metadata

title: Sentiment Analysis
emoji: 🤗
colorFrom: blue
colorTo: green
pinned: false
sdk: gradio
python_version: 3.11
app_file: app/gui.py
datasets:
  - mrshu/amazonreviews
  - stanfordnlp/sentiment140
  - stanfordnlp/imdb
  - Sp1786/multiclass-sentiment-analysis-dataset
models:
  - spacy/en_core_web_sm

Sentiment Analysis

Description
Installation
- Prerequisites
Usage
- Predict
- GUI
- Training
- Evaluation
Options
Implementation
- Architecture
- Pre-trained Models
License

Description

This is a simple sentiment analysis model written in Python, designed to predict whether the provided text has a positive or negative sentiment. The project comes with both a graphical user interface and a command-line interface. While training the model, the user can choose from a couple of datasets to train the model on and then evaluate the trained model on another dataset. Once the model is trained, it can be used to predict the sentiment of any text with the help of the GUI or CLI.

Installation

Clone the repository and once inside the directory, run the following command to install the dependencies:

python -m pip install -r requirements.txt

Ensure that you have at least one dataset downloaded and placed in the data directory before running train. For evaluate, you will need the test dataset. See Datasets for more information.

The project comes with pre-trained models that can be used for prediction. See Pre-trained Models for more information.

Prerequisites

Python 3.11+

Usage

To see the available commands and options, run:

python -m app --help

Predict

To perform sentiment analysis on a given text, run the following command:

python -m app predict --model <model> I love this movie

where <model> is the path to the trained model.

Alternatively, you can pipe the text into the command:

echo "I love this movie" | python -m app predict --model <model>

GUI

To launch the GUI, run the following command:

python -m app gui --model <model>

where <model> is the path to the trained model. Add the --share flag to create a publicly accessible link.

After running the command, open the link from the terminal in your browser to access the GUI.

Training

Before training the model, ensure that the specified dataset is downloaded and can be accessed at its respective path. To train the model, run the following command:

python -m app train --dataset <dataset> {options}

where <dataset> is the name of the dataset to train the model on. For available datasets, see Datasets.

The trained model will be exported to the models directory.

To see all available options, run:

python -m app train --help

Evaluation

Once the model is trained, you can evaluate it on a different dataset by running the following command:

python -m app evaluate --model <model>

where <model> is the path to the trained model. For available datasets, see Datasets.

To see all available options, run:

python -m app evaluate --help

Options

Datasets

Option	Path	Notes	Dataset
sentiment140	`data/sentiment140.csv`		Twitter Sentiment Analysis
amazonreviews	`data/amazonreviews.bz2`	only train is used	Amazon Product Reviews
imdb50k	`data/imdb50k.csv`		IMDB Movie Reviews
test	`data/test.csv`	required for `evaluate`	Multiclass Sentiment Analysis

Vectorizers

Option	Description	When to Use
`count`	Count Vectorizer	When the frequency of words is important
`tfidf`	TF-IDF Vectorizer	When the importance of words is important
`hashing`	Hashing Vectorizer	When memory is a concern

Environment Variables

The following environment variables can be set to customize the behavior of the application:

Name	Description	Default
`MODEL_DIR`	the directory where the trained models are stored	`models`
`DATA_DIR`	the directory where the datasets are stored	`data`
`CACHE_DIR`	the directory where cached files are stored	`.cache`

Implementation

Architecture

The input text is first preprocessed and tokenized using re and spaCy where:

The text is cleaned up by removing any HTML tags and converting emojis to text
Stop words and punctuation are removed
URLs, email addresses and numbers are removed
Words are converted to lowercase
Lemmatization is performed (words are converted to their base form based on the surrounding context)

After tokenization, feature extraction is performed on the tokens using the chosen vectorizer. Each vectorizer has its own advantages and disadvantages, and the choice of vectorizer can affect the speed and accuracy of the model (see Vectorizers). The extracted features are then passed to the classifier which predicts the class which in this case is the sentiment of the text. Both the vectorizer and classifier are trained on the specified dataset.

%%{ init : { "flowchart" : { "curve" : "monotoneX" }}}%%
graph LR
  START:::hidden --> |text|Preprocessing

  subgraph Preprocessing
    direction TB
    A[Tokenizer]
    B1[HashingVectorizer]
    B2[CountVectorizer]
    B3[TfidfVectorizer]

    A --> B1
    A --> |tokens|B2
    A --> B3

    B1 --> C1:::hidden
    B2 --> C2:::hidden
    B3 --> C3:::hidden
  end

  Preprocessing --> |features|Classification

  subgraph Classification
    direction LR
    D1[LogisticRegression]
    D2[LinearSVC]
  end

  Classification --> |sentiment|END:::hidden

  classDef hidden display: none;

Pre-trained Models

The following pre-trained models are available for use:

Dataset	Vectorizer	Features	Classifier	Accuracy	Model
`sentiment140`	`tfidf`	`LinearRegression`	20 000	?	Here
`imdb50k`	`tfidf`	`LinearRegression`	20 000	?	Here
`imdb50k`	`tfidf`	`LinearRegression`	800	?	Here
`imdb50k`	`hashing`	`LinearRegression`	1 048 576	55.65% ± 1.07%	Here

The accuracy of the models is based on the cross-validation score using the test dataset and 5 folds.

Note

Due to the size of the amazonreviews dataset, it was not possible to train a model with a vectorizer other than hashing.

License

Distributed under the MIT License. See LICENSE for more information.

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