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Fine Grained Sentiment Classification

Fine Grained Sentiment Classification is a Python research repository that compares rule-based, linear and neural NLP classifiers on the Stanford Sentiment Treebank’s five-class sentiment task.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Fine Grained Sentiment Classification — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a code reference or experimental starting point for comparing five-class sentiment methods on SST-5, rather than a hosted text-analysis application.

Who it is for

  • NLP learners comparing sentiment-classification methods on a common dataset.
  • Researchers or developers exploring rule-based, TF-IDF, embedding and transformer approaches to five-class text classification.

Documented uses

  • Compare predictions from multiple classifier families on SST-5.
  • Train a FastText model with automatic hyperparameter tuning.
  • Fine-tune Flair or a causal transformer for the dataset’s classification task.
Before you choose

Requirements & limits

The repository is public and lists an MIT license.

  • The project centers on SST-5 and five sentiment classes.
  • The model options differ substantially: TextBlob and VADER use rule-based scores, logistic regression and SVM use TF-IDF features, and the neural options include FastText, stacked-embedding Flair and a causal transformer
  • The README describes substantial training time for Flair, including more than a day for 50–100 epochs on a single GPU.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the Python environment

Create and activate the repository’s virtual environment and install its listed requirements.

Before you start

  • Python 3 and pip3
  • The repository checkout with requirements.txt

Steps

  1. Create a virtual environment with the command below.

    Documented command
    python3 -m venv venv

  2. Activate it with source venv/bin/activate.

  3. Install dependencies with the command below.

    Documented command
    pip3 install -r requirements.txt

Expected outcome

The virtual environment is activated with the repository requirements installed.

Guide limitations

  • This setup sequence does not cover running a classifier or training a model.
Documentation 1 ↗
Source-based practical guide

Tune a FastText classifier

Run the documented FastText automatic-tuning command with training and validation data.

Before you start

  • The repository Python environment and FastText v0.9.2 or later, as cited for Python API tuning
  • Training and validation files in the SST data directory

Steps

  1. Change to the training directory.

  2. Run the README’s train_fasttext.py example with --filepath, --train, --valid, --duration 1800 and --modelsize 10M arguments.

  3. Set the --model argument to the desired model path.

Expected outcome

The documented process writes a tuned FastText model with the `.ftz` extension to the specified model path.

Guide limitations

  • The example uses a 1,800-second tuning duration and a 10 MB model-size cap; the README says the duration can be changed.
  • The training and validation file paths must match the local data layout.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
prrao87/fine-grained-sentiment · Official homepage title · retained in its source language
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