In this project I analyzes a dataset of survey papers to uncover trends and insights into re- search developments across various fields. By exploring attributes such as taxonomy, release date, and summaries, we visualize paper trends over time. Additionally, we classify the papers using machine learning techniques, address- ing class imbalance through data augmentation and stratified sampling. Implementing stratified cross-validation ensures robust model evalua- tion. Ultimately, this work aims to enhance understanding of survey literature and provide valuable insights for researchers and practition- ers in their respective domains. 1 Introduction In recent years, the rapid growth of academic lit- erature has made it increasingly difficult for re- searchers to stay updated on emerging trends and developments across various fields. Survey pa- pers, which synthesize existing research, play a cru- cial role in providing comprehensive overviews of these advancements. This project aims to analyze a dataset of survey papers, focusing on attributes such as taxonomy, publication date, and summaries. Through data exploration and machine learning- based classification, we identify trends in survey publications and categorize papers across different research domains. Addressing challenges like class imbalance, this study provides valuable insights for researchers, enhancing their understanding of key areas in the literature. 2 Related Work In recent years, there has been a surge of interest in analyzing academic literature to uncover trends and patterns in research. Various studies have employed natural language processing (NLP) techniques to extract meaningful insights from large volumes of scientific texts. For instance, researchers have utilized citation analysis to measure the impact ofsurvey papers and their contributions to different fields. Additionally, studies have explored the applica- tion of machine learning for automatic classifica- tion of research articles. Techniques such as su- pervised learning algorithms have been employed to categorize papers based on their content, tax- onomy, and other features. Addressing class im- balance has also gained attention, with methods like SMOTE (Synthetic Minority Over-sampling Technique) being utilized to improve model per- formance in scenarios where certain categories are underrepresented. 3 Methodology The basic methodology of this project involves sev- eral key steps aimed at analyzing survey papers using both qualitative and quantitative techniques. •Load the Dataset: The data is loaded from a CSV file into a pandas DataFrame for analysis. The code here uses the pandas library to read a CSV file, allowing easy manipulation and exploration of the data. •Data Collection and Preparation: The dataset comprises 144 records, each representing a survey paper. Key attributes include taxonomy (classification categories), titles, authors, re- lease dates, paper IDs, and links. The dataset is pre-processed to handle missing values, re- move duplicates, and ensure consistency. •Exploratory Data Analysis (EDA): Initial anal- ysis of the dataset involves exploring the dis- tribution of survey papers across different taxonomies, trends in publication over time, and descriptive statistics. Methods such as value_counts() and visualizations (e.g., bar charts, line plots) are employed to understand the underlying patterns and relationships in the data. •Classification and Model Training: Machine learning models, such as the Random Forest classifier, are used to analyze the data and clas- sify survey papers based on certain features. The dataset is split into training and testing sets, and models are evaluated using metrics like accuracy and F1 score. Stratified cross- validation ensures robust model evaluation. •Handling Class Imbalance: Since the dataset exhibits class imbalance (with fewer papers in certain categories), techniques like strati- fied cross-validation and weighted F1 scoring are applied to handle this issue and improve model performance. •Visualization: Various visualizations, includ- ing publication trends and taxonomy distribu- tion, are generated to communicate findings. Graphs provide an intuitive representation of the number of papers published over time and their classification into different taxonomies. 3.1 Data Exploration 2021-07 2022-08 2022-12 2023-02 2023-03 2023-04 2023-05 2023-06 2023-07 2023-08 2023-09 2023-10 2023-11 2023-12 2024-01 Month0.02.55.07.510.012.515.017.520.0Number of Surveys Survey Trends Over Time Figure 1: The trends of survey papers over months The graph "Survey Trends Over Time" shows the number of surveys conducted over various months, spanning from mid-2021 (July 2021) to early 2024 (January 2024). Here’s the key interpretation: •X-Axis (Month): Represents the timeline, starting from July 2021 to January 2024. •Y-Axis (Number of Surveys): Displays the count of surveys, with values ranging from 0 to 20. • Trend: There is an observable increase in the number of surveys over time. The number starts low around mid-2021 and consistently rises, peaking close to20 surveys around early 2024. The overall trend shows growth, suggesting that the frequency or quantity of surveys has been steadily increasing over this time period. This graph likely indicates a growing interest or requirement in conducting surveys as time progresses. Trustworthy ComprehensivePromptingScience RecSys & IR Multi-modal & Pre-trainingEvaluation Software EngineeringAdaptation TuningRoboticsGraphs OthersLaw Finance Education Hardware Architecture T axonomy0510152025Number of Survey PapersDistribution of Proposed T axonomy Figure 2: The distribution of the proposed taxonomy "Distribution of Proposed Taxonomy" provides insights into the focus areas of survey papers across various research domains. From the graph, several conclusions can be drawn: •Dominant Research Areas: Fields like "Multi- modal Pre-training" and "Trustworthy" have the highest representation, indicating that these areas are receiving significant attention in the research community. These may be trending or critical areas in the current re- search landscape. •Moderate Focus: Categories such as "Compre- hensive," "Prompting," and "Evaluation" also have a substantial number of survey papers, suggesting that these fields maintain steady research interest and are well-explored. •Niche or Emerging Areas: Topics like "Robotics," "Graphs," "Law," and "Finance" are less represented, with fewer than 10 pa- pers. This could imply either that these fields are niche or that research in these areas is still emerging and may see future growth. •Broad Distribution: The taxonomy covers a wide range of domains, reflecting the diver- sity of topics in contemporary research. Some areas may still be underexplored, offering po- tential opportunities for future surveys and investigations. Overall, the distribution highlights the current re- search priorities and gaps in the literature, with cer- tain fields seeing more in-depth exploration while others remain less covered. Jan 2022Jan 2023Jan 2024Jul Jul Jul Month01234567Number of Survey PapersMonthly Distribution of T axonomy Categories T axonomy Adaptation Tuning Comprehensive Education Evaluation Finance Graphs Hardware Architecture Law Multi-modal & Pre-training Others Prompting RecSys & IR Robotics Science Software Engineering Trustworthy Figure 3: Time series of taxonomy distribution The number of survey papers published across various research domains on a monthly basis from January 2022 to January 2024. •Seasonal Fluctuations: There are notable fluc- tuations in the number of survey papers pub- lished in different months, with some peaks and troughs across the years. •Steady Growth: A general increase in survey paper publication can be observed as time pro- gresses, with certain categories consistently seeing activity throughout the period. •Highly Represented Categories: "Multi- modal Pre-training," "Trustworthy," and "Prompting" seem to have higher activity, with these categories contributing signifi- cantly to the peaks in certain months. •Less Active Categories: Fields like "Finance," "Law," and "Graphs" are less represented in most months, indicating lower survey output in these areas. •Weak Correlation: The correlation of -0.033 implies that changes in the number of authors are not strongly related to the dates of the sur- veys or research. A value close to 0 generally suggests little to no linear relationship. •Red indicates positive correlations (values near +1), as seen on the diagonal where vari- ables are correlated with themselves. •Blue indicates negative correlations (values near -1). In this case, since the correlation between num_authors and date is close to zero, the blue hue is not very intense. Figure 4: Correlation Matrix In summary, this matrix shows that there is no meaningful correlation between the num_authors and date in the dataset. Both variables are mostly independent of each other in terms of linear rela- tionship. Figure 5: Top 10 authors contribution In above graph the top 10 authors based on their number of published papers. Philip S. Yu has the highest number of papers, followed by Enhong Chen and Caiming Xiong. The remaining authors have a relatively similar number of papers pub- lished. 3.2 Data Manipulation The data manipulation phase involves transforming and preparing the dataset for effective analysis and model development. 3.2.1 Creating the Feature Matrix •Text Transformation: Initially, text data from the titles andsummaries of the papers is combined. Using TfidfVectorizer from sklearn , this text is transformed into a TF- IDF (Term Frequency-Inverse Document Fre- quency) representation. This sparse matrix represents each paper as a row and each unique term as a column, capturing the signif- icance of words across the dataset. •Category Encoding: TheCategories field, which contains multiple labels for each paper, is processed using MultiLabelBinarizer to create one-hot encoded categorical labels. •Feature Combination: The TF-IDF matrix is combined with the one-hot encoded cate- gories using hstack , producing a comprehen- sive feature matrix that integrates both textual and categorical data. 3.2.2 Preprocessing the Feature Matrix •Normalization: Normalization is applied to ensure that all features contribute equally, which is especially important for machine learning algorithms relying on distance met- rics. •Label Encoding: The labels for each paper are encoded in a binary format, allowing the model to effectively learn relationships be- tween features and labels. •Data Splitting: The dataset is split into training (60%) and testing (40%) sets using train_test_split , ensuring proper evalua- tion of model performance on unseen data. In conclusion, these steps organize the raw dataset into a structured format, combining textual features with one-hot encoded categorical labels, applying normalization, and creating distinct train- ing and testing sets to facilitate effective model training and evaluation. 3.3 Data Evaluation The Random Forest Classifier yielded an accuracy of 39.66%, but the model struggled to predict many classes, particularly due to class imbalance in the dataset. Several categories had zero precision, re- call, and F1-scores, indicating that the model was biased towards the more frequent classes, while un- derrepresented classes were poorly predicted. To address this issue, techniques such as oversampling (e.g., SMOTE), class weighting, and advanced re- sampling methods could be employed to balance the dataset and improve the model’s performance, especially in predicting minority classes. Evalua- tion using metrics like F1-score would provide amore reliable assessment in such imbalanced sce- narios. Figure 6: Cross Validation Accuracy score In(fig.6) this approach, we use stratified cross- validation to evaluate the performance of the model across five folds, which helps ensure that the class distribution is maintained in each fold. The accu- racy obtained from this cross-validation is 38.37%, and the weighted F1 score is 0.24. The low F1 score, in particular, highlights the challenge of han- dling class imbalance. A warning was raised indi- cating that the least populated class has only one member, which affects the performance and relia- bility of cross-validation. Visualizing the accuracy scores across folds gives insight into the model’s stability and consistency in performance across dif- ferent splits of the data. 4 Conclusion The project successfully analyzed survey papers across different taxonomies, providing insights into publication trends and classification patterns. Using machine learning techniques, the model achieved moderate performance, highlighting chal- lenges with class imbalance. The visualizations and analysis contribute to understanding the land- scape of survey papers and their categorization. A APPENDIX A.1 Dataset Description The dataset used in this project includes infor- mation on survey papers, categorized by taxon- omy, with attributes such as title, authors, release date, links, and paper ID. The dataset contains 144 records with the following columns: •Taxonomy : Categories assigned to each sur- vey paper. •Title : The title of the survey paper. •Authors : Authors of the survey paper. •Release Date : Date when the survey paper was released. •Links : URLs to the paper. •Paper ID : Unique identifier for each paper. •Categories : Research areas for each survey. •Summary : Short summary of the paper’s con- tent. A.2 Methodology The project followed a structured methodology to analyze and visualize survey paper data: •Data Preprocessing: The dataset was loaded, and date formats were adjusted for temporal analysis. Missing values were handled appro- priately. •Visualization: Publication trends and taxon- omy distributions were visualized using bar charts and line graphs to explore patterns over time. •Machine Learning: Random Forest was ap- plied to classify the papers into their respec- tive taxonomy categories. Stratified cross- validation was employed to evaluate model performance, addressing class imbalance is- sues. •Evaluation: The model’s performance was evaluated using accuracy and F1 scores, which were visualized across folds in cross- validation. A.3 Figures Figure 1: Survey Trends Over Time: This figure displays the number of survey papers published each month, illustrating the trends in survey publi- cations over time. Figure 2: Distribution of Proposed Taxonomy: This bar chart shows the distribution of survey pa- pers across different taxonomy categories, high- lighting areas with more research focus. Figure 3: Time series of Taxonomy Distribution: The monthly publication count of survey papers across different research domains from January 2022 to January 2024.Figure 4: Correlation Matrix: This matrix indi- cates that there is no significant correlation between the number of authors and the publication date in the dataset. Figure 5: Top 10 authors contribution This bar chart shows the top 10 authors based on their num- ber of published papers. Figure 6: Cross validation Accuracy score. In this approach, we use stratified cross-validation to evaluate the performance of the model across five folds A.4 Code Relevant code snippets for data loading, visualiza- tion, and machine learning analysis were imple- mented using Python and the scikit-learn library.