sentiment labelling. this section describes the relationships and interactions between sentiment analysis and the identification of septic and pure sentences. to simplify the results and make them more interpretable, we categorized negative and positive sentiments as "opinionated" and neutral sentiments as "non-opinionated." the distribution of opinionated (both positive and negative sentiment) and non-opinionated(neutral sentiment) sentences is visualized in figure 2 . this figure illustrates the significant impact of opinionated sentences on septic sentences and the prevalence of non-opinionated sentences in pure sentences. opinionated sentences tend to exhibit a higher likelihood of being associated with septic content, while non-opinionated sentences are more likely to be indicative of pure content. this alignment is considered favourable. the correlation analysis reveals a notable influence of 'septic-pure' on sentiment classification. sentences containing sepsis characteristics exhibit distinct sentiment patterns, emphasizing the significance of integrating sepsis-aware sentiment analysis for more precise classification and information retrieval in news articles. we extended our analysis by incorporating the sentiment information as an additional feature in a convolutional neural network (cnn) model. we conducted experiments both with and without the sentiment feature to evaluate its impact on classification performance. the results, presented in these results underscore the valuable role of sentiment analysis as an additional feature for improving the accuracy of the cnn model in septic-pure sentence classification. now, we describe the impact of incorporating the 'septic-pure' feature into the sentiment analysis task. we utilize the roberta pretrained sentiment analysis model's output on our dataset as the sentiment labels, followed by a manual correction process. we evaluate two different configurations for sentiment labels, one using opinionated and non-opinionated sentences and the other using neutral, positive, and negative labels. we examine the impact of incorporating septic-pure classification as an additional feature for both cases in cnn model. we observe a 2% increase in accuracy when incorporating 'septic-pure' as a feature. the results are summarized in the the reason behind this adverse effect of the septic-pure feature in the ternary sentiment clas- ## limitations of the work while our research contributes to improving the classification of septic and pure sentences, there are certain constraints and challenges that warrant consideration. our experiments shed light on intriguing aspects of the relationship between septic-pure features and sentiment analysis. however, these findings come with uncertainties that impact the overall scope of our work. in the opinionated and non-opinionated sentiment classification task, the inclusion of the septic-pure feature leads to a increase(7%) in accuracy. conversely, applying the septic-pure feature results on opinionated and non-opinionated sentiment classification task leads to a decrease(4%) in accuracy. unfortunately, the specific reasons behind these variations remain unclear. the complexity arises from the intricate nature of news sentences, where sentiment and septic properties may interact in unpredictable ways. the observed increase and decrease in accuracy highlight the need for a more comprehensive understanding of the relationship between sentiment and septic-pure features. further investigation into the subtleties of these interactions is necessary to unravel the underlying factors contributing to the observed performance variations. ## conclusion and future scope in this paper, we conducted an extensive analysis of septic-pure sentence classification within the context of news articles, employing a diverse set of sentiment analysis models and a convolutional neural network (cnn) architecture. we introduced sentiment information as an additional feature in the cnn model and observed a marked improvement in its classification performance, highlighting the value of sentiment analysis in this task. in the future, we plan to further refine our approach by fine-tuning the model with our news sentence sentiment-annotated dataset, aiming to enhance its performance on septic-pure sentence classification.