Stop Word Removal Research Articles

IMPLEMENTATION OF TEXT PROCESSING FOR SENTIMENT ANALYSIS OF TAX PAYMENT INTEREST AFTER THE "RUBICON" PHENOMENON

In February 2023, an incident occurred involving the child of an official from the Indonesian Directorate General of Taxes who committed violence against a member of the GP Ansor organization. The news spread widely and brought a new issue, namely suspicious reporting of the official's wealth with an amount of up to 56 billion Indonesian Rupiahs. In order to determine public sentiment towards the "RUBICON" case, which was receiving attention, sentiment analysis of tax payment interest was conducted using text mining techniques. Data processing was done using the R language and RStudio application, taking a dataset of 23,785 tweets from the public about paying taxes on Twitter. Next, text cleaning was done to remove numbers, symbols, and URLs, as well as text processing using stemming, tokenizing, stopword removal, and TF-IDF methods. The TF-IDF method shows that the words "rafael" and "case" are the top keywords. This study used a supervised model by comparing SVM, KNN, and Naive Bayes algorithms, and evaluation was done using a confusion matrix with accuracy results in descending order of 0.8922, 0.8049, and 0.7369. The conclusion of this study is that the SVM algorithm successfully classified sentiment with the highest level of accuracy and obtained the highest negative sentiment of 5,616 sentences.

ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

The high pace rising global competitions across education sector has forced institutions to enhance aforesaid aspects, which require assessing students or related stakeholders’ perception and opinion towards the learning materials, courses, learning methods or pedagogies, etc. To achieve it, the use of reviews by students can of paramount significance; yet, annotating student’s opinion over huge heterogenous and unstructured data remains a tedious task. Though, the artificial intelligence (AI) and natural language processing (NLP) techniques can play decisive role; yet the conventional unsupervised lexicon, corpus-based solutions, and machine learning and/or deep driven approaches are found limited due to the different issues like class-imbalance, lack of contextual details, lack of long-term dependency, convergence, local minima etc. The aforesaid challenges can be severe over large inputs in Big Data ecosystems. In this reference, this paper proposed an outlier resilient semantic featuring deep driven sentiment analysis model (ORDSAENet) for educational domain sentiment annotations. To address data heterogeneity and unstructured-ness over unpredictable digital media, the ORDSAENet applies varied pre-processing methods including missing value removal, Unicode normalization, Emoji and Website link removal, removal of the words with numeric values, punctuations removal, lower case conversion, stop-word removal, lemmatization, and tokenization. Moreover, it applies a text size-constrained criteria to remove outlier texts from the input and hence improve ROI-specific learning for accurate annotation. The tokenized data was processed for Word2Vec assisted continuous bag-of-words (CBOW) semantic embedding followed by synthetic minority over-sampling with edited nearest neighbor (SMOTE-ENN) resampling. The resampled embedding matrix was then processed for Bi-LSTM feature extraction and learning that retains both local as well as contextual features to achieve efficient learning and classification. Executing ORDSAENet model over educational review dataset encompassing both qualitative reviews as well as quantitative ratings for the online courses, revealed that the proposed approach achieves average sentiment annotation accuracy, precision, recall, and F-Measure of 95.87%, 95.26%, 95.06% and 95.15%, respectively, which is higher than the LSTM driven standalone feature learning solutions and other state-of-arts. The overall simulation results and allied inferences confirm robustness of the ORDSAENet model towards real-time educational sentiment annotation solution.

Emotion detection on social media status in Myanmar language

<span lang="EN-US">Many social media emerged and provided services during these years. Most people, especially in Myanmar, use them to express their emotions or moods, learn subjects, sell products, read up-to-date news, and communicate with each other. Emotion detection on social users makes critical tasks in the opinion mining and sentiment analysis. This paper presents the emotion detection system on social media (Facebook) user status or post written in Myanmar (Burmese) language. Before the emotion detection process, the user posts are pre-processed under segmentation, stemming, part-of-speech (POS) tagging, and stop word removal. The system then uses our preconstructed Myanmar word-emotion Lexicon, M-Lexicon, to extract the emotion words from the segmented POS post. The system provides six types of emotion such as surprise, disgust, fear, anger, sadness, and happiness. The system applies naïve Bayes (NB) emotion classifier to examine the emotion in the case of more than two words with different emotion values are extracted. The classifiers also classify the emotion of the users on their posts. The experiment shows that the system can detect 85% accuracy in NB based emotion detection while 86% in recurrent neural network (RNN).</span>

ANALISIS SENTIMEN MASYARAKAT TERHADAP KEBIJAKAN PENERAPAN PPKM DI MEDIA SOSIAL TWITTER DENGAN MENGGUNAKAN METODE XGBOOST

Corona Virus Disease (Covid-19) is a virus that causes respiratory infections in humans. Indonesia is a country that has been infected with this virus, the implementation of restrictions on community activities (PPKM) is implemented by the government as a policy to reduce the spread of Covid-19. Pros and cons arise due to the impact of the policy. Therefore, assessing how public opinion or sentiment is towards this policy is important to do. This study aims to implement the XGBoost algorithm in the sentiment classification process. Sentiment analysis targets public opinion on Twitter, the dataset used is 1958 positive tweets and 3980 negative tweets. At the preprocessing stage, case-folding, stopwords removal, tokenizing, and stemming are carried out. Giving weights to terms uses the Term Frequency-Relevance Frequency method to turn each term into a number. In the final stage, classification is carried out by implementing the XGBoost method with optimal hyperparameter scores. K-fold cross validation is used to evaluate model performance. Based on the evaluation results, the best performance was obtained by a model with a hyperparameter value with an n_estimator of 1000, a learning_rate of 0.1, a max_depth of 6, a subsample of 1, a gamma of 0 and utilizing the stem-ming process in preprocessing with an accuracy value of 85.27%. precision of 86.07%, and recall of 85.23%.

Analisis Emosi pada Media Sosial Twitter Menggunakan Metode Multinomial Naive Bayes dan Synthetic Minority Oversampling Technique

Twitter social media is often used to express one's emotions through tweets. Much research has been conducted on emotional analysis in the social media Twitter. Machine learning is a tool that is widely used to categorize emotions. However, an imbalance in the amount of data between classes is often a problem. So, this research aims to determine the performance of the combined Multinomial Naïve Bayes (MNB) and Synthetic Minority Oversampling Technique (SMOTE) methods for emotional analysis of tweets from the social media Twitter. Each tweet through data preprocessing in this research includes case folding, data cleaning, convert slangword, convert negation, tokenization, stopword removal, and stemming. For feature extraction the n-gram method is used and for feature weighting the term frequency method is used. Testing was carried out using K-Fold Cross Validation. Based on the test results, using SMOTE an average accuracy of 0.65 or 65% was obtained and an average f1-score value of 0.66 or 66%. Meanwhile, without SMOTE, an average accuracy of 0.64 or 64% was obtained and an average f1-score of 0.65 or 65%. Although in this study it can be shown that the results using SMOTE are 1% better in categorizing emotions. However, the results obtained are not optimal, and other methods of data balancing and machine learning still need to be studied.

Uji Kernel SVM dalam Analisis Sentimen Terhadap Layanan Telkomsel di Media Sosial Twitter

Telkomsel is an internet service provider in Indonesia which was launched in 1995. As an internet 
 service provider with the most users, Telkomsel has become the center of attention of internet users in Indonesia. This 
 invites user opinions and perspectives on Telkomsel, which is commonly referred to as sentiment. One of the media 
 commonly used to express an opinion and point of view is Twitter. Twitter is a social media platform that is often a 
 place for sharing and spreading the news, and discussing ideas, and opinions of Twitter users. In this study, the algorithm used 
 is the Support Vector Machine. In the Support Vector Machine, there is a kernel trick that will be used to determine 
 kernel performance and analyze sentiment. The sentiments analyzed amounted to 537 tweets collected by scraping. 
 The collected tweets will go through the preprocessing stage, namely cleaning, case folding, tokenizing, normalization, 
 stemming, stopword removal, and detokenizing. A sentiment is classified into 2 labels, namely positive and negative. 
 Based on the test results, the sigmoid kernel has the best performance with an accuracy value of 0.950, a precision of 
 0.945, a recall of 0.860, an f1-score of 0.896, and sentiment tend toward negative.

Marathi text summarization through NLP and deep learning mechanism

<p>Every day, an ever-increasing amount of people gain access to the internet platform. This has proven to be efficient in creating cost-effective internet platform deployments and applications. The growth in the amount of people using the platform has resulted in a rise in the quantity of information accessible on the internet in the form of news, media, and other forms of communication. This causes evaluating and comprehending a significant amount of textual information a very challenging task. For the objective of generating textual summaries for Marathi texts, an effective and trustworthy approach is required. Through the use of machine learning methods, a successful strategy for extracting summary for the Marathi text has been generated for this objective. To obtain the Marathi text summary, the proposed method uses feature extraction as well as deep belief networks and decision tree methodologies. The experimentation was carried out on the performance of the Term Frequency-Inverse Document Frequency (TF-IDF) in the stopword elimination procedure, along with the evaluation of the summarization outcome which achieves a Mean Absolute Error (MAE) of 2.8 for the stopword removal approach through TF-IDF technique and a precision of 95.49% with an accuracy of 92.76%.</p>

A scoping review of topic modelling on online data

With <span>the increasing prevalence of unstructured online data generated (e.g., social media, online forums), mining them is important since they provide a genuine viewpoint of the public. Due to this significant advantage, topic modelling has become more important than ever. Topic modelling is a natural language processing (NLP) technique that mainly reveals relevant topics hidden in text corpora. This paper aims to review recent research trends in topic modelling and state-of-the-art techniques used when dealing with online data. Preferred reporting items for systematic reviews and meta-analysis (PRISMA) methodology was used in this scoping review. This study was conducted on recent research works published from 2020 to 2022. We constructed 5 research questions for the interest of many researchers. 36 relevant papers revealed that more work on non-English languages is needed, common pre-processing techniques were applied to all datasets regardless of language e.g., stop word removal; latent dirichlet allocation (LDA) is the most used modelling technique and also one of the best performing; and the produced result is most evaluated using topic coherence. In conclusion, topic modelling has largely benefited from LDA, thus, it is interesting to see if this trend continues in the future across languages.</span>

HINDI FAKE NEWS DETECTOR

Fake news has proliferated on the internet in recent decades. More people than ever before are creating and sharing knowledge because of social networks, many of which have no connection to reality. This has led to the rapid dissemination of false information used for various political and business objectives. Finding reliable news sources has become more difficult due to online newspapers. In this work, we gathered news articles of Hindi text from various news sources. Techniques for pre-processing, feature extraction, classification, and prediction are all extensively covered. A “Fake news detection system” has been developed in this project. Various Hindi news articles have been collected from multiple sources to help diversify the dataset and train the model better. The project first pre-processes the dataset and uses the pre-trained Bert model for feature extraction. Then, the data is classified from the dataset and prediction processes are employed on the dataset. Various machine learning algorithms and deep learning models like Naïve Bayes, Long Short-Term Memory, Logistic Regression have been employed in previous works for the purpose of detecting fake news. Pre-processing steps include data cleaning, stop words removal, tokenizing, stemming. The testing and training of the dataset include using the BERT for sequence classification model. The model is trained and tested against the validation dataset

Integrating Text Classification into Topic Discovery Using Semantic Embedding Models

Topic discovery involves identifying the main ideas within large volumes of textual data. It indicates recurring topics in documents, providing an overview of the text. Current topic discovery models receive the text, with or without pre-processing, including stop word removal, text cleaning, and normalization (lowercase conversion). A topic discovery process that receives general domain text with or without processing generates general topics. General topics do not offer detailed overviews of the input text, and manual text categorization is tedious and time-consuming. Extracting topics from text with an automatic classification task is necessary to generate specific topics enriched with top words that maintain semantic relationships among them. Therefore, this paper presents an approach that integrates text classification for topic discovery from large amounts of English textual data, such as 20-Newsgroups and Reuters Corpora. We rely on integrating automatic text classification before the topic discovery process to obtain specific topics for each class with relevant semantic relationships between top words. Text classification performs a word analysis that makes up a document to decide what class or category to identify; then, the proposed integration provides latent and specific topics depicted by top words with high coherence from each obtained class. Text classification accomplishes this with a convolutional neural network (CNN), incorporating an embedding model based on semantic relationships. Topic discovery over categorized text is realized with latent Dirichlet analysis (LDA), probabilistic latent semantic analysis (PLSA), and latent semantic analysis (LSA) algorithms. An evaluation process for topic discovery over categorized text was performed based on the normalized topic coherence metric. The 20-Newsgroups corpus was classified, and twenty topics with the ten top words were identified for each class. The normalized topic coherence obtained was 0.1723 with LDA, 0.1622 with LSA, and 0.1716 with PLSA. The Reuters Corpus was also classified, and twenty and fifty topics were identified. A normalized topic coherence of 0.1441 was achieved when applying the LDA algorithm, obtaining 20 topics for each class; with LSA, the coherence was 0.1360, and with PLSA, it was 0.1436.

Detection of offensive terms in resource-poor language using machine learning algorithms.

The use of offensive terms in user-generated content on different social media platforms is one of the major concerns for these platforms. The offensive terms have a negative impact on individuals, which may lead towards the degradation of societal and civilized manners. The immense amount of content generated at a higher speed makes it humanly impossible to categorise and detect offensive terms. Besides, it is an open challenge for natural language processing (NLP) to detect such terminologies automatically. Substantial efforts are made for high-resource languages such as English. However, it becomes more challenging when dealing with resource-poor languages such as Urdu. Because of the lack of standard datasets and pre-processing tools for automatic offensive terms detection. This paper introduces a combinatorial pre-processing approach in developing a classification model for cross-platform (Twitter and YouTube) use. The approach uses datasets from two different platforms (Twitter and YouTube) the training and testing the model, which is trained to apply decision tree, random forest and naive Bayes algorithms. The proposed combinatorial pre-processing approach is applied to check how machine learning models behave with different combinations of standard pre-processing techniques for low-resource language in the cross-platform setting. The experimental results represent the effectiveness of the machine learning model over different subsets of traditional pre-processing approaches in building a classification model for automatic offensive terms detection for a low resource language, i.e., Urdu, in the cross-platform scenario. In the experiments, when dataset D1 is used for training and D2 is applied for testing, the pre-processing approach named Stopword removal produced better results with an accuracy of 83.27%. Whilst, in this case, when dataset D2 is used for training and D1 is applied for testing, stopword removal and punctuation removal were observed as a better preprocessing approach with an accuracy of 74.54%. The combinatorial approach proposed in this paper outperformed the benchmark for the considered datasets using classical as well as ensemble machine learning with an accuracy of 82.9% and 97.2% for dataset D1 and D2, respectively.

DZ-SMS: An Authentic Corpus of Algerian SMS

In this article, a complete methodology of a corpus realization of authentic Short Message Service (SMS) from Algerian dialect and which are transcribed in Latin characters or symbols is presented. A linguistic material constituted by 6,000 SMS coming from the different geographical regions of Algeria (Middle, East, and West) corresponding to 42 administrative and geographical departments, have been collected. The coexistence of several dialects through these three regions simultaneously has obliged us to consider and operate a classification of the data for each dialect. This data classification has yielded three extracted regional dialectic corpora, each of them covering a specific number of administrative departments. These treatments are based on the so-called Data-n-gram tokenization targeting the suppression of the stop words, the stemming and the imbalance of the classes linked to the nature of the SMS. Consequently, three text classifiers based on three linear classifiers, namely, Stochastic Gradient Descent (SGD), The Ridge Regression (RDG), and Linear Support Vector Machines, to find out the number of significant corpora to extract from the collected data. A deep analysis of the results has shown that the 5-grams data representation is more representative whereas the stop-words removal and stemming process has generated an information loss that has subsequently inferred an alteration of the recognition rate of about 2%. The emerging problem of classes imbalance has been treated by using three techniques: Random Oversampling, Synthetic Minorities Oversampling Technique (SMOTE), and Adaptive Synthetic (ADASYN). This treatment produced interesting results and enhancements; particularly, the classification by region with the oversampling process SMOTE by using the RDG technique has reached a better percentage of 55.93% whereas the classification by department with the oversampling process ADASYN associated with the SGD has only yielded a maximum score of about 17.11%. The results, which undoubtedly are in favor of the classification by region, have compelled us to create three Subdialectal regional corpora, each, covering a certain number of Algerian departments.

SENTIMENT ANALYSIS ON REVIEWS OF WOMEN'S TOPS ON SHOPEE MARKETPLACE USING NAIVE BAYES ALGORITHM

Reviews of women's tops in the market are valuable information if processed properly. Merchants can conduct product review analysis to obtain information that can be used to evaluate products and services. Product review analysis activities are not enough just to see the number of stars, it is necessary to see the entire content of the review comments to be able to know the intent of the review. Sentiment analysis system is a system used to automatically analyze online product reviews to obtain information including sentiment information that is part of online reviews. The data is classified using Naive Bayes. The data collected were 1,000 product reviews of women's tops as samples. The purpose of this study is to determine the sentiment analysis of female top product reviews using the Naive Bayes algorithm. The stages of this research include data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning emoticons & symbols, Case folding, Word Normalizer, Tokenize, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be classified into 3 (three) classes, namely negative, positive and neutral. The data will then be evaluated using accuracy parameter testing. The test results show an accuracy value of 89%, this result shows that the product reviews of women's tops are more positive.

Sentiment Analysis of YouTube Comments Toward Chat GPT

<p>Sentiment analysis is used for analyzing the emotions and attitudes expressed in text data. In this study, sentiment analysis is used to understand people’s enthusiasm toward Chat GPT. The primary objective of this study is to investigate the acceptance of people of new artificial intelligence technology, Chat GPT, that may change the future. To get a deep understanding of it, a large dataset of user comments from YouTube is collected and then data pre-processing is done by removing stop words, punctuations, and irrelevant information. Using Text Blob and VADER approaches, comments are classified into positive, neutral, and negative categories. The result shows that most users have a positive sentiment to receive and use Chat GPT. The contribution of this study is to provide insights into the sentiment of people’s response to Chat GPT, which can inform user acceptance of the language model development and give guide its future applications.</p>

Will the Relaxation of COVID-19 Control Measures Have an Impact on the Chinese Internet-Using Public? Social Media-Based Topic and Sentiment Analysis.

Objective: In December 2022, the Chinese government announced the further optimization of the implementation of the prevention and control measures of COVID-19. We aimed to assess internet-using public expression and sentiment toward COVID-19 in the relaxation of control measures in China. Methods: We used a user-simulation-like web crawler to collect raw data from Sina-Weibo and then processed the raw data, including the removal of punctuation, stop words, and text segmentation. After performing the above processes, we analyzed the data in two aspects. Firstly, we used the Latent Dirichlet Allocation (LDA) model to analyze the text data and extract the theme. After that, we used sentiment analysis to reveal the sentiment trend and the geographical spatial sentiment distribution. Results: A total of five topics were extracted according to the LDA model, namely, Complete liberalization, Resource supply, Symptom, Knowledge, and Emotional Outlet. Furthermore, sentiment analysis indicates that while the percentages of positive and negative microblogs fluctuate over time, the overall quantity of positive microblogs exceeds that of negative ones. Meanwhile, the geographical dispersion of public sentiment on internet usage exhibits significant regional variations and is subject to multifarious factors such as economic conditions and demographic characteristics. Conclusion: In the face of the relaxation of COVID-19 control measures, although concerns arise among people, they continue to encourage and support each other.

An automatic product recommendation system in e-commerce using Flamingo Search Optimizer and Fuzzy Temporal Multi Neural Classifier

<p>In this paper, a new automatic product recommendation system (APRS) is proposed to recommend the suitable products to the customer in e-commerce by analyzing the customers’ reviews. This recommendation system applies semantic aware data preprocessing, feature selection and extraction and classification. The initial level data preprocessing including blank space and stop word removal. Moreover, we use a Flamingo Search Optimizer (FSO) for optimizing the features that are extracted in the initial level data preprocessing. In addition, a new Fuzzy Temporal Multi Neural Classification Algorithm (FTMNCA) is proposed for performing effective classification that is helpful to make effective decision on prediction process. In addition, the proposed automatic product recommendation system recommends the suitable products to the customers according to the classification result. Finally, the proposed system is evaluated by conducting various experiments and proved as superior than the available systems in terms of prediction accuracy, precision, recall and f-measure.</p>

Sentiment analysis from Bangladeshi food delivery startup based on user reviews using machine learning and deep learning

Food delivery methods are at the top of the list in today's world. People's attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classification techniques are used: extreme gradient boosting (XGB), random forest classifier (RFC), decision tree classifier (DTC), and multi nominal Naive Bayes (MNB). Three deep learning (DL) models are used: convolutional neural network (CNN), long term short memory (LSTM), and recurrent neural network (RNN). The XGB model exceeds all four machine learning (ML) algorithms with an accuracy of 89.64%. LSTM has the highest accuracy rate of the three DL algorithms, with an accuracy of 91.07%. Among ML and DL models, LSTM DL takes the lead to predict the sentiment.

Sentiment analysis from Bangladeshi food delivery startup based on user reviews using machine learning and deep learning

Food delivery methods are at the top of the list in today's world. People's attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classification techniques are used: extreme gradient boosting (XGB), random forest classifier (RFC), decision tree classifier (DTC), and multi nominal Naive Bayes (MNB). Three deep learning (DL) models are used: convolutional neural network (CNN), long term short memory (LSTM), and recurrent neural network (RNN). The XGB model exceeds all four machine learning (ML) algorithms with an accuracy of 89.64%. LSTM has the highest accuracy rate of the three DL algorithms, with an accuracy of 91.07%. Among ML and DL models, LSTM DL takes the lead to predict the sentiment.

Analisis Sentimen Ulasan Aplikasi Info BMKG di Google Play Menggunakan TF-IDF dan Support Vector Machine

Posting online reviews has become one of the most popular ways to express opinions and sentiments towards service applications. The Meteorology, Climatology and Geophysics Agency (BMKG) Info application is an Android and iOS-based mobile application that provides information on weather, climate, air quality, and earthquakes that occur in various regions in Indonesia. The information contained in this application is very important but has a worse value than other forecasting applications. Sentiment analysis is the process of classifying text into several classes such as positive sentiment, negative or not containing both. This research aims to analyze user reviews on the BMKG Info application from the Google Play website. The benefits obtained are as consideration for developers to improve the shortcomings of the application. The classification process uses Term Frequency-Inverse Document Frequency (TF-IDF) and the Support Vector Machine (SVM) algorithm. This research successfully collected 2500 reviews from users of the BMKG Info application on the Google PlayStore website using the web scraping method. Text preprocessing of the reviews used case folding, symbolic and stopword removal, tokenization, normalization, and stemming. User ratings help in identifying the sentiment label of a review, 66% of reviews are positive while the rest are negative. The most frequently reviewed topics with sentiment value are "application", "information", "update". This research conducted three experimental scenarios based on the composition of training data and test data. Based on the prediction model, the scenario with 75%:25% split data has the highest accuracy rate of 79%.

Text Data Mining for Uncovering the Influence of Religion on Ancient Greek Philosophical Thought with Optimization

Text data mining can provide valuable insights into the influence of religion on the development of ancient Greek philosophical thought. This paper presented text data mining techniques to perform feature extraction and classification with Gaussian Optimization (FeCGO), to analyze the influence of religion on the development of ancient Greek philosophical thought. This paper explores the application of text data mining techniques, specifically feature extraction and classification with Gaussian Optimization (FeCGO), to analyze the influence of religion on the development of ancient Greek philosophical thought. The FeCGO examined the relevant texts, including works by ancient Greek philosophers, religious texts, myths, and historical accounts. These texts are subjected to preprocessing steps, such as tokenization, stop word removal, stemming, and normalization, to ensure the data is prepared for analysis. The proposed FeCGO method combines the Gaussian Optimization algorithm with a classification model to optimize the classification accuracy and performance. Labeled data is used to train the FeCGO model, with texts categorized based on their religious or philosophical themes. The findings contribute to a deeper understanding of the interplay between religion and philosophy in ancient Greek society. The application of text data mining techniques, specifically FeCGO, demonstrates the potential of computational methods to extract valuable insights from large-scale textual datasets.