Short Text Analysis is a Java-based pipeline for processing and classifying short text content (for example, tweets). The project combines text preprocessing, feature extraction, dataset preparation, and machine-learning classification to support quick experimentation on short-message datasets.

At a high level, the application helps you:

• Clean and normalize raw short-text inputs
• Compute features used for model training/inference
• Convert processed data into ML-friendly ARFF format
• Train/evaluate classification models (Weka-based)
• Integrate data from files and SQL-backed sources where needed

The repository implements an end-to-end short-text classification workflow:

1. Input ingestion from local files and/or database-connected sources
2. Text preprocessing (token cleanup, normalization, and preparation)
3. Feature extraction from cleaned text records
4. ARFF dataset generation for Weka consumption
5. Model execution and evaluation through Weka-related classes
6. Output artifacts such as prepared datasets, model results, and evaluation summaries

This structure is useful for experimentation, baseline model comparisons, and extending short-text analytics pipelines.

If you are new to the codebase, read files in this order:

1. Main

   • Entry point/orchestrator for pipeline execution.
   • Wires together preprocessing, feature extraction, data conversion, and model operations.

2. TweetProcess

   • Core text-processing stage.
   • Responsible for preparing raw tweet/short-text content for downstream feature computation.

3. FeatureExtract

   • Computes the engineered features used by classifiers.
   • Defines what information from each short text becomes model input.

4. MakeARFF

   • Converts processed examples + features into ARFF format.
   • Acts as the bridge from internal data structures to Weka-compatible datasets.

5. Weka-related classes

   • Train, evaluate, and potentially persist/compare models.
   • Encapsulate classifier choice, metrics, and evaluation flow.

6. Data integration helpers (ExcelToDB, SQL/JDBC classes)

   • Import/export and storage integration points.
   • Useful when data preparation and experiments involve database-backed workflows.

When making changes, start at Main to see control flow, then inspect each layer implementation.

The project is organized in a practical layered style:

• Orchestration layer: Main

  • Controls execution order and high-level workflow.

• Processing layer: TweetProcess

  • Handles short-text cleanup and normalization logic.

• Feature layer: FeatureExtract

  • Produces model-relevant numeric/categorical signals.

• Data preparation layer: MakeARFF

  • Builds Weka-ready ARFF datasets.

• Model layer: Weka classes

  • Runs training/inference and computes evaluation metrics.

• Integration layer: ExcelToDB, SQL helpers

  • Connects external data sources/sinks to the analysis pipeline.

Raw short text -> Preprocessing -> Feature extraction -> ARFF conversion -> Weka classification/evaluation -> Results

• Java (core language/runtime)
• Weka APIs for machine-learning models and evaluation
• JDBC/SQL APIs for database integration
• File I/O for dataset ingestion and artifact output

• JDK 8+ (or the version used by your team)
• Build tooling available in this repo context (IDE/manual javac flow)
• Weka library/JAR configured in classpath
• Access to any required input files and (if used) SQL database credentials

1. Clone the repository.
2. Import/open as a Java project in your IDE.
3. Ensure Weka and JDBC dependencies are resolvable in classpath.
4. Confirm input data locations and database configuration (if applicable).
5. Run the Main class.
6. Inspect generated ARFF files, model outputs, and evaluation results.

• Short-text/tweet records (file or DB sourced)
• Optional metadata/labels needed for supervised training

• Processed text representations
• Feature-enriched datasets
• ARFF files
• Classification/evaluation summaries

• Trace one complete run through Main before changing logic.
• Make small, layer-scoped changes (processing vs feature vs model).
• Validate intermediate artifacts (especially ARFF structure) after edits.
• Update this README when behavior, architecture, or setup steps change.

To keep project documentation useful, every PR should preserve and update this README when needed.

• Project overview and purpose
• Execution/getting-started instructions
• Code walkthrough of key classes
• Technical architecture and data flow
• Dependencies/integration requirements
• Inputs/outputs and expected artifacts

Before requesting review, confirm:

• Behavior changes are reflected in Project Overview or What the Application Does
• New/modified classes are reflected in Code Walkthrough
• Architecture/data flow updates are reflected in Technical Overview
• Setup/runtime dependency changes are reflected in Getting Started
• New required configs, inputs, or outputs are documented

If a code change affects how the system runs or is understood, README updates are required in the same PR.